Abstract
Traditional design, manufacturing and maintenance are run and managed independently under their own rules and regulations in an increasingly time-and-cost ineffective manner. A unified platform for efficient and intelligent design-manufacturing-maintenance of mechanical equipment and systems is highly needed in this rapidly digitized world. In this work, the definition of digital twin and its research progress and associated challenges in the design, manufacturing and maintenance of engineering components and equipment were thoroughly reviewed. It is indicated that digital twin concept and associated technology provide a feasible solution for the integration of design-manufacturing-maintenance as it has behaved in the entire lifecycle of products. For this aim, a framework for information-physical combination, in which a more accurate design, a defect-free manufacturing, a more intelligent maintenance, and a more advanced sensing technology, is prospected.
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1 Introduction
Traditionally, the design and manufacturing of equipment are run and managed independently, and relatively disconnected with each other, resulting in low reusability of design information, and manufacturing and maintenance data cannot effectively support the optimized design of equipment. This disconnection results in virtual mapping, cyclic iteration and integrated development of equipment design, manufacturing and maintenance not being achieved, and consequently, companies will have the problems with long development cycles and high maintenance costs. On the other hand, traditional maintenance scenarios include safety assessment [1], life prediction [2–4], structural integrity monitoring [5, 6], and online monitoring [7, 8]. These traditional measures are mainly based on physical mechanisms and laws, and is highly relied on simplified models. The integration of design-manufacturing-maintenance of engineering equipment is a huge challenge due to the difficulty of creating accurate physical models for the design of complex equipment.
In engineering practice, the intelligent operation and maintenance needs to consider the monitoring of operation [9], warning of sudden failure [10], timely start and pause of control systems [11], feedback of maintenance status [12] and the visualization of maintenance [13]. With the development of virtual technologies such as the Internet, VR and AR, the design-manufacturing-maintenance of mechanical equipment has gradually diversified [14–16]. It is urgent to explore and improve sustainable and intelligent design-manufacturing-maintenance integration technology for mechanical equipment and systems, and create a fast, efficient and visualized industrial equipment intelligent design-manufacturing-maintenance platform [17–20].
The emerging digital twin concept may offer a possible solution [21–25]. The digital twin can provide real-time feedback from the virtual world to engineers [26–29], who can combine information from the virtual world with information from expert databases to make maintenance decisions, which will significantly reduce time and costs. The combination of the digital twin with the design-manufacture-maintenance of equipment can help to make more efficient, cost-effective and convenient steps in new product development and in-service equipment management.
There are three cores of digital twin in intelligent design-manufacturing-maintenance, as shown in Figure 1, mainly including the design of digital models, the construction of physical models and the techniques for fusing digital and physical models, which should be compatible with each other. The construction of a physical model includes program design, program execution and data feedback. Design-manufacturing-maintenance involves cross-industry, cross-platform, human-machine interaction and collaboration, and machine-machine interaction and collaboration throughout the product lifecycle. As such, digital twin technology plays a key role in driving the development of industrial clustering.
The three cores of digital twin-driven intelligent operation and maintenance
On the other hand, the convergence of human-cyber-physical systems is the core factor of digital twin technology applied to the intelligent operation and maintenance of industrial equipment [30, 31]. Currently, there are few studies on the application of cyber-physical systems to the intelligent design-manufacturing-maintenance of industrial equipment. To address the fundamental issue of sensor-model-diagnostic high-fidelity system fusion, a scientific basis for edge intelligence-based lifecycle management is required.
This paper is an overview of the integration of digital twin with equipment design-manufacturing-maintenance, by highlighting the challenges including design solution determination, digital scene construction and the fusion of the physical world with the virtual model, and by paying attention to the progress and prospects for digital twin application into the real world.
2 Digital Twin: Definition
Though the digital twin is attracting more and more attention, its definition has evolved for a long time. The concept of digital twin is retroactive to 2003, when Professor Grieves of the University of Michigan introduced the concept in a course on total product lifecycle management. The four stages of digital twin development are shown in Figure 2. The digital twin was defined as physical products, virtual products and the convergence between the two [32, 33]. Glaessgen et al. [34] raised digital twin as an integrated multi-physical, multi-scale, high-fidelity twin system through physical models, sensor upgrades, and historical data, as shown in Figure 3. Abdulmotaleb et al. [35] proposed digital twin as a virtual copy of an organism or non-organism physical entity, while allowing information interoperability between the physical and virtual entities. Söderberg et al. [36] considered digital twin as an optimization of digital copies of physical entities. Bolton et al. [37] thought of digital twins as virtual representations of physical objects across life cycle that can be understood, learned and reasoned with real-time data. Negri et al. [38, 39] proposed the concept of model engineering and related techniques, and a set of measurement criteria for a correct digital twin, i.e., a digital twin is defined as a simulation model that acquires data from the field and triggers the operation of physical devices. The digital twin is a virtual simulation that integrates multiple data, dimensions, attributes and application possibilities in the context of new-generation information technology, industrial Internet technology and smart manufacturing concepts [40–42]. It is believed that, digital twin is a virtual entity that creates physical entities digitally, making full use of data such as physical models, sensor updates and operational histories to integrate multi-disciplinary, multi-physical quantity, multi-scale and multi-probability simulation processes and complete mapping in virtual space, thus reflecting the full life-cycle processes of the corresponding physical equipment for simulation, monitoring, evaluation, prediction, optimization, control and other fields.
Four stages of development for digital twin technology
Representative examples of digital twin-driven technologies in industry [34]
One of the features of digital twin is to create virtual models of physical objects digitally to simulate the behavior of physical objects [32]. Virtual models can understand the state of physical entities by sensing data to predict, evaluate and analyze the dynamic changes of simulated objects. In turn, the physical object will respond according to the optimized solution in the simulation. By closing the cyber-physical loop [43], the digital twin can achieve optimization of the entire production process. This interactivity between the real and digital worlds of a product or process provides a rich set of models and data for manufacturing process analysis and optimization, enabling more realistic and comprehensive measurements of system unpredictability [44].
The big data technology is often involved in digital twin for accurate calculation, as it can effectively mine the hidden and effective data, which greatly improves the intelligence of digital twin [45] and the applicability. Digital twin as a new technology has been applied in various fields, such as aerospace [46–50], industry manufacturing [51–53], 3D printing [54, 55], medical services [56–59], bio-manufacturing [60], agricultural machine [61], robots [62–64], power industry [65, 66], smart city [67], pressure vessel [68], machining [69–72], automatic transportation [73], roads infrastructure [74], construction system [75], as listed in Table 1.
3 Digital Twin for Design
Digital twin has gained widespread attention in the aerospace field [76, 77, 120]. For example, the U.S. Air Force Laboratory proposed a study to predict structural life and ensure structural integrity of aircraft using digital twin technology in 2011. It seems the digital twin is a new paradigm in the age of information and data revolution. The digital twin can combine physical equipment data, virtual equipment data, and connecting data of physical and virtual equipment to support the design, manufacturing and maintenance of equipment.
Digital twin is a multidisciplinary crossover technology that allows designers in plant for quick evaluation and identification of design flaws [106]. A hybrid design approach of data science and physical mechanisms is the main avenue for future research on large high-end devices. Zhuang et al. [108] proposed an implementation framework for designing and applying shop-floor digital twin. The digital twin design adopts advanced sensing equipment, industrial internet, big data, cloud computing and other science and technology. It has the characteristics of high fidelity, cross-dimensional integration, and high reliability. It can realize functions such as status detection, life prediction, and data tracking [34, 121]. It follows that the digital twin is a holistic system model of objects and environments with multiple software, relying on data transmission from sensors to address the operational conditions of physical entities in the virtual world, and the feedback from the virtual world to design and improve the operational quality and efficiency of the physical world and enhance economic efficiency.
