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Research Article | Open Access

Long short-term memory based semi-supervised encoder– decoder for early prediction of failures in self-lubricating bearings

Vigneashwara PANDIYAN1( ), Mehdi AKEDDAR1, Josef PROST2, Georg VORLAUFER2, Markus VARGA2, Kilian WASMER1
Laboratory for Advanced Materials Processing (LAMP), Swiss Federal Laboratories for Materials Science and Technology (Empa), Thun CH-3602, Switzerland
AC2T research GmbH, Wiener Neustadt 2700, Austria
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Graphical Abstract

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.

Keywords

predictive maintenancein-situ sensinglong short-term memory (LSTM)encoder–decoderwear monitoringtribology

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Friction
Volume 11 Issue 1,
January 2023
Pages 109-124
DOI: 10.1007/s40544-021-0584-3
Cite this article:
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, 2023, 11(1): 109-124. https://doi.org/10.1007/s40544-021-0584-3

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Received: 27 September 2021
Revised: 09 November 2021
Accepted: 04 December 2021
Published: 28 April 2022
© The author(s) 2021.

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