基于机器学习方法的活塞拍打状态监测与故障诊断

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Accounts of Chemical Research Pub Date : 2023-03-11 DOI:10.4271/03-16-07-0051
Praveen Kochukrishnan, K. Rameshkumar, S. Srihari
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引用次数: 0

摘要

各种内燃机状态监测技术可用于早期故障检测和诊断,以确保平稳运行、提高耐用性、降低排放并防止故障发生。一个故障,如活塞拍打,会损坏关键部件,如活塞、活塞环和气缸套,是可能导致这种后果的故障之一。通过对发动机振动和声发射信号的分析,对活塞拍击工况进行了监测。建立了一个实验装置,用于获取各种活塞拍打严重程度条件下的振动和声发射传感器特征。从振动和声发射传感器特征中提取时域特征,并利用单因素方差分析(ANOVA)选择最佳特征,建立机器学习(ML)模型。除了单独的传感器特征分类,特征融合方法提高了预测精度。本研究中用于构建预测模型的机器学习算法有分类回归树(CART)、随机森林和支持向量机(SVM)。使用不同的性能度量对这些训练过的模型进行性能比较。结果表明,在不同转速和载荷条件下,对活塞拍打强度的预测准确率达到了最高分类准确率的94.95%。
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Piston Slap Condition Monitoring and Fault Diagnosis Using Machine Learning Approach
Various internal combustion (IC) engine condition monitoring techniques exist for early fault detection and diagnosis to ensure smooth operation, increased durability, low emissions, and prevent breakdowns. A fault, such as piston slap, can damage critical components like the piston, piston rings, and cylinder liner and is among those faults that may lead to such consequences. This research has been conducted to monitor piston slap conditions by analyzing the engine vibration and acoustic emission (AE) signals. An experimental setup has been established for acquiring vibration and AE sensor signatures for various piston slap severity conditions. Time-domain features are extracted from vibration and AE sensor signatures, and among them, the best features are selected using one-way analysis of variance (ANOVA) to create machine learning (ML) models. Apart from individual sensor feature classification, the feature fusion method increases the prediction accuracy. ML algorithms used in this study for building the prediction models are classification and regression trees (CART), random forest, and support vector machine (SVM). Performance comparisons of these trained models are made using different performance measures. It is observed that about 94.95% of maximum classification accuracy is obtained in predicting the piston slap severity at different speeds and load conditions.
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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