Keywords

1 Introduction

Electricity consumption by the industrial sector accounted for 33% of total industrial energy consumption in the European Union in 2020 [1]. Moreover, companies are facing continuously rising electricity costs, forcing them to treat electric energy as a resource that must be strategically planned and managed. A prerequisite to identify energy efficiency potentials and to increase energy efficiency without compromising production targets is the measurement and analysis of the energy demand [2]. Methods to identify energy efficiency measures of production machines are associated with high costs for metering equipment and time consuming analysis requiring expertise [2, 3]. In industrial practice companies tend to underestimate the monetary potential of investments in energy efficiency and therefore often lack energy measurements on machine and component level [4]. A further significant barrier for identifying a large number of energy efficiency measures are limited in-house skills for processing and using measurement data [5].

Various scientific approaches, ranging from simulations to analysis of measurement data and expert knowledge, allow the identification of possible energy efficiency measures [6, 7]. Easy to applicate approaches are presented by [6, 8] and [9] but either do not offer sufficient accuracy or are limited to individual machine types such as machine tools. Furthermore, in existing approaches such as [6] and [3], data acquisition and analysis are performed separately in respect to time using systems that are independent of each other. As a result, time-consuming data export and data preparation from one system are required before data analysis can be applied on another system. Against this background, this paper presents a cost-efficient holistic framework to acquire and analyze energy data as an essential step in improving the energy efficiency of production machines.

Following the introduction, Sect. 2 reviews the background, including production machines, as well as the measurement and analysis of electrical data. The overall framework is presented along with the energy transparency and analysis system (ETAS) in Sect. 3, followed by case studies on two machines in Sect. 4. Finally, Sect. 5 concludes this paper with the summary and outlook.

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2 Background

2.1 Production Machines

In the context of this paper, the characterization of machine tools of [10] and [11] is extended to cover additional production machines. Based on this, production machines can be considered as an assembly of multiple electrical components that are required in their entirety to execute a specific production task [10, 11]. The energetic behavior of a production machine is therefore determined by the power consumption of its individual components and their interaction [10]. Thus, to identify energy efficiency potentials of production machines, it is necessary to measure and analyze the energy input and its distribution among the individual components.

2.2 Electricity Measurement

By measuring voltage and current in three-phase and single-phase networks as a function of time, all other specific information can be derived mathematically [12]. In a general three-phase network with alternating current, the total active power, which is a significant parameter for the energetic evaluation of electric machines, is calculated by using the following equation.

$${P}_{\text{tot}}=\sum\nolimits_{n=1}^{3}{P}_{n}=\sum\nolimits_{n=1}^{3}{U}_{{\text{N}}n} {I}_{n}\text{ cos}{ \varphi }_{n}$$
(1)

where \({\varphi }_{n}\) is the phase angle between the voltage \({U}_{{\text{N}}n}\) and current \({I}_{n}\) for each phase [2]. The measurement of the voltage \({U}_{{\text{N}}n}\) must be performed by direct tapping to each phase \({L}_{n}\). The current is measured indirectly with the magnetic field around the conductor via current transformers. Thus, an electrical measurement is possible without interrupting the circuit. [13]

2.3 Analysis of Electrical Energy Data

To derive energy efficiency measures for machines and components, it is necessary to analyze measurement and meta data [3]. Analyzing electrical load profiles enables the allocation of the total energy demand to single components, workpieces, machining operations and energy states as well as estimating and forecasting the energy demand per time interval [3, 14]. Further applications include the detection of load fluctuations and the identification of deviations from a reference load profile [15]. Supervised and unsupervised machine learning algorithms can be applied for this purpose to avoid time-consuming analyses that require expertise [3, 14].

3 Framework

3.1 Methodology

Following the concept proposed in [6, 8] and [3] for the assessment of energy efficiency potentials of machine tools, the framework shown in Fig. 1 is presented for all production machines powered by electrical energy. The framework can be divided into the three main building blocks preparation, Raspberry Pi App (RPi App) and Web App. The preparation begins with the selection of electrical consumers for the measurement. Relevant energy consumers can be identified using an energy portfolio or a Pareto analysis [16]. Suitable current transformers are then selected on the basis of circuit diagrams and the electrical installation is conducted. Following the preparation, the frontend of the RPi App allows the user to enter the measurement configuration, to verify measurement data graphically and to initiate and conclude the measurement. Simultaneously, values are continuously read from the measuring devices and stored into a SQLite database by the backend.

The Web App features direct import of data from the RPi App, management of multiple devices and analysis of measurement data involving machine learning algorithms with no additional expertise required for data preparation. In addition, data for long-term storage can also be exported via the Web App.

Fig. 1.
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Framework for identifying energy efficiency potentials of production machines.

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3.2 Energy Transparency and Analysis System

The developed energy transparency and analysis system (ETAS) which is shown in Fig. 2 includes the hardware and the RPi App as the software. The hardware contains a power analyzer with current measuring modules forming the measuring device. Further components of the hardware are current, voltage and temperature sensors, a RPi and a touchscreen. The software can be divided into the RPi App installed on the ETAS and the Web App deployed on the user’s computer or a server. The RPi App handles the measurement configuration and execution leading to the data acquisition and the Web App is used for data analysis and management including data export.

