Abstract
The creation of binary and multi-classification models with the goal of accurately detecting and categorizing motor defects is important to study. This work explores how autoencoders can be used to apply self-supervised learning in industrial settings. A generic temporal convolutional network with an autoencoder was built and trained using the milling data. The successful identification and classification of motor faults underscore the industrial anomaly detection using advanced machine learning techniques. Also, paper highlights the importance of data processing and domain expertise in achieving significant model performance improvements. The performance used a fraction of the data that was available, highlighting the need of future research to include a more extensive range of fault states and the intensities of those fault states in order to enhance the diagnostic capabilities of the model.
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Data Availability
The dataset generated and analyzed during the current study are available from the corresponding author on reasonable request.
References
Yang Q, Pattipati KR, Awasthi U, Bollas GM. Hybrid data-driven and model-informed online tool wear detection in milling machines. J Manuf Syst. 2022. https://doi.org/10.1016/j.jmsy.2022.04.001.
Tanuska P, Spendla L, Kebisek M, Duris R, Stremy M. Smart anomaly detection and prediction for assembly process maintenance in compliance with industry 4.0. Sensors. 2021;21(7):2376. https://doi.org/10.3390/s21072376.
Cooper C, Zhang J, Gao RX, Wang P, Ragai I. Anomaly detection in milling tools using acoustic signals and generative adversarial networks. Procedia Manufacturing. 2020;48:372-8.https://doi.org/10.1016/j.promfg.2020.05.059.
Qurthobi A, Maskeliūnas R, Damaševičius R. Detection of mechanical failures in industrial machines using overlapping acoustic anomalies: a systematic literature review. Sensors. 2022;22(10):3888.https://doi.org/10.3390/s22103888
Soori M, Arezoo B, Dastres R. Internet of things for smart factories in industry 4.0, a review. ScienceDirect, 2023 https://doi.org/10.1016/j.iotcps.2023.04.006.
Mallioris P, Diamantis E, Bialas C, Bechtsis D. Predictive maintenance framework for assessing health state of centrifugal pumps. IJ-AI. 2023;https://doi.org/10.11591/ijai.v13.i1.pp850-862
Jha UC, Sai RJ, Reddy MVBK, Singh A. “Analysis of predictive maintenance in industry 4.0: A review”. Int J Mech Eng http://www.iaras.org/iaras/journals/ijme
Kamat PV, Sugandhi R, Kumar S. “Deep learning-based anomaly-onset aware remaining useful life estimationof bearings.” PeerJ Comput. Sci. 2021;7:e795. https://doi.org/10.7717/peerj-cs.795
Tanuska P, Spendla L, Kebisek M, Duris R, Stremy M. “Smart anomaly detection and prediction for assembly process maintenance in compliance with industry 4.0”. Sensors. 2021; 21(7):2376. https://doi.org/10.3390/s21072376
Elahi M, Afolaranmi SO, Martinez Lastra JL, et al. A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment. Discov Artif Intell. 2023;3:43. https://doi.org/10.1007/s44163-023-00089-x.
Moleda M, Momot A, Mrozek D. Predictive maintenance of boiler feed water pumps using SCADA data. Sensors. 2020;20(2):E571. https://doi.org/10.3390/s20020571.
Aguilera JJ, Meesenburg W, Ommen T, Markussen WB, Poulsen JL, Zühlsdorf B, Elmegaard B. “A review of common faults in large-scale heat pumps. Renew Sustain Energy Rev, Elsevier 2022. https://doi.org/10.1016/j.rser.2022.112826
Abdallah M, Joung B-G, Lee WJ, Mousoulis C, Raghunathan N, Shakouri A, Sutherland JW, Bagchi S. Anomaly detection and inter-sensor transfer learning on smart manufacturing datasets. Sensors. 2023;23:486. https://doi.org/10.3390/s23010486.
Kamat P, Sugandhi R. Anomaly detection for predictive maintenance in industry 4.0- A survey. E3S Web of Conferences 2020;170: 0 https://doi.org/10.1051/e3sconf/202017002007
Ur RK, Mian Z, Yasir A et al. Implementation of reliability centered maintenance (RCM) in the background of industry 4.0 –Issues, Challenges, and Opportunities. 21 June 2023, https://doi.org/10.21203/rs.3.rs-3053231/v1
Acknowledgements
The author would like to thank Acharya Institute of Technology, Bangalore, Karnataka, India and MS Ramaiah Institute of Technology, Bangalore, Karnataka, India for their encouragement and support in carrying out this research work.
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Varalakshmi, B.D., Lingaraju, G.M. Enhancing Industrial Anomaly Detection with Auto Encoder-Based Temporal Convolutional Networks for Motor Fault Classification. SN COMPUT. SCI. 5, 1067 (2024). https://doi.org/10.1007/s42979-024-03425-9
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DOI: https://doi.org/10.1007/s42979-024-03425-9