Abstract
The existing knowledge regarding the interfacial forces, lubrication, and wear of bearings in real-world operation has significantly improved their designs over time, allowing for prolonged service life. As a result, self-lubricating bearings have become a viable alternative to traditional bearing designs in industrial machines. However, wear mechanisms are still inevitable and occur progressively in self-lubricating bearings, as characterized by the loss of the lubrication film and seizure. Therefore, monitoring the stages of the wear states in these components will help to impart the necessary countermeasures to reduce the machine maintenance downtime. This article proposes a methodology for using a long short-term memory (LSTM)-based encoder—decoder architecture on interfacial force signatures to detect abnormal regimes, aiming to provide early predictions of failure in self-lubricating sliding contacts even before they occur. Reciprocating sliding experiments were performed using a self-lubricating bronze bushing and steel shaft journal in a custom-built transversally oscillating tribometer setup. The force signatures corresponding to each cycle of the reciprocating sliding motion in the normal regime were used as inputs to train the encoder—decoder architecture, so as to reconstruct any new signal of the normal regime with the minimum error. With this semi-supervised training exercise, the force signatures corresponding to the abnormal regime could be differentiated from the normal regime, as their reconstruction errors would be very high. During the validation procedure for the proposed LSTM-based encoder—decoder model, the model predicted the force signals corresponding to the normal and abnormal regimes with an accuracy of 97%. In addition, a visualization of the reconstruction error across the entire force signature showed noticeable patterns in the reconstruction error when temporally decoded before the actual critical failure point, making it possible to be used for early predictions of failure.
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Acknowledgements
This work was funded by the Austrian COMET Program (project InTribology, No. 872176) via the Austrian Research Promotion Agency (FFG) and the Provinces of Niederösterreich and Vorarlberg, and has been carried out within the Austrian Excellence Centre of Tribology (AC2T research GmbH). The authors would like to thank Christoph Haslehner for performing the experiments and Matthias Freisinger for microscopic analysis of the bearings.
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Vigneashwara PANDIYAN. He is currently a postdoctoral researcher at the Laboratory for Advanced Materials Processing (LAMP), ETH Swiss Federal Laboratories for Materials Science and Technology (Empa, Switzerland). He completed his M.S. and Ph.D. from Nanyang Technological University (NTU), Singapore, under Rolls-Royce@NTU corporate lab. Prior to joining Empa, he was a research scientist in A*Star-Agency for Science, Technology and Research, Singapore. He now concentrates on implementing machine learning models for in-situ monitoring of manufacturing processes for anomaly detection based on sensor signatures.
Mehdi AKEDDAR. He is currently a master student at RELab at ETHZ on unsupervised home therapy for post stroke patients. He received a B.S. degree in microengineering from EPFL. He completed a master in medical robotics at École polytechnique fédérale de Lausanne (EPFL). He did a year-long visit to McGill, Montreal, Canada, in 2019 for the end of his B.S. degree. He completed an internship at Empa in the domain of machine learning applied to time series data from tribology.
Josef PROST. He received his Ph.D. in physics from Vienna University of Technology, Austria, in 2018. He is currently working as a postdoctoral researcher at the Austrian Excellence Centre for Tribology (AC2T research GmbH) in Wiener Neustadt, Austria. His main research interest is the application of advanced data analysis and visualisation methods to tribological research questions, including the detection of anomalous operation states and impending failures using machine learning models.
Georg VORLAUFER. He is currently a principal scientist at AC2T research GmbH. He completed his M.S. in physics at the TU Wien, Austria, in 1998 and received his Ph.D. degree in physics in 2002 from the same institution. Between 1998 and 2001, he carried out his Ph.D. studies in the field of vacuum and surface science at CERN (Geneva, Switzerland). He has more than 18 years of experience in the field of tribology. Although for many years his research interests have been mainly in the field of physics-based modelling and simulation of tribological systems, he is currently concentrating on tribology-related aspects of data science, machine learning, and artificial intelligence.
Markus VARGA. He is currently leading the strategic research area of Synaptic Tribology at AC2T research GmbH. He received his M.S. at the University of Applied Science Wiener Neustadt, Austria, in mechatronics and completed his Ph.D. in tribology at the Montanuniversität Leoben, Austria. For more than ten years, his main research field is the optimisation of industrial maintenance by tribological measures, i.e., wear protection, sensors for early detection of failures, etc.
Kilian WASMER. He received the B.S. degree in mechanical engineering from the Applied University, Sion, Switzerland and Applied University, Paderborn, Germany, in 1999. He received his Ph.D. degree in mechanical engineering from Imperial College London, UK, in 2003. His current position is deputy laboratory head of LAMP at Empa as well as a lecturer at EPFL.
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Pandiyan, V., Akeddar, M., Prost, J. et al. Long short-term memory based semi-supervised encoder—decoder for early prediction of failures in self-lubricating bearings. Friction 11, 109–124 (2023). https://doi.org/10.1007/s40544-021-0584-3
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DOI: https://doi.org/10.1007/s40544-021-0584-3