In 2016, the U.S. Air Force developed and applied a digital twin analysis framework to provide engineering analysis capabilities and decision support for the entire lifecycle of aeronautical system. The digital twin mergers physics-based modeling and experimental data to generate an authoritative digital representation of the system at each stage of the weapon system acquisition and sustainment process. As a result, the digital twin can save the development time and cost of the aeronautical system design [82], as shown in Figure 4. In 2017, Tao et al. [104] proposed the concept of the digital twin workshop, explaining in detail the main components and operational mechanism of digital twin workshop. In 2018, Bohlin et al. [95] presented a digital twin-based active part matching and self-adjustment device to improve geometric quality without tightening incoming part tolerances, which provided the framework for implementing digital twin-driven Smart Assembly 4.0. Xu et al. [122] designed a digital cam servo motion system based on digital twin. Utilizing multi-dimensional simulation software, the trajectory planning, state monitoring and precise control of the electronic cam motion were realized by employing the virtual-real interaction capability of the digital twin technology. Guo et al. [106] proposed a modular approach to help build flexible digital twins and make changes accordingly. With this flexible digital twin, designers can quickly evaluate different designs and easily identify design defects, thus time-saving, as shown in Figure 5.
Digital twin approach to saving design time and cost [82]
Framework for digital twin application in plant design [106]
For intelligent design, digital twin needs to be combined with next generation of information technology, such as machine learning, deep learning, cloud computing and big data [123]. Machine learning can be the result of a digital twin simulation with simple learning capabilities. Cloud computing can provide digital twin with multi-dimensional data computing technology and cloud data storage technology. Integrating cloud technology in digital twin can effectively reduce the computation time of complex systems and overcome the difficulties of storing large amounts of data [124], as shown in Figure 6. In this regard, manufacturing companies such as General Electric, Siemens and Tesla have already started to create application scenarios that enrich the digital twin through next-generation information technology [102].
Cloud-integrated cyber physical systems for complex industrial applications [124]
In addition to the application of digital twin technology in industry, digital twin technology is also being explored in agriculture and animal husbandry to promote smart agriculture [116] and smart animal husbandry [125, 126]. Therefore, a real-time, bi-directional, transparent and systematic consideration of design-manufacture-performance is only possible by using full digital twin technology to build a large number of surreal models and data, including digital product models, digital design models, digital manufacturing models, and digital performance models [127].
4 Digital Twin for Manufacturing
4.1 Evolution of Manufacturing Mode
Since 1970s, the manufacturing industry has been changing from product manufacturing to service-oriented manufacturing. Traditional manufacturing industry includes three stages for development, i.e., labor-intensive mainly in light textile industry, technology and capital-intensive mainly in electromechanical and high-tech industries, and knowledge, technology, capital and service intensive towards equipment manufacturing. Recently, intelligent manufacturing has gradually become dominant with the introduction of big data, artificial intelligence and digital twin concept [128]. As a result, intelligent manufacturing systems such as electronic manufacturing, digital manufacturing and virtual manufacturing, have become the new paradigm for improving manufacturing operations in manufacturing environments [129]. Traditional manufacturing is an online process where product designs and drawings are forwarded to the shop floor for manufacturing prototypes. Digital technology, on the other hand, is a cyclical process of conceptual design and innovation of products in computer-aided design software. These designs and processes are simulated to verify the feasibility of product production. Products are inspected at each stage of the manufacturing process using inspection techniques and computer-aided quality control methods.
The rising of cyber-physical system and digital twin simulation is also updating the manufacturing industries. For example, Negri et al. [92] proposed a production system separated from the core simulation to allow flexibility in deciding whether to activate copies of specific behaviors only when needed. Similarly, the petrochemical industry will also undergo intelligent transformation and upgrading. Min et al. [115] proposed a framework and method for building a digital twin model based on the petrochemical industry IoT, machine learning, and a practice cycle of information exchange between physical plant and virtual digital twin model for control optimization, as shown in Figure 7.
Digital twin-based petrochemical production control architecture [115]
Nowadays, more and more countries are investing in manufacturing capacity to increase productivity to offset costs, and manufacturers are being forced to develop advanced manufacturing technologies to achieve greater capacity with faster and more complex mechanical systems [130]. Hu et al. [131] proposed a scheme including automatic creation of mechanical, electrical, and plumbing systems that included modules for logic chains, equipment grouping, labeling schemes, and conversion of building information model information into geographic information system maps, as shown in Figure 8.
Schematic diagram of integrated delivery of BIM-based mechatronics design, operation and maintenance [131]
In addition, artificial intelligence technology has impact on enterprises, and enterprise IT operation and maintenance management is also moving towards intelligent and advanced pace [132]. Liu et al. [46] proposed a technical framework for intelligent solution design, simulation verification and in-orbit analysis using digital twin and digital ties. Wang et al. [133] proposed a digital twin-based aero-engine low-pressure turbine unit body docking technology by modeling the physical objects in the environment and process, and using multiple sensors to achieve physical fusion, model fusion, and data fusion based on a 3D virtual docking simulation process. Zhang et al. [134] constructed of a spacecraft digital twin model to represent the process, state, and behavior of the spacecraft as it completed its in-orbit assembly. The emerging technologies such as cloud computing, IoT, are expected to combine information technology with manufacturing.
4.2 Digital Manufacturing Based on Digital Twin
The aim of data-driven manufacturing is to transform data acquired throughout the product lifecycle into manufacturing intelligence in order to provide data solutions for the development of manufacturing [135]. In data-driven manufacturing, the data generated by manufacturing systems is experiencing explosive growth and has reached over 1000 EB per year [136]. Systematic computational analysis of manufacturing data will lead to more informed design solutions, which in turn will increase the effectiveness of data-driven manufacturing [137]. In other words, data-driven manufacturing can be seen as a necessary condition for smart manufacturing. As a result, data is becoming a key enabler of manufacturing competitiveness [138] and manufacturers are beginning to recognize the strategic importance of data.
Data-driven value does not depend only on the amount of data under consideration, but on the information and knowledge hidden within. New information technologies such as IoT, cloud computing, mobile internet and AI can be strategically leveraged and effectively integrated to support data-driven manufacturing [139]. A large body of research has emerged in recent years examining data-driven manufacturing, including industrial automation [140]. Galletti et al. [141] studied big data as a driver of industrial competitiveness. Dubey et al. [142] illustrated the unique role of big data analytics in sustainable manufacturing. Zhang et al. [143] proposed big data analytics architecture for clean manufacturing and maintenance processes. Lohtander et al. [110] constructed digital twins of micro-manufacturing cells by using production components and simulation software, which allowed the immediate integration of the machine into the industrial environment and allowed the subsequent control of all parameters of the production system.
The digital twin provides a state-of-the-art technical framework for smart manufacturing in the manufacturing industry [144], and has a supporting role in securing the industrial internet and the smart manufacturing 2025 strategy with cyber physical systems at its core as well as innovative value [91, 130, 145, 146]. Zhuang et al. [147] raised that digital twin helped to solve the problem of effective convergence and management of heterogeneous dynamic data from multiple sources in the whole product lifecycle, and proposed a digital twin implementation framework for product manufacturing.
In summary, digitally driven manufacturing technologies enable manufacturers to manage real-time, bi-directional and co-evolving mappings between physical objects and their digital representations, which paves the way for deep cyber-physical integration. Combined with the digital twin, data-driven smart manufacturing will become more responsive, adaptive and predictive.
