S. Aravinth ¹, R. Jegadeeshwaran ¹, V. Sugumaran ¹

Affiliations
1 School of Mechanical and Building Sciences, VIT University, Chennai Campus, Chennai - 600127, Tamil Nadu, IN

Abstract

Objectives: The brake system is one of the major components used in automobiles which inhibits motion by absorbing energy from a moving system. So regular monitoring is essential in brake system which ensures not only vehicle safety but also human lives. Methods/Statistical Analysis: In this study, a vibration based fault diagnosis approach has been reported through machine learning approach. A hydraulic brake setup was fabricated and vibration signals under various fault conditions were extracted using accelerometer sensor with suitable frequency. These signals were compared with good range of signals and variation is analyzed through histogram feature extraction, selection and classification of machine learing scenario. Findings: Histogram features were extracted by separation of signals into different bin ranges among which bin with highest accuracy level is further processed through selection process of Decision Tree and 87.78% was the achieved accuracy in fault determination. In Voting Feature Interval (VFI) 85.64% was the accuracy attained in error identification. Application/Improvements: Since Decision Tree gives the better result in fault identification in brake fault diagnosis of this study, it can be further improved by varying the frequency ranges of signals, so each and every variation in signals are noted. Moreover improvement in accuracy level can also be achieved in future by increasing number of samples percondtion of brake system.

Keywords

Decision Tree, Histogram Features, Machine Learning, Vibration Signals, Voting Feature Interval

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R. Jegadeeshwaran ¹, V. Sugumaran ¹

Affiliations
1 School of Mechanical and Building Science, VIT University Chennai Campus, Chennai - 600127,Tamil Nadu, IN

Abstract

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, 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.

Keywords

Brake System, Condition Monitoring, Fault Diagnosis, Statistical Learning, Vibration Signal.

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