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

查看全文 作　　者：Vigneashwara [1]PANDIYAN;Mehdi [1]AKEDDAR;Josef [2]PROST;Georg [2]VORLAUFER;Markus [2]VARGA;Kilian [1]WASMER

机构地区：[1]Laboratory for Advanced Materials Processing(LAMP),Swiss Federal Laboratories for Materials Science and Technology(Empa),Thun CH-3602,Switzerland;[2]AC2T research GmbH,Wiener Neustadt 2700,Austria

出　　处：《Friction》索引2023年第11卷第1期,共16页

基　　金：This work was funded by the Austrian COMET Program(project InTribology,No.872176)via the Austrian Research Promotion Agency(FFG)and the Provinces of Niederosterreich and Vorarlberg,and has been carried out within the Austrian Excellence Centre of Tribology(AC2T research GmbH). 摘　　要：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. 关键词：predictive maintenance in-situ sensing long short-term memory(LSTM) encoder-decoder wear monitoring TRIBOLOGY

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

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