5 Digital Twin for Maintenance
5.1 The Significance
Maintenance has evolved from “maintenance after the fact” and “preventive maintenance” to “predictive maintenance”. Precision maintenance is the way of the future, with the aim of ensuring operational safety and reducing synergistic optimization targets and operational costs [127]. The digital-twin-based maintenance include five key technologies: data collection technology, data modelling technology, twin data application, artificial intelligence technology and human-machine interaction technology. The scientific principles of intelligent operation and maintenance of industrial equipment include sensing and damage perception, damage evolution and prediction models, and diagnosis and decision intelligence, as shown in Figure 9. The prediction of damage mainly involves the description of evolutionary laws and the development of predictive models, the coupling of multiparameter quantities and mechanisms, multi-scale and multi-level simulations and experimental reproduction. Lifetime intelligent maintenance includes the fusion of multi-physics big data, artificial intelligence and internet diagnostics, system diagnostics based on component-level data, intelligent diagnostics and diagnostic intelligence issues.
New framework of digital twin for intelligent operation and maintenance of large machinery equipment
The use of intelligent operation and maintenance technology in the automatic verification system of energy meters can improve efficiency and produce better economic and social benefits [20]. The application of intelligent technologies such as artificial intelligence, robotics, augmented reality, and online monitoring to traditional substations to achieve visibility of the station situation, risk penetration, power consumption, one-touch operation, and integrated command can significantly improve the operation and maintenance costs of traditional power stations [148–150]. Through high-capacity mobile communication network, high-speed wireless network and various mobile terminals, the operation and maintenance mode of relay protection and circuit breakers equipment are changing towards portability, efficiency and intelligence [151–156]. Therefore, digital twin is an important way to achieve equipment health status monitoring and provides a new paradigm for equipment fault diagnosis. For example, Gregor et al. [157] described the design ideas for an integrated reconfigurable maintenance system. Xu et al. [113] proposed a two-stage digital twin-assisted fault diagnosis method based on deep migration learning to achieve fault diagnosis in the operation phase and maintenance phase, as shown in Figure 10.
Schematic of digital twin-assisted fault diagnosis method based on deep migration learning [113]
5.2 The Applications
Intelligent operation and maintenance of industrial equipment is an important part of Industry 4.0 [158]. Intelligent operation and maintenance has been penetrated into various fields with the development of information technology, such as intelligent power station [159], intelligent network [160], engineering vehicles [161], green buildings group [162], micro-grid technology [163], communication network [164], nuclear power [118, 165–168], alleviation photovoltaic [169], bridge maintenance [170].
In machinery industry, the operation and maintenance of machine tools require regular inspection and adjustment by human beings. The diversification and complexity of industrial requirements make CNC machine tools more and more automated and networked. And remote monitoring and intelligent fault diagnosis system is the foundation and indispensable unit of machine tool automation and networking [99, 171]. Luo et al. [100, 172, 173] established a multi-domain unified modeling approach for digital twins, explored the mapping strategy between physical and digital spaces, and proposed a self-prediction and self-maintenance method for digital twins. If machine tools can be built with a digital twin in virtual space that can monitor the health status and operating history of machine tools at any time, unnecessary losses caused by unexpected events can be greatly avoided [101, 174–177]. Scaglioni et al. [98] developed a dynamic model of the Mandelli M5 machine tool. The dynamic behavior of the digital twin was verified by installing the required sensors on the machine.
In other industries, Martínez et al. [105] proposed a method for generating a simulation-based digital twin from an automatically generated first-principles model that can be used for operation and maintenance of the equipment life cycle, as shown in Figure 11. Liu et al. [178, 179] proposed an intelligent management platform for coal mine electromechanical equipment based on the IoT, effectively reduces the probability of equipment failure, improves the level of equipment refinement management, and realizes the whole life cycle management of equipment. Moussa et al. [114] proposed a digital twin model for large hydroelectric generators. To improve the accuracy and efficiency of prediction and health management of wind power, Tao et al. [112, 180–183] proposed a digital twin failure prediction model for complex equipment, which effectively utilized the interaction mechanism of digital twin and data fusion techniques. Chen et al. [184] proposed a future-oriented intelligent, semi-autonomous human-cyber-physical system fusion wind turbine under this new concept, as shown in Figure 12. Xiao et al. [185] developed an intelligent ultrasonic inspection system with defect tracking and defect simulation analysis for the characteristics of defects in the welded joints of hydraulic turbine runners. By embedding virtual 3D models, the use of AI technology can precisely combine virtual and reality. In addition, there is a need to develop predictive maintenance solutions for industrial equipment that combine digital twins with augmented reality [84], as shown in Figure 13.
Simulation-based digital twin and its application in the operation and maintenance of equipment life cycle [105]
A conceptual framework diagram of a human-machine information-physical system for wind turbines in the future Industry 5.0 era [184]
Digital twin-based electrical monitoring and information interaction [84]
To realize the intelligent operation and management of microgrid and intelligent micro network group [186], Fu et al. [163] established a microgrid fault diagnosis model based on advanced Petri net theory by studying the microgrid operation and maintenance knowledge base, and proposed an intelligent diagnosis and analysis method of microgrid faults. Park et al. [117] proposed an operational scheduling model of energy storage system applied to virtual space for building microgrids using digital twin technology. Mu et al. [165] leveraged virtual reality technology to establish a virtual environment for existing nuclear power plants, and an intelligent nuclear power immersion system was developed by integrating data monitoring and fault analysis, and providing operation and maintenance assurance, equipment management, fault analysis, operation and maintenance demonstration, and intelligent simulation training. Liu et al. [187] proposed a new concept of combining digital twin model with steel structure operation and maintenance safety by taking spoke type cable truss as an example. Liao et al. [188] developed a new data-driven approach to machine performance evaluation and prediction in order to reduce the problem of reaching the shortest possible downtime during machine maintenance.
In overview, in the area of digital twin-based maintenance, transforming maintenance decisions from reliance on downtime inspection to a combination of online testing and simulation. In particular, it is important to note that digital twin technology is not simply a simulation, but requires a focus on underlying technological innovation, including sensing technology, sensing networks, digital model building and so on.
6 The Challenges and Solutions
6.1 The Challenges
A large amount of data information will appear in the design process of mechanical equipment, but there are currently barriers to share this data information between peers, with inadequate use of data and inconsistent data standards. The needs of users are developing in the direction of diversification and personalization, which presents a huge challenge to the design of mechanical equipment.
To achieve high fidelity, high precision and visualization in the manufacture of mechanical equipment, the support of digital twin technology is urgently needed. The current use of digital twin technology for manufacturing is still mainly based on finite element simulation [189], thus requiring improvements in digital twin manufacturing technology to achieve high fidelity and high precision manufacturing of mechanical equipment.
The whole life cycle management of the product based on virtual model is often carried out to achieve real-time and intelligent operation and maintenance of equipment. As for complex structures of large equipment, real-time data from internal components that are closely related with each other are limited. In this case, the concept of digital twin can capture, locate and clarify faults, and assess the state of equipment for predictive maintenance. Digital twin-based maintenance requires comprehensive view of multi-physics structures and establishment of system-level modeling under multi-physics field. Nevertheless, the challenge is, to correct the key characteristic parameters, to develop accurate mechanistic and lifetime models of the equipment based on various verification tests.
6.2 Design-Manufacturing-Maintenance Integration
Although traditional design can be closed-loop, i.e., a top-down decomposition process aiming for a bottom-up structure, it is relatively static, does not indicate processing, manufacturing, testing, repairing and prediction of the physical product, and thus cannot reflect the dynamic state in different stages, and is unable to guide the full lifecycle management. Currently there exists the inability of feedback of real-time manufacturing testing and assembly, and end-user’s data to the product design process. In addition, the conventional design, manufacturing and maintenance are independently run and managed with various rules. Such kind of disconnection results in low availability of design information for reuse, ineffectiveness of manufacturing data to support optimal design, and poor guidance of maintenance data for design and manufacturing, and thus hinder optimization and integration of design, manufacturing and maintenance, and increase the time and cost for product development.
