Abstract
Machine learning and artificial intelligence have emerged as the most advanced technologies in today's society for solving issues as well as assessing and forecasting occurrences. The use of AI and ML in various organizations is to capitalize on the benefits of vast amounts of data based on scientific approaches, notably machine learning, which may identify patterns of decision-making and minimize the need for human intervention. The purpose of this research is to develop a suitable neural network model, which is a component of AI and ML, to assess and forecast crack propagation in a bearing with a seeded crack. The bearing was continually run for many hours, and data were retrieved at time intervals that might be utilized to forecast crack growth. The variables RMS, crest factor, SNR, skewness, kurtosis, and Shannon entropy are collected from the continuously running bearing and utilized as input parameters, with total crack area and crack width regarded as output parameters. Finally, utilizing several methodologies of the neural network tool in MATLAB, a realistic ANN model was trained to predict the crack area and crack width.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Abbreviations
- ANN:
-
Artificial neural network
- ML:
-
Machine learning
- AI:
-
Artificial intelligence
- RMS:
-
Root-mean-square
- SNR:
-
Signal-to-noise ratio
- NN tool:
-
Neural network tool
References
Kim T, Vecchietti LF, Choi K, Lee S, Har D (2021) Machine learning for advanced wireless sensor networks: a review. IEEE Sens J 21(11):12379–12397. https://doi.org/10.1109/JSEN.2020.3035846
Liu J, Li T, Xie P, Du S, Teng F, Yang X (2020) Urban big data fusion based on deep learning: an overview. Inf Fus 53:123–133
Kumar A, Gupta PK, Srivastava A (2020) A review of modern technologies fortackling COVID-19 pandemic. Diabetes Metab Syndr 14(4):569–573
Lalmuanawma S, Hussain J, Chhakchhuak L (2020) Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: a review. Chaos, Solitons Fractals 139:110059
Prakash KB, Imambi SS, Ismail M, Kumar TP, Pawan YN (2020) Analysis, prediction and evaluation of covid-19 datasets using machine learning algorithms. Int J 8(5):2199–2204
Tuli S, Tuli S, Tuli R, Gill SS (2020) Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet Things 11:100222
Yan L, Zhang HT, Goncalves J, Xiao Y, Wang M, Guo Y, Yuan Y (2020) An interpretable mortality prediction model for COVID-19 patients. Nat Mach Intell 2(5):283–288
Shona D, Shobana A (2016) Fast and effective network intrusion detection technique using hybrid revised algorithms
Tran VL, Thai DK, Nguyen DD (2020) Practical artificial neural network tool for predicting the axial compression capacity of circular concrete-filled steel tube columns with ultra-high-strength concrete. Thin-Walled Struct 151:106720
Tran TT, Nguyen PC, Pham QH (2021) Vibration analysis of FGM plates in thermal environment resting on elastic foundation using ES-MITC3 element and prediction of ANN. Case Stud Therm Eng 24:100852
Zippelius A, Hanß A, Schmid M, Pérez-Velázquez J, Elger G (2022) Reliability analysis and condition monitoring of SAC+ solder joints under high thermomechanical stress conditions using neuronal networks. Microelectron Reliab 129:114461
Boujoudar Y, Azeroual M, Elmoussaoui H, Lamhamdi T (2021) Intelligent control of battery energy storage for microgrid energy management using ANN. Int J Electr Comput Eng 11(4) (2088-8708)
Aghdasinia H, Hosseini SS, Hamedi J (2021) Improvement of a cement rotary kiln performance using artificial neural network. J Ambient Intell Humaniz Comput 12(7):7765–7776
Alonso-Montesinos J, BallestrÃn J, López G, Carra E, Polo J, Marzo A, Batlles FJ (2021) The use of ANN and conventional solar-plant meteorological variables to estimate atmospheric horizontal extinction. J Clean Prod 285:125395
Zhang P, Gao Z, Cao L, Dong F, Zou Y, Wang K, Sun P (2022) Marine systems and equipment prognostics and health management: a systematic review from health condition monitoring to maintenance strategy. Machines 10(2):72
Senthiil PV, Sirusshti VA, Sathish T (2019) Equivalent stress prediction of automobile structural member using FEA-ANN technique. Int J Mech Prod Eng Res Dev 9(2):757–768
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shoor, S., Gopaluni, D.T., Tamang, W., Prasad, P., Singh, H., Singh, M. (2024). Analysis of Crack Dimensions During Crack Propagation Using Neural Network. In: Bhardwaj, A., Pandey, P.M., Misra, A. (eds) Optimization of Production and Industrial Systems. CPIE 2023. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-99-8343-8_19
Download citation
DOI: https://doi.org/10.1007/978-981-99-8343-8_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8342-1
Online ISBN: 978-981-99-8343-8
eBook Packages: EngineeringEngineering (R0)