Brake fault diagnosis using Clonal Selection Classification Algorithm (CSCA) – A statistical learning approach

Abstract

a b s t r a c tIn automobile, brake system is an essential part responsible for control of the vehicle. Any failure in the brake system impacts the vehicle's motion. It will generate frequent catastrophic effects on the vehicle cum passenger's safety. Thus the brake system plays a vital role in an automobile and hence condition monitoring of the brake system is essential. Vibration based condition monitoring using machine learning techniques are gaining momentum. This study is one such attempt to perform the condition monitoring of a hydraulic brake system through vibration analysis. In this research, the performance of a Clonal Selection Classification Algorithm (CSCA) for brake fault diagnosis has been reported. A hydraulic brake system test rig was fabricated. Under good and faulty conditions of a brake system, the vibration signals were acquired using a piezoelectric transducer. The statistical parameters were extracted from the vibration signal. The best feature set was identified for classification using attribute evaluator. The selected features were then classified using CSCA. The classification accuracy of such artificial intelligence technique has been compared with other machine learning approaches and discussed. The Clonal Selection Classification Algorithm performs better and gives the maximum classification accuracy (96%) for the fault diagnosis of a hydraulic brake system.

Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach

“…In Jegadeeshwaran and Sugumaran, 16 the authors have discussed about brake fault diagnosis using clonal selection principle. The fault diagnosis is vibration based, and they have simulated nine fault conditions in a brake system.…”

Experimental analysis in the test rig to detect temperature at the surface disc brake rotor using rubbing thermocouple

“…A brake failure diagnosis approach with statistics has been developed by several researchers [7], but most have not yet applied it in a brake failure detection device. The paper [8] show the vibration-based fault diagnosis of the automobile hydraulic brake system using an artificial intelligence technique called CSC algorithm. This test was successfully carried out in a test rig but it might not function optimally when applied under actual conditions because the vibration noise from outside such as tire and road vibration is great.…”

Methodologies of Knowledge Discovery from Data and Data Mining Methods in Mechanical Engineering

“…They used rough set theory to generate the rules from the vibration signals Muralidharan V. et al 2012 [71] A vibration based condition monitoring system was introduced. Wavelet analysis for feature extraction was used and Naive Bayes algorithm and Bayes nets were compared for fault diagnosis of monoblock centrifugal pump Jegadeeshwaran R. et al 2015 [72] In this study the authors introduce a Clonal Selection Classification Algorithm (CSCA) for condition monitoring of a hydraulic brake system. through vibration analysis.…”