Digital twin technology provides a feasible solution for this integration as it spans the full lifecycle of product design-manufacturing-maintenance. The utilizing of digital twin helps unification of modeling standards and rules as geometric parameters, manufacturing and maintenance data are well represented. Only in a complete digital twin can a large number of surreal models and data be built, including digital product models, digital manufacturing models, and digital maintenance models. Design, manufacturing and maintenance are real-time, bi-directional, transparent, and need systematic considerations. Therefore, the application of digital twin technology to the integration of optimal design, intelligent manufacturing and efficient operation and maintenance is promising. For example, a digital bus based on the product model data interaction specification was exploited by Boeing. By improving existing virtual prototypes, a manufacturing-oriented digital twin design model was created, which initially enabled design and manufacturing collaboration [190].
The digital-twin-based integration of design-manufacturing-maintenance needs a framework for information-physical combination, which can describe and manage the heterogeneous, polymorphic, and massive data generated at different stages of product development and application. This has sound physical basis as it is a cross-discipline, personalized, and data-focused process [191, 192]. In terms of design, scientific modeling is required to maximize uncertainty control and thus determine reasonable safety intervals, which requires consideration of interaction of different failure mechanisms and development of physics-based accurate lifetime prediction methods. On the manufacturing side, achieving defect-free and residual stress-free manufacturing is being a long-time pursuit. The emerging technology of additive manufacturing is being improved to control internal defects and quantify its impact on mechanical performance. The life enhancement from surface strengthening is significant, while needs to be further quantified in extreme conditions. For maintenance, the focus is on digital twin technology to shift maintenance decisions from reliance on downtime inspection to a combination of online testing and simulation. Nevertheless, note that the digital twin technology is highly relied on sensing technology and its network. As the application of digital twin technology for industrial equipment requires the extraction of relevant operational data from physical entities, this requires high-fidelity, high-precision data sensors to achieve this. In addition, it is a challenge to transfer these massive amounts of data to the digital twin of the equipment in real time, which requires a latency-free, high-speed wireless network.
7 Summary and Outlook
In this overview, the definition of digital twin, and its development in design, manufacturing and maintenance of engineering components and equipment were summarized. Key challenges including convergence, standardization, long life, and high fidelity need to be addressed accordingly for a better digital twin technology. The development of feedback between design and manufacturing, and better standardization of repair data through physical inspection of mechanical equipment, and use of artificial intelligence-enabled repair data analysis, will greatly aid in the integration of design-manufacturing-maintenance based on digital twin concept. For this aim, there needs a framework for information-physical combination, in which a more accurate design, a defect-free manufacturing, a more intelligent maintenance, and a more advanced sensing technology, are prospected.
References
M An, W Lin, S Huang. An intelligent railway safety risk assessment support system for railway operation and maintenance analysis. The Open Transportation Journal, 2013, 7(1): 27-42.
X C Zhang, J G Gong, F Z Xuan. A deep learning based life prediction method for components under creep, fatigue and creep-fatigue conditions. International Journal of Fatigue, 2021, 148: 106236.
J R Li, F Tao, Y Cheng, et al. Big data in product lifecycle management. International Journal of Advanced Manufacturing Technology, 2015, 81(1-4): 667-684.
F Tao, Y Zuo, L D Xu, et al. Internet of things and BOM-based life cycle assessment of energy-saving and emission-reduction of products. Ieee Transactions on Industrial Informatics, 2014, 10(2): 1252-1261.
S Thons, M H Faber, W Rucker. Optimisation of the structural integrity management for offshore wind turbines. Bautechnik, 2012, 89(8): 525-532.
S Thöns. On the value of monitoring information for the structural integrity and risk management. Computer‐Aided Civil and Infrastructure Engineering, 2018, 33(1): 79-94.
X Rao, C Sheng, Z Guo, et al. A review of online condition monitoring and maintenance strategy for cylinder liner-piston rings of diesel engines. Mechanical Systems and Signal Processing, 2022, 165: 108385.
M Rabiei, M Modarres. A recursive Bayesian framework for structural health management using online monitoring and periodic inspections. Reliability Engineering & System Safety, 2013, 112: 154-164.
B Ernst, K Rohrig, R Jursa. Online-monitoring and prediction of wind power in german transmission system operation centres. World Wind Energy Conference, Cape Town, South Africa, Nov. 23-26, 2003.
Y Kim, H J Lim, H Sohn. Nonlinear ultrasonic modulation based failure warning for aluminum plates subject to fatigue loading. International Journal of Fatigue, 2018, 114: 130-137.
A Diveyev, E Sofronova. Application of network operator method for synthesis of optimal structure and parameters of automatic control system. IFAC Proceedings Volumes, 2008, 41(2): 6106-6113.
T Y Ju, D U Yeom, W B Kim. Preventive maintenance feedback status and methodology review. Transactions of the Korean Nuclear Society Autumn Meeting, Yeosu, Korea, Oct. 25-26, 2018.
T Zhang, C Zhang, Y Wang, et al. A vision-based fusion method for defect detection of milling cutter spiral cutting edge. Measurement, 2021, 177: 109248.
X J Xing, J L Wang, M D Li. Services and key technologies of the Internet of Things. ZTE Communications, 2020, 8(2): 26-29.
G W Harrison, E Haruvy, E E Rutström. Remarks on virtual world and virtual reality experiments. Southern Economic Journal, 2011, 78(1): 87-94.
J L Chu, L Li. Application of virtualization technology in internet data center of universities. Research and Exploration in Laboratory, 2012, 31(12): 67-71.
A Mukherjee, P Mukherjee, N Dey, et al. Lightweight sustainable intelligent load forecasting platform for smart grid applications. Sustainable Computing: Informatics and Systems, 2020, 25: 100356.
G Zhang, J Lin, Y Zhang, et al. Big data based intelligent operation and maintenance platform. 2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE), Beijing, China, Sept. 11-13, 2020: 249-253.
S D Yu. Research on cloud computing in the key technologies of railway intelligent operation and maintenance sharing platform. Journal of Physics: Conference Series, 2021, 1800(1): 012010.
R Li, P B Shi, M K Li, et al. Research on intelligent operation and maintenance platform based on automatic calibration system of electric energy meter. E3S Web of Conferences, 2020, 194(2): 03001.
R Soni, M Bhatia. Digital twin: intersection of mind and machine. International Journal of Computational Intelligence & IoT, 2019, 2(3): 667-670.
P Zheng, Y Lin, C H Chen, et al. Smart, connected open architecture product: an IT-driven co-creation paradigm with lifecycle personalization concerns. International Journal of Production Research, 2019, 57(8): 2571-2584.
N Nikolakis, K Alexopoulos, E Xanthakis, et al. The digital twin implementation for linking the virtual representation of human-based production tasks to their physical counterpart in the factory-floor. International Journal of Computer Integrated Manufacturing, 2019, 32(1): 1-12.
F Tao, M Zhang, Y S Liu, et al. Digital twin driven prognostics and health management for complex equipment. Cirp Annals-Manufacturing Technology, 2018, 67(1): 169-172.
S Legutko. Development trends in machines operation maintenance. Eksploatacja I Niezawodnosc-Maintenance and Reliability, 2009, (2): 8-16.
H Aydogan, F Aras. Design, simulation and virtual implementation of a novel fundamental programmable logic controllers laboratory in a 3D virtual world. The International Journal of Electrical Engineering & Education, 2019: 1-16.
