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

作     者：Vigneashwara PANDIYAN Josef PROST Georg VORLAUFER Markus VARGA Kilian WASMER Vigneashwara PANDIYAN;Josef PROST;Georg VORLAUFER;Markus VARGA;Kilian WASMER

作者机构：Laboratory for Advanced Materials Processing(LAMP)Swiss Federal Laboratories for Materials Science and Technology(Empa)Thun CH-3602Switzerland AC2T research GmbHViktor-Kaplan-Straße 2/CWiener Neustadt 2700Austria 

出 版 物：《Friction》 (摩擦（英文版）)

年 卷 期：2022年第10卷第4期

页      面：583-596页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081104[工学-模式识别与智能系统] 080203[工学-机械设计及理论] 0802[工学-机械工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was funded by the Austrian COMET Program(project InTribology,No.872176)via the Austrian Research Promotion Agency(FFG) the Provinces of Niederösterreich and Vorarlberg and has been carried out within the Austrian Excellence Centre of Tribology(AC2T Research GmbH) Experiments were carried out within the framework of a project funded by the government of Lower Austria(No.K3-F-760/001-2017) 

主　　题：surface integrity acoustic emission auto encoders condition monitoring wear prediction 

摘      要：Functional surfaces in relative contact and motion are prone to wear and tear,resulting in loss of efficiency and performance of the workpieces/*** occurs in the form of adhesion,abrasion,scuffing,galling,and scoring between ***,the rate of the wear phenomenon depends primarily on the physical properties and the surrounding *** 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)*** this technique is better than offline inspection,it possesses inherent disadvantages for training the ML ***,supervised training of ML models requires the datasets considered for the classification to be of equal weightage to avoid *** 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 ***,classification models would not classify new wear phenomena from the normal regime if they are *** a promising alternative,in this work,we propose a methodology able to differentiate the abnormal regimes,i.e.,wear phenomenon regimes,from the normal *** 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 *** a result,the ML algorithms would be able to detect whether some overlaps exist with the learnt distributions when a new,unseen signal *** achieve this goal,a generative convolutional neural network(CNN)architecture based on variational auto encoder(VAE)is built and *** the validation procedure of the proposed CNN architectures,we were