{"title":"Piston Slap Condition Monitoring and Fault Diagnosis Using Machine\u0000 Learning Approach","authors":"Praveen Kochukrishnan, K. Rameshkumar, S. Srihari","doi":"10.4271/03-16-07-0051","DOIUrl":"https://doi.org/10.4271/03-16-07-0051","url":null,"abstract":"Various internal combustion (IC) engine condition monitoring techniques exist for\u0000 early fault detection and diagnosis to ensure smooth operation, increased\u0000 durability, low emissions, and prevent breakdowns. A fault, such as piston slap,\u0000 can damage critical components like the piston, piston rings, and cylinder liner\u0000 and is among those faults that may lead to such consequences. This research has\u0000 been conducted to monitor piston slap conditions by analyzing the engine\u0000 vibration and acoustic emission (AE) signals. An experimental setup has been\u0000 established for acquiring vibration and AE sensor signatures for various piston\u0000 slap severity conditions. Time-domain features are extracted from vibration and\u0000 AE sensor signatures, and among them, the best features are selected using\u0000 one-way analysis of variance (ANOVA) to create machine learning (ML) models.\u0000 Apart from individual sensor feature classification, the feature fusion method\u0000 increases the prediction accuracy. ML algorithms used in this study for building\u0000 the prediction models are classification and regression trees (CART), random\u0000 forest, and support vector machine (SVM). Performance comparisons of these\u0000 trained models are made using different performance measures. It is observed\u0000 that about 94.95% of maximum classification accuracy is obtained in predicting\u0000 the piston slap severity at different speeds and load conditions.","PeriodicalId":47948,"journal":{"name":"SAE International Journal of Engines","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76579210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}