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Vibration based Brake Fault Diagnosis using Voting Feature Interval and Decision Tree with Histogram Features
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.
Decision Tree, Histogram Features, Machine Learning, Vibration Signals, Voting Feature Interval
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