Vibration Based Condition Monitoring of a Hydraulic Brake System through Statistical Learning Approaches: A Review

R. Jegadeeshwaran, V. Sugumaran
2016 Indian Journal of Science and Technology  
Background/Objectives: To study the recent development for monitoring the condition of a hydraulic brake system using statistical learning approaches. Methods/Statistical Analysis: Machine fault diagnosis is one of the condition monitoring approaches used to monitor the condition of machinery. For brake fault diagnosis, many conventional techniques have been reported in literature. In recent days, statistical learning approaches like, naïve bayes, decision tree, bayes net, best first tree,
more » ... st first tree, support vector machines, K Star have been successfully used for the fault diagnosis study. Findings: Keeping in mind the end goal to distinguish the most plausible deficiencies prompting to disappointment, numerous strategies in particular, like thermal image mapping, oil particle analysis, acoustic emission signal analysis, vibration analysis have been used for analyzing the data. Among these, vibration signal has been conveniently used for many fault diagnosis study. The same vibration signal can be used for the brake fault diagnosis study. Then these vibration data are processed using shortterm Fourier transform, high-resolution spectral analysis, waveform analysis, wavelet analysis, wavelet transform, etc. The results of such analysis are used to analyze the causes of failures. Recent advancement is the application of statistical approach for analyzing the data. This study presents a brief review about the possibilities for implementing the recent statistical learning approaches for monitoring the condition of the brake system. Application/Improvements: Number of new statistical learning approaches like nested dichotomy, clonal selection classification algorithm, Artificial Immune Recognition System (AIRS) algorithm can be used for the brake fault diagnosis study.
doi:10.17485/ijst/2016/v9i48/107932 fatcat:2ifxxyxczvfjnpnq3klfolrk34