TY  - EJOU
AU  - T.M., Alamelu Manghai 
AU  - R, Jegadeeshwaran 
AU  - V., Sugumaran 

TI  - Brake Fault Diagnosis Through Machine Learning Approaches – A Review
T2  - Structural Durability \& Health Monitoring

PY  - 2017
VL  - 11
IS  - 1
SN  - 1930-2991

AB  - 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.
KW  - Vibration analysis
KW  -  machine learning
KW  -  feature extraction
KW  -  feature selection
KW  -  feature classification
KW  -  Brake fault diagnosis

DO  - 10.3970/sdhm.2017.012.043