F Tao, Q L Qi. New IT driven service-oriented smart manufacturing: framework and characteristics. IEEE Transactions on Systems Man Cybernetics-Systems, 2019, 49(1): 81-91.
J Leng, D Wang, W Shen, et al. Digital twins-based smart manufacturing system design in Industry 4.0: A review. Journal of Manufacturing Systems, 2021, 60: 119-137.
F Tao, L Zhang, V C Venkatesh, et al. Cloud manufacturing: a computing and service-oriented manufacturing model. Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture, 2011, 225(B10): 1969-1976.
F Tao, Q Qi, L Wang, et al. Digital twins and cyber–physical systems toward smart manufacturing and industry 4.0: Correlation and comparison. Engineering, 2019, 5(4): 653-661.
A Huang, Y Shen, Y Tang. Operation and maintenance design of industrial equipment based on mixed reality technology. Ordnance Industry Automation, 2019, 38(9): 72-77.
M Grieves. Digital twin: Manufacturing excellence through virtual factory replication. White Paper, 2014: 1-7.
F Tao, F Y Sui, A Liu, et al. Digital twin-driven product design framework. International Journal of Production Research, 2019, 57(12): 3935-3953.
E Glaessgen, D Stargel. The digital twin paradigm for future NASA and US Air Force vehicles. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA, Honolulu, Hawail, Apr. 23-26, 2012: 1818.
A El Saddik. Digital twins: The convergence of multimedia technologies. IEEE multimedia, 2018, 25(2): 87-92.
R Söderberg, K Wärmefjord, J S Carlson, et al. Toward a digital twin for real-time geometry assurance in individualized production. CIRP Annals, 2017, 66(1): 137-140.
R N Bolton, J R Mccoll-Kennedy, L Cheung, et al. Customer experience challenges: bringing together digital, physical and social realms. Journal of Service Management, 2018, 29(5): 776-808.
E Negri, S Berardi, L Fumagalli, et al. MES-integrated digital twin frameworks. Journal of Manufacturing Systems, 2020, 56: 58-71.
L Zhang, L F Zhou, B K P Horn. Building a right digital twin with model engineering. Journal of Manufacturing Systems, 2021, 59: 151-164.
G Githens. Product lifecycle management: driving the next generation of lean thinking by Michael Grieves. Journal of Product Innovation Management, 2007, 24(3): 278-280.
M Grieves, J Vickers. Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary Perspectives on Complex Systems. Springer. 2017: 85-113.
F Tao, W Liu, J Liu, et al. Digital twin and its potential application exploration. Computer Integrated Manufacturing Systems, 2018, 24(1): 1-18.
H J Zhang, G H Zhang, Q Yan. Digital twin-driven cyber-physical production system towards smart shop-floor. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(11): 4439-4453.
C Shen, Y Chen, B Chen, et al. A novel robot kinematic calibration method based on common perpendicular line model. Industrial Robot: An International Journal, 2018, 45(6): 766-775.
Y Cheng, Q Qi, F Tao. New IT-driven manufacturing service management: research status and prospect. China Mechanical Engineering, 2018, 29(18): 2177.
X X Liu, L Tang, H B Zeng, et al. Smart design and simulation of aerospace control system based on digital twin. Journal of System Simulation, 2019, 31(3): 377. (in Chinese)
M L Xiong, H W Wang, Q Fu, et al. Digital twin-driven aero-engine intelligent predictive maintenance. International Journal of Advanced Manufacturing Technology, 2021, 114(11-12): 3751-3761.
H X Cai, J M Zhu, W Zhang. Quality deviation control for aircraft using digital twin. Journal of Computing and Information Science in Engineering, 2021, 21: 3.
M Liao, G Renaud, Y Bombardier. Airframe digital twin technology adaptability assessment and technology demonstration. Engineering Fracture Mechanics, 2020, 225: 106793.
J Wang, Y Xiang, Z He. Models and implementation of digital twin based spacecraft system engineering. Computer Integrated Manufacturing Systems, 2019, 25(6): 1348-1360.
G Knapp, T Mukherjee, J Zuback, et al. Building blocks for a digital twin of additive manufacturing. Acta Materialia, 2017, 135: 390-399.
G Lugaresi, A Matta. Automated manufacturing system discovery and digital twin generation. Journal of Manufacturing Systems, 2021, 59: 51-66.
Y L Fang, C Peng, P Lou, et al. Digital-twin-based job shop scheduling toward smart manufacturing. IEEE Transactions on Industrial Informatics, 2019, 15(12): 6425-6435.
T Debroy, W Zhang, J Turner, et al. Building digital twins of 3D printing machines. Scripta Materialia, 2017, 135: 119-124.
Y G Kabaldin, P V Kolchin, D A Shatagin, et al. Digital twin for 3D printing on CNC machines. Russian Engineering Research, 2019, 39(10): 848-851.
B Bjornsson, C Borrebaeck, N Elander, et al. Digital twins to personalize medicine. Genome Medicine, 2019, 12: 1.
Y Peng, M Zhang, F Q Yu, et al. Digital twin hospital buildings: an exemplary case study through continuous lifecycle integration. Advances in Civil Engineering, 2020: 1-13.
J Masison, J Beezley, Y Mei, et al. A modular computational framework for medical digital twins. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118(20): 1-11.
M N K Boulos, P Zhang. Digital twins: From personalised medicine to precision public health. Journal of Personalized Medicine, 2021, 11(8): 1-12.
C L Gargalo, S C De Las Heras, M N Jones, et al. Towards the development of digital twins for the bio-manufacturing industry. Digital Twins, 2020: 1-34.
K Nemtinov, M Eruslanova, A Zazulya, et al. Creating a digital twin of an agricultural machine. MATEC Web of Conferences, 2020, 329: 1-8.
M Matulis, C Harvey. A robot arm digital twin utilising reinforcement learning. Computers & Graphics-Uk, 2021, 95: 106-114.
E G Kaigom, J Rossmann. Value-driven robotic digital twins in cyber-physical applications. IEEE Transactions on Industrial Informatics, 2021, 17(5): 3609-3619.
X Li, B He, Y M Zhou, et al. Multisource model-driven digital twin system of robotic Assembly. IEEE Systems Journal, 2021, 15(1): 114-123.
Y N Bulatov, A V Kryukov, K E Korotkova. Digital twin of the distributed generation plant. IOP Conference Series: Materials Science and Engineering, 2020, 976: 012024.
J P Huang, L L Zhao, F W Wei, et al. The application of digital twin on power industry. 6th International Conference on Advances in Energy Resources and Environment Engineering (ICAESEE), Chongqing, China, Nov. 20-22, 2020.
J E Taylor, G Bennett, N Mohammadi. Engineering smarter cities with smart city digital twins. Journal of Management in Engineering, 2021, 37: 6.
A E Burov, O G Burova. Development of digital twin for composite pressure vessel. Journal of Physics: Conference Series, 2020, 1441: 012133.
C H Zhou, F F Zhang, B Wei, et al. Digital twin-based stamping system for incremental bending. International Journal of Advanced Manufacturing Technology, 2021, 116(1-2): 389-401.
A Ladj, Z Q Wang, O Meski, et al. A knowledge-based digital shadow for machining industry in a digital twin perspective. Journal of Manufacturing Systems, 2021, 58: 168-179.
R Bambura, M Solc, M Dado, et al. Implementation of digital twin for engine block manufacturing processes. Applied Sciences-Basel, 2020, 10: 18.
A I Rudskoy, N G Kolbasnikov. Digital twins of processes of thermomechanical treatment of steel. Metal Science and Heat Treatment, 2020, 62(1-2): 3-10.
