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

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.

Identification of abnormal tribological regimes using a microphone and semi-supervised machine-learning algorithm

Functional surfaces in relative contact and motion are prone to wear and tear,resulting in loss of efficiency and performance of the workpieces/machines.Wear occurs in the form of adhesion,abrasion,scuffing,galling,and scoring between contacts.However,the rate of the wear phenomenon depends primarily on the physical properties and the surrounding environment.Monitoring the integrity of surfaces by offline inspections leads to significant wasted machine time.A potential alternate option to offline inspection currently practiced in industries is the analysis of sensors signatures capable of capturing the wear state and correlating it with the wear phenomenon,followed by in situ classification using a state-of-the-art machine learning(ML)algorithm.Though this technique is better than offline inspection,it possesses inherent disadvantages for training the ML models.Ideally,supervised training of ML models requires the datasets considered for the classification to be of equal weightage to avoid biasing.The collection of such a dataset is very cumbersome and expensive in practice,as in real industrial applications,the malfunction period is minimal compared to normal operation.Furthermore,classification models would not classify new wear phenomena from the normal regime if they are unfamiliar.As a promising alternative,in this work,we propose a methodology able to differentiate the abnormal regimes,i.e.,wear phenomenon regimes,from the normal regime.This is carried out by familiarizing the ML algorithms only with the distribution of the acoustic emission(AE)signals captured using a microphone related to the normal regime.As a result,the ML algorithms would be able to detect whether some overlaps exist with the learnt distributions when a new,unseen signal arrives.To achieve this goal,a generative convolutional neural network(CNN)architecture based on variational auto encoder(VAE)is built and trained.During the validation procedure of the proposed CNN architectures,we were capable of identifying acoustics signals corresponding to the normal and abnormal wear regime with an accuracy of 97%and 80%.Hence,our approach shows very promising results for in situ and real-time condition monitoring or even wear prediction in tribological applications.