The requirement of fault diagnosis in the field of automobiles is growing higher day by day.The reliability of human resources for the fault diagnosis is uncertain.Brakes are one of the major critical components in au...The requirement of fault diagnosis in the field of automobiles is growing higher day by day.The reliability of human resources for the fault diagnosis is uncertain.Brakes are one of the major critical components in automobiles that require closer and active observation.This research work demonstrates a fault diagnosis technique for monitoring the hydraulic brake system using vibration analysis.Vibration signals of a rotating element contain dynamic information about its health condition.Hence,the vibration signals were used for the brake fault diagnosis study.The study was carried out on a brake fault diagnosis experimental setup.The vibration signals under different fault conditions were acquired from the setup using an accelerometer.The condition monitoring of the hydraulic brake system using the vibration signal was processed using a machine learning approach.The machine learning approach has three phases,namely,feature extraction,feature selection,and feature classification.Histogram features were extracted from the vibration signals.The prominent features were selected using the decision tree.The selected features were classified using a fuzzy classifier.The histogram features and the fuzzy classifier combination produced maximum classification accuracy than that of the statistical features.展开更多
文摘The requirement of fault diagnosis in the field of automobiles is growing higher day by day.The reliability of human resources for the fault diagnosis is uncertain.Brakes are one of the major critical components in automobiles that require closer and active observation.This research work demonstrates a fault diagnosis technique for monitoring the hydraulic brake system using vibration analysis.Vibration signals of a rotating element contain dynamic information about its health condition.Hence,the vibration signals were used for the brake fault diagnosis study.The study was carried out on a brake fault diagnosis experimental setup.The vibration signals under different fault conditions were acquired from the setup using an accelerometer.The condition monitoring of the hydraulic brake system using the vibration signal was processed using a machine learning approach.The machine learning approach has three phases,namely,feature extraction,feature selection,and feature classification.Histogram features were extracted from the vibration signals.The prominent features were selected using the decision tree.The selected features were classified using a fuzzy classifier.The histogram features and the fuzzy classifier combination produced maximum classification accuracy than that of the statistical features.