A Martinez-Gutierrez, J Diez-Gonzalez, R Ferrero-Guillen, et al. Digital twin for automatic transportation in industry 4.0. Sensors, 2021, 21: 10.
O El Marai, T Taleb, L Song. Roads infrastructure digital twin: A step toward smarter cities realization. IEEE Network, 2021, 35(2): 136-143.
Y Liu, Y H Sun, A Yang, et al. Digital twin-based ecogreen building design. Complexity, 2021: 1-10.
E J Tuegel, A R Ingraffea, T G Eason, et al. Reengineering aircraft structural life prediction using a digital twin. International Journal of Aerospace Engineering, 2011: 1687-5966.
E Tuegel. The airframe digital twin: some challenges to realization. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA, Honolulu, Hawail, Apr. 23-26, 2012: 1812.
C Li, S Mahadevan, Y Ling, et al. A dynamic Bayesian network approach for digital twin. 19th AIAA Non-Deterministic Approaches Conference, Grapevine, Texas, Jan. 9-13, 2017: 1566.
A Cerrone, J Hochhalter, G Heber, et al. On the effects of modeling as-manufactured geometry: toward digital twin. International Journal of Aerospace Engineering, 2014: 1-10.
M Dreyer, C Nicolet, A Gaspoz, et al. Monitoring 4.0 of penstocks: digital twin for fatigue assessment. IOP Conference Series: Earth and Environmental Science, 2021, 774: 012009.
F Piltan, J M Kim. Crack size identification for bearings using an adaptive digital twin. Sensors, 2021, 21: 15.
E M Kraft. The air force digital thread/digital twin-life cycle integration and use of computational and experimental knowledge. 54th AIAA Aerospace Sciences Meeting, 2016: 0897.
G N Schroeder, C Steinmetz, C E Pereira, et al. Digital twin data modeling with automationml and a communication methodology for data exchange. IFAC-PapersOnLine, 2016, 49(30): 12-17.
S Rabah, A Assila, E Khouri, et al. Towards improving the future of manufacturing through digital twin and augmented reality technologies. Procedia Manufacturing, 2018, 17: 460-467.
B A Talkhestani, N Jazdi, W Schlögl, et al. Consistency check to synchronize the digital twin of manufacturing automation based on anchor points. Procedia Cirp, 2018, 72: 159-164.
F Tao, J F Cheng, Q L Qi, et al. Digital twin-driven product design, manufacturing and service with big data. International Journal of Advanced Manufacturing Technology, 2018, 94(9-12): 3563-3576.
R Vrabič, J A Erkoyuncu, P Butala, et al. Digital twins: Understanding the added value of integrated models for through-life engineering services. Procedia Manufacturing, 2018, 16: 139-146.
B He, K J Bai. Digital twin-based sustainable intelligent manufacturing: a review. Advances in Manufacturing, 2021, 9(1): 1-21.
K M Alam, A El Saddik. C2PS: A digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE Access, 2017, 5: 2050-2062.
T H J Uhlemann, C Lehmann, R Steinhilper. The digital twin: Realizing the cyber-physical production system for industry 4.0. Procedia Cirp, 2017, 61: 335-340.
T H J Uhlemann, C Schock, C Lehmann, et al. The digital twin: demonstrating the potential of real time data acquisition in production systems. Procedia Manufacturing, 2017, 9: 113-120.
E Negri, L Fumagalli, C Cimino, et al. FMU-supported simulation for CPS digital twin. Procedia manufacturing, 2019, 28: 201-206.
A Banerjee, R Dalal, S Mittal, et al. Generating digital twin models using knowledge graphs for industrial production lines. UMBC Information Systems Department, 2017: 1-6.
J Vachálek, L Bartalský, O Rovný, et al. The digital twin of an industrial production line within the industry 4.0 concept. 2017 21st International Conference on Process Control (PC), Štrbské Pleso, Slovakia, June. 6-9, 2017: 258-262.
R Bohlin, J Hagmar, K Bengtsson, et al. Data flow and communication framework supporting digital twin for geometry assurance. ASME International Mechanical Engineering Congress and Exposition, Tampa, Florida, USA, Nov. 3-9, 2017: 1-7.
L Hu, N T Nguyen, W Tao, et al. Modeling of cloud-based digital twins for smart manufacturing with MT connect. Procedia Manufacturing, 2018, 26: 1193-1203.
Y Cai, B Starly, P Cohen, et al. Sensor data and information fusion to construct digital-twins virtual machine tools for cyber-physical manufacturing. Procedia Manufacturing, 2017, 10: 1031-1042.
B Scaglioni, G Ferretti. Towards digital twins through object-oriented modelling: a machine tool case study. IFAC-PapersOnLine, 2018, 51(2): 613-618.
Y G Kabaldin, D A Shatagin, M S Anosov, et al. CNC machine tools and digital twins. Russian Engineering Research, 2019, 39(8): 637-644.
W Luo, T Hu, C Zhang, et al. Digital twin for CNC machine tool: modeling and using strategy. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(3): 1129-1140.
Q Z Qiao, J J Wang, L K Ye, et al. Digital twin for machining tool condition prediction. 52nd CIRP Conference on Manufacturing Systems (CMS), Ljubljana, Slovenia, Jun. 12-14, 2019: 1388-1393.
B Schleich, N Anwer, L Mathieu, et al. Shaping the digital twin for design and production engineering. CIRP Annals, 2017, 66(1): 141-144.
F Tao, M Zhang. Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access, 2017, 5: 20418-20427.
F Tao, M Zhang, J Cheng, et al. Digital twin workshop: a new paradigm for future workshop. Computer Integrated Manufacturing Systems, 2017, 23(1): 1-9.
G S Martínez, S Sierla, T Karhela, et al. Automatic generation of a simulation-based digital twin of an industrial process plant. IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society, 2018: 3084-3089.
J Guo, N Zhao, L Sun, et al. Modular based flexible digital twin for factory design. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(3): 1189-1200.
Q Liu, H Zhang, J Leng, et al. Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system. International Journal of Production Research, 2019, 57(12): 3903-3919.
C B Zhuang, T Miao, J H Liu, et al. The connotation of digital twin, and the construction and application method of shop-floor digital twin. Robotics and Computer-Integrated Manufacturing, 2021, 68: 102075.
M Lohtander, N Ahonen, M Lanz, et al. Micro manufacturing unit and the corresponding 3D-model for the digital twin. Procedia manufacturing, 2018, 25: 55-61.
M Lohtander, E Garcia, M Lanz, et al. Micro manufacturing unit–creating digital twin objects with common engineering software. Procedia Manufacturing, 2018, 17: 468-475.
M Macchi, I Roda, E Negri, et al. Exploring the role of digital twin for asset lifecycle management. IFAC-PapersOnLine, 2018, 51(11): 790-795.
F Tao, M Zhang, Y Liu, et al. Digital twin driven prognostics and health management for complex equipment. Cirp Annals, 2018, 67(1): 169-172.
Y Xu, Y Sun, X Liu, et al. A digital-twin-assisted fault diagnosis using deep transfer learning. IEEE Access, 2019, 7: 19990-19999.
C Moussa, K Ai-Haddad, B Kedjar, et al. Insights into digital twin based on finite element simulation of a large hydro generator. IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society, 2018: 553-558.
Q Min, Y Lu, Z Liu, et al. Machine learning based digital twin framework for production optimization in petrochemical industry. International Journal of Information Management, 2019, 49: 502-519.
C Pylianidis, S Osinga, I N Athanasiadis. Introducing digital twins to agriculture. Computers and Electronics in Agriculture, 2021, 184: 1-25.
H A Park, G Byeon, W Son, et al. Digital twin for operation of microgrid: optimal scheduling in virtual space of digital twin. Energies, 2020, 13(20): 1-15.
