Fuzzy classifier with automatic rule generation for fault diagnosis of hydraulic brake system using statistical features

2015 ◽  
Vol 1 (3) ◽  
pp. 333
Author(s):  
R. Jegadeeshwaran ◽  
V. Sugumaran
Keyword(s):  
Fault Diagnosis ◽  
Brake System ◽  
Rule Generation ◽  
Statistical Features ◽  
Fuzzy Classifier ◽  
Hydraulic Brake

Vibration based Fault Diagnosis of Automobile Hydraulic Brake System using Fuzzy Logic with Best First Tree Rules

2017 ◽  
Vol 8 (4) ◽  
Author(s):  
M.N. Gajre ◽  
R. Jegadeeshwaran ◽  
V. Sugumaran ◽  
A. Talbar
Keyword(s):  
Fuzzy Logic ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Brake System ◽  
Learning Approach ◽  
Fuzzy Classifier ◽  
Braking System ◽  
Hydraulic Brake ◽  
Machine Learning Approach

Brakes are indispensable element of automobile. It takes significant factor to slow down or stop vehicle at an instant which will help to prevent an incident or accident in panic scenario. In appropriate braking or breakdown in braking system may direct devastating effect on automobile as well as traveller safety. To enhance potential of braking system condition monitoring is drastic demand in automotive field. This research predominantly concentrates towards fault diagnosis of a hydraulic brake system with the principle of vibration signal. Feature extraction, feature selection and feature classification are the key measures under machine learning approach. Feature extraction can certainly accomplished by acquiring vibration from the system. Statistical features were for the fault diagnosis of hydraulic brake system. Best first tree algorithm will pick most effective features that will differentiate the fault conditions of the brake through given train samples. Fuzzy logic was selected as a classifier. In the present study, fuzzy classifier with the best first tree rules was used to perform the classification accuracy.

scholarly journals Application of FURIA for Finding the Faults in a Hydraulic Brake System Using a Vibration Analysis through a Machine Learning Approach

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Alamelu Manghai T. M ◽  
Jegadeeshwaran R
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Continuous Monitoring ◽  
Selection Process ◽  
Brake System ◽  
Learning Approach ◽  
Vibration Signals ◽  
Hydraulic Brake ◽  
Machine Learning Approach

Vibration-based continuous monitoring system for fault diagnosis of automobile hydraulic brake system is presented in this study. This study uses a machine learning approach for the fault diagnosis study. A hydraulic brake system test rig was fabricated. The vibration signals were acquired from the brake system under different simulated fault conditions using a piezoelectric transducer. The histogram