Hardware Implementation.

The hardware part of the ETAS consists of voltage probes, 24 current sensors and four multi-functional sensors (e.g. residual current or temperature sensors), which can be connected as analog inputs. The connectors enable the use of sensors of different accuracies and measurement ranges, depending on the nominal power and intended use. The sensors are attached to the power analyzer, which is a Janitza UMG 801, equipped with two Janitza 800-CT8-A modules. The power analyzer is connected to the RPi via an ethernet cable. The RPi’s storage is extended by a 120 Gigabyte SD-card, which provides sufficient capacity for up to four years of measurements when recording all measurement channels at a sampling rate of 1 Hz. A touchscreen connected to the RPi offers an interface for the measurement configuration and the visualization of the measured values. Additionally, all electronic components are galvanically isolated from the voltage supply and the analog inputs.

Fig. 2.
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Energy transparency and analysis system (ETAS).

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Software Implementation.

The measurement configuration is entered via a Python application, which is visualized on the touchscreen. The user can configure the sensors, bundle multiple measurement channels into a single consumer and define a sampling rate of up to 5 Hz. The final configuration is transferred to the power analyzer via Open Platform Communications Unified Architecture. The most recent measured values are visualized subsequently to verify their validity. If no currents \({I}_{n}\) are permanently negative, values of \({\varphi }_{n}\) are between 0 and 1, and the voltages \({U}_{{\text{N}}n}\) equal \((230\pm 10) \mathrm{V}\), the electrical installation is considered valid for the measurement. Afterwards, the measurement can be started and the RPi App reads the measured values from the power analyzer, stores them in a comma-separated text file and visualizes the trends of the active power for each component.

3.3 Web App

Due to the limited computational power of the RPi, an external Web App is used for further tasks. The Web App provides the functionality to manage multiple ETAS and import data from them via Secure File Transfer Protocol. In addition, analysis procedures such as machine learning algorithms with the subsequent calculation of energy key performance indicators can be deployed within the Web App. Moreover, the Web App aggregates all data from the managed ETAS within a SQLite database and allows the export of measurement data for long-term data storage.

4 Use Cases

4.1 Experimental Setup

The ETAS and the presented framework are applied to a cleaning machine (Fig. 3 (a)) and a lathe (Fig. 3 (b)) within the ETA Research Factory at the Technical University of Darmstadt. These machines are well suited for the use cases since they combine multiple electrical components and demonstrate the applicability of the framework to different production tasks. Within the same production chain, shafts are turned on the lathe and gears are processed in the cleaning machine to be later assembled into a gearbox. The ETAS is installed to all components on each machine. In the presented use cases, measurements were conducted on each machine for one hour, which was considered a representative period, since the cycle time is approximately 6 min for the cleaning machine and two minutes for the lathe.

Fig. 3.
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(a) Cleaning machine and (b) lathe at the ETA Research Factory.

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4.2 Results

The measurement results are partially shown in Fig. 4 for the cleaning machine. Algorithms such as those presented in [3] provide a possibility for automatic data analysis of load profiles for the detection of machine states. This enables energy performance indicators to be calculated. For the measurement data of the two presented production machines, the machine states were automatically identified using the machine learning algorithm Gaussian Naive Bayes [17] within the Web App. Subsequently, the non-production time factor (NPTF) is calculated, which is defined as the ratio of non-production time and total observed time [18]. Both the algorithm and the NPTF are suitable to demonstrate the application of the framework exemplarily. For the performed studies, the non-production time corresponds to the equipping time of the machines.

Fig. 4.
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Load profiles of the main power supply, hot-air dryer and heating wash tank of the cleaning machine.

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Figure 5 illustrates the calculated NPTF of the two machines alongside the relative energy consumption of all measured components together with their nominal power. This shows that the equipment time for the cleaning machine is comparatively high and that the main drives, heating wash tank and chiller require a considerable amount of electric energy. Moreover, the heating wash tank and hot-air dryer reveal how components with lower nominal power can consume more energy. The load profiles of the individual components indicate that the higher degree of utilization of the heating wash tank is responsible for this. Possible energy efficiency measures for these studies could include shortening the equipping time and improving the thermal insulation of the cleaning machine, as well as using more efficient main drives and chiller on the lathe. However, the measures need to be examined more closely for final conclusions to be drawn.

Fig. 5.
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NPTF and relative energy consumption of the measured components.

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5 Summary and Outlook

To provide a straightforward holisitc framework for all steps from initial measurement configuration to final data analysis, the ETAS was developed. The framework was applied on two production machines, showing its ability to quickly determine the components of the machines which might be suitable candidates for efficiency improvements. Nevertheless, the approach presented does not provide a quantifiable information about energy saving potentials or actual energy saving measures.

However, analyses as shown in Fig. 5 can provide deeper energy transparency on machine and component level and therefore be a starting point for identifying organizational and technical energy efficiency measures. Further developments of the software will include an extension of the available analysis algorithms, the integration of additional energy performance indicators and the implementation of an expert systems for energy efficiency measures.