B Kochunas, X Huan. Digital twin concepts with uncertainty for nuclear power applications. Energies, 2021, 14(14): 1-32.
Y Wei, T Hu, Y Wang, et al. Implementation strategy of physical entity for manufacturing system digital twin. Robotics and Computer-Integrated Manufacturing, 2022, 73: 102259.
W Q Yang, Y Zheng, S Y Li. Application status and prospect of digital twin for on-orbit spacecraft. IEEE Access, 2021, 9: 106489-106500.
M Shafto, M Conroy, R Doyle, et al. Modeling, simulation, information technology & processing roadmap. National Aeronautics and Space Administration, 2012, 32: 1-38.
J T Xu, T J Guo. Application and research on digital twin in electronic cam servo motion control system. International Journal of Advanced Manufacturing Technology, 2021, 112(3-4): 1145-1158.
J Vitorino, E Ribeiro, R Silva, et al. Industry 4.0 - digital twin applied to direct digital manufacturing. Applied Mechanics and Materials, 2019, 890: 54-60.
Z Shu, J Wan, D Zhang, et al. Cloud-integrated cyber-physical systems for complex industrial applications. Mobile Networks and Applications, 2016, 21(5): 865-878.
C Verdouw, B Tekinerdogan, A Beulens, et al. Digital twins in smart farming. Agricultural Systems, 2021, 189.
S Neethirajan, B Kemp. Digital twins in livestock farming. Animals, 2021, 11: 4.
Z Lv, S Xie. Artificial intelligence in the digital twins: State of the art, challenges, and future research topics. Digital Twin, 2021, 1(12): 12.
H Lan, Y Ding, J Hong, et al. A web-based manufacturing service system for rapid product development. Computers in Industry, 2004, 54(1): 51-67.
H Kim, J K Lee, J H Park, et al. Applying digital manufacturing technology to ship production and the maritime environment. Integrated Manufacturing Systems, 2002, 13(5): 295-305.
J Lee, E Lapira, S Yang, et al. Predictive manufacturing system-Trends of next-generation production systems. IFAC proceedings volumes, 2013, 46(7): 150-156.
Z Z Hu, P L Tian, S W Li, et al. BIM-based integrated delivery technologies for intelligent MEP management in the operation and maintenance phase. Advances in Engineering Software, 2018, 115: 1-16.
X H Chen, H Zhao, G F Duan. Collaborative commerce mode of enterprises intelligent e-maintenance system. 3rd International Conference on Electronic Commerce Engineering, Hangzhou, China, Oct. 24-27, 2003: 232-235.
L Wang. Research on the docking technology of final installation for aeroengine low pressure turbine unit based on digital twin. Computer Measurement & Control, 2018, 26(10): 286-290. (in Chinese)
Z Yuliang, W Zhangjiapeng. Digital twin technology for spacecraft on-orbit assembly. Navigationand Control, 2018, 17(3): 75-82. (in Chinese)
P O’donovan, K Leahy, K Bruton, et al. An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities. Journal of Big Data, 2015, 2(1): 1-26.
S Yin, O Kaynak. Big data for modern industry: challenges and trends [point of view]. Proceedings of the IEEE, 2015, 103(2): 143-146.
G Shao, S-J Shin, S Jain. Data analytics using simulation for smart manufacturing. Proceedings of the Winter Simulation Conference 2014, 2014: 2192-2203.
A Kusiak. Smart manufacturing must embrace big data. Nature, 2017, 544(7648): 23-25.
F Tao, Q Qi, A Liu, et al. Data-driven smart manufacturing. Journal of Manufacturing Systems, 2018, 48: 157-169.
M Obitko, V Jirkovský, J Bezdíček. Big data challenges in industrial automation. Industrial Applications of Holonic and Multi-Agent Systems. Springer. 2013: 305-316.
A Galletti, D C Papadimitriou. How big data analytics are perceived as a driver for competitive advantage: A qualitative study on food retailers. Department of Business Studies, 2013, 11(4): 369-370.
R Dubey, A Gunasekaran, S J Childe, et al. The impact of big data on world-class sustainable manufacturing. The International Journal of Advanced Manufacturing Technology, 2016, 84(1-4): 631-645.
Y Zhang, S Ren, Y Liu, et al. A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. Journal of Cleaner Production, 2017, 142: 626-641.
P Evangeline, Anandhakumar. Chapter two - digital twin technology for “smart manufacturing”. Advances in Computers. Elsevier. 2020: 35-49.
F Tao, H Zhang, A Liu, et al. Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 2018, 15(4): 2405-2415.
C Esposito, A Castiglione, B Martini, et al. Cloud manufacturing: security, privacy, and forensic concerns. IEEE Cloud Computing, 2016, 3(4): 16-22.
C Zhuang, J Liu, H Xiong, et al. Connotation, architecture and trends of product digital twin. Computer Integrated Manufacturing Systems, 2017, 23(4): 753-768. (in Chinese)
B B Luo, X Qiong, C X Wang. Research on application of intelligent operation and maintenance in conventional substations. 2019 IEEE Sustainable Power and Energy Conference (iSPEC), Beijing, China, Nov. 21-23, 2019: 740-743.
J Tao, M Zhong, J Wang, et al. Engineering practice of on-line monitoring and intelligent diagnosis system for secondary equipment in smart substation. 2020 IEEE 3rd International Conference on Electronics Technology (ICET), Chengdu, China, May. 8-12, 2020: 397-403.
Z R Huang, W Mao, M Chen, et al. An intelligent operation and maintenance system for power consumption based on deep learning. IOP Conference Series: Materials Science and Engineering, 2019, 486: 012107.
D Wu, P Li, H Zhou, et al. Research and application of intelligent maintenance of relay protection equipment based on internet of things technology. IOP Conference Series: Earth and Environmental Science, 2020: 032043.
S Song, P D Xu, Y Yang, et al. Intelligent mobile operation and maintenance technology and its application of for relay protection based on power wireless virtual private network. International Conference on Power System Technology (POWERCON), Guangzhou, China, Nov. 06-08, 2018: 3142-3150.
W W Liang, F Ouyang, S N Wang, et al. Smart operation and maintenance platform of protection relay based on mobile sensing and big data. Asia Conference on Geological Research and Environmental Technology (GRET), Electr Network, Oct. 10-11, 2020.
P C Lin, J C Gu, M T Yang. An intelligent maintenance model to assess the condition-based maintenance of circuit breakers. International Transactions on Electrical Energy Systems, 2015, 25(10): 2376-2393.
P C Lin, J C Gu, M T Yang. Intelligent maintenance model for condition assessment of circuit breakers using fuzzy set theory and evidential reasoning. IET Generation Transmission & Distribution, 2014, 8(7): 1244-1253.
Z Pei, Z Shutao, S Lu, et al. Research on remote fault diagnosis of high voltage circuit breakers based on LabVIEW High voltage circuit breaker. Advanced Materials Research, 2013, 765-767: 2294-2298.
M Gregor, M Haluška, M Fusko, et al. Model of intelligent maintenance systems. Annals of DAAAM Proceedings of the 26-th DAAAM International Symposium on Intelligent Manufacturing and Automation, Vienna, Austria, 2016: 1097-1101.
J X Wang, D Z Wang, S H Wang, et al. Fault diagnosis of bearings based on multi-sensor information fusion and 2D convolutional neural network. IEEE Access, 2021, 9: 23717-23725.
Q Xu, S Xia, B He. Operation and maintenance analysis on substation intelligent inspection robot system. Zhejiang Electric Power, 2014, 33(5): 30-32.
