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Brake Fault Diagnosis Through Machine Learning Approaches – A Review

Alamelu Manghai T.M.1, Jegadeeshwaran R2, Sugumaran V.3
School of Mechanical and Building Sciences, VIT University Chennai Campus, Chennai, Tamil Nadu, India – 600 127.
E-mail: alamelumangai.m@vit.ac.in; Phone: +91 44 39931335; Fax: +91 44 39932555
School of Mechanical and Building Sciences, VIT University Chennai Campus, Chennai, Tamil Nadu, India – 600 127.
E-mail: krjegadeeshwaran@gmail.com; Phone: +91 44 39931335; Fax: +91 44 39932555
School of Mechanical and Building Sciences, VIT University Chennai Campus, Chennai, Tamil Nadu, India – 600 127. Phone: +91 44 39931335; Fax: +91 44 39932555

Structural Durability & Health Monitoring 2017, 11(1), 43-67. https://doi.org/10.3970/sdhm.2017.012.043

Abstract

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.

Keywords

Vibration analysis, machine learning, feature extraction, feature selection, feature classification, Brake fault diagnosis.

Cite This Article

T.M., A. M., R, J., V., S. (2017). Brake Fault Diagnosis Through Machine Learning Approaches – A Review. Structural Durability & Health Monitoring, 11(1), 43–67.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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