Brake Fault Diagnosis Through Machine Learning Approaches–A Review

• 作者: R.Jegadeeshwaran T.M.Alamelu Manghai V.Sugumaran
• 来源期刊: 结构耐久性与健康监测(英文)
• 学科: 地球科学
• 关键词: VIBRATION analysis MACHINE learning FEATURE extraction FEATURE selection FEATURE classification BRAKE FAULT diagnosis
• 年，卷（期）: 2017,（1）
• 页码范围: 41-61
• 页数: 21页
• 分类号: P31

摘要

Diagnosis is the recognition of the nature and cause of a certain phenomenon.It is generally used to determine cause and effect of a problem. Machine fault diagnosis is a field of finding faults arising in machines. To identify the most probable faults leading to failure, many methods are used for data collection, including vibration monitoring,thermal imaging, oil particle analysis, etc. Then these data are processed using methods like spectral analysis, wavelet analysis, wavelet transform, short-term Fourier transform,high-resolution spectral analysis, waveform analysis, etc. The results of this analysis are used in a root cause failure analysis in order to determine the original cause of the fault.This paper presents a brief review about one such application known as machine learning for the brake fault diagnosis problems.