W Xidian, W Lei, J Zihan, et al. Intelligent Network operation and maintenance based on deep learning technology. 10th International Conference on Computer Engineering and Networks Advances in Intelligent Systems and Computing (AISC 1274), 2021: 200-207.
Q Z Tu, M Sun, J Yan, et al. Research and development of vehicle intelligent terminal system for engineering equipment. 2011 International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), 2011: 1031-1036.
C F Qu, D L Wang, W X Liu, et al. Design and implementation of an intelligent operation and maintenance management cloud platform for green buildings group. 5th International Conference on Electrical Engineering, Control and Robotics (EECR), Guangzhou, China, Jan. 12-14, 2019.
M Fu, J Y Song, J T Zhao, et al. Intelligent operation and maintenance of micro-grid technology and system development. 3rd International Conference on Environmental Science and Material Application (ESMA), Chongqing, China, Nov. 25-26, 2017.
L Wang, Y Zhao, Z K Chen. Research on key technologies of intelligent operation and maintenance of communication Network. Journal of Physics: Conference Series, 2021, 1856: 012022.
Z H Mu, Q H Hu, M A Liu, et al. An immersive nuclear power intelligent control system based on virtual reality. International Conference on Smart Grid and Electrical Automation (ICSGEA), Changsha, China, May. 27-28, 2017: 151-154.
T Washio, M Sakuma, H Furukawa, et al. Criteria of diversity evaluation for intelligent diagnosis of nuclear power plants. Journal of the Atomic Energy Society of Japan, 1995, 37(12): 1128-1136.
Y Zhou, Y Liang, R Li. Research on intelligent diagnosis technology of emergency diesel generator in nuclear power plant based on new monitoring method. IOP Conference Series: Materials Science and Engineering, 2019, 677(3): 1-5.
H Kim, S Choi, S-H Chang. Development strategies on an intelligent software system for total operation and maintenance of nuclear power plants. The 7th International Conference on Human-Computer Interaction, 1997.
B Guo, Y Shu, R Li, et al. Design and engineering application of intelligent operation and maintenance service system for distributed poverty alleviation photovoltaic. Electrical Measurement and Instrumentation, 2019, 56(7): 84-88.
S G Yoon, S Soobong, D H Shin. Numerical model updating for bridge maintenance using digital-twin model. KIBIM Magazine, 2018, 8(4): 34-40.
Z L Lu, S G Hu, T Y Wang, et al. Remote monitoring and intelligent fault diagnosis technology research based on open CNC system. Advanced Materials Research, 2013, 819: 234-237.
R Shi, Z Guo, Z Song. Research based on state monitoring of CNC machine tools intelligent security system. Applied Mechanics and Materials, 2013, 427-429: 1328-1332.
J Z Lin, D Y Jiang, B Geng, et al. Research on the remote monitoring and fault diagnosis of CNC system based on network. 2nd International Conference on Functional Manufacturing and Mechanical Dynamics, Hangzhou, China, Jan. 22-25, 2012: 465-470.
H L Liu, J C Dong, T Y Wang, et al. The digital manufacturing equipment and development of high speed and high precision with monitoring and intelligent maintenance. Key Engineering Materials, 2016, 693: 1948-1953.
B D Deebak, F Al Turjman. Digital-twin assisted: Fault diagnosis using deep transfer learning for machining tool condition. International Journal of Intelligent Systems, 2021: 1-28.
X H Jiang, T Zhang, C X Xie. Study on the intelligent tele-monitoring system of CNC machine. Modular Machine Tool & Automatic Manufacturing Technique, 2014, (9): 77-79, 84.
A A Kutin, V V Bushuev, V V Molodtsov. Digital twins of mechatronic machine tools for modern manufacturing. IOP Conference Series: Materials Science and Engineering, 2019: 012070.
Z Liu, X Xu. Design of intelligent management platform for coal mine electromechanical equipments based on Internet of things. Industry and Mine Automation, 2019, 45(4): 101-104.
J Que. Construction and implementation of platform for intelligent mine. Industry and Mine Automation, 2018, 44(4): 90-93.
S Duer. Assessment of the operation process of wind power plant's equipment with the use of an artificial neural network. Energies, 2020, 13: 10.
Y Kong, Z Y Qin, T Y Wang, et al. An enhanced sparse representation-based intelligent recognition method for planet bearing fault diagnosis in wind turbines. Renewable Energy, 2021, 173: 987-1004.
H Li, H Wang, Y Cheng, et al. Technology and application of data-driven intelligent services for complex products. China Mechanical Engineering, 2020, 31(7): 757-772. (in Chinese)
B S Peng, H Xia, X Z Lv, et al. An intelligent fault diagnosis method for rotating machinery based on data fusion and deep residual neural network. Applied Intelligence, 2021, 52: 3051-3065.
X Chen, M A Eder, A Shihavuddin, et al. A human-cyber-physical system toward intelligent wind turbine operation and maintenance. Sustainability, 2021, 13(2): 561.
K Xiao, Y K Shi, Q Z Ma, et al. The intelligent ultrasonic system for quality testing of weld connections in turbine runners. International Forum on Mechanical and Material Engineering (IFMME 2013), Guangzhou, China, Jun. 13-14, 2013: 1543-1546.
Z W Pu, L Wan, M Wu, et al. Design of intelligent micro network group intelligent operation and maintenance management platform based on cloud architecture. 2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), Shenyang, China, Nov.20-22, 2020: 523-526.
Z S Liu, A T Jiang, A S Zhang, et al. Intelligent prediction method for operation and maintenance safety of prestressed steel structure based on digital twin technology. Advances in Civil Engineering, 2021, 2021: 1-17.
W Z Liao, Y Wang, E S Pan. Single-machine-based predictive maintenance model considering intelligent machinery prognostics. International Journal of Advanced Manufacturing Technology, 2012, 63(1-4): 51-63.
M Schluse, J Rossmann. From simulation to experimentable digital twins: Simulation-based development and operation of complex technical systems. 2016 IEEE International Symposium on Systems Engineering (ISSE), Edinburgh, UK, Oct. 3-5, 2016: 1-6.
M Helu, A Joseph, T Hedberg Jr. A standards-based approach for linking as-planned to as-fabricated product data. CIRP Annals, 2018, 67(1): 487-490.
H Li, Y Ji, G Luo, et al. A modular structure data modeling method for generalized products. The International Journal of Advanced Manufacturing Technology, 2016, 84(1): 197-212.
K T Ulrich. Product design and development. Tata McGraw-Hill Education. 2003.
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Supported by National Natural Science Foundation of China (Grant Nos. 51922041, 51835003).
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YF: writing—original draft, investigation. GZ: reference collection and analysis. MZ: investigation, supervision, writing—review and editing, manuscript revision, funding. FX: investigation, project administration, funding, conceptualization, manuscript revision. All authors read and approved the final manuscript.
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Yang Fu, born in 1995, is currently a PhD candidate at Key Laboratory of Pressure Systems and Safety, Ministry of Education, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China.
Gang Zhu, born in 1992, is currently a PhD candidate at Key Laboratory of Pressure Systems and Safety, Ministry of Education, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China.
Mingliang Zhu, born in 1984, is currently a professor at Key Laboratory of Pressure Systems and Safety, Ministry of Education, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China.
Fuzhen Xuan, born in 1970, is currently a professor at Key Laboratory of Pressure Systems and Safety, Ministry of Education, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China.
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Fu, Y., Zhu, G., Zhu, M. et al. Digital Twin for Integration of Design-Manufacturing-Maintenance: An Overview. Chin. J. Mech. Eng. 35, 80 (2022). https://doi.org/10.1186/s10033-022-00760-x
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DOI: https://doi.org/10.1186/s10033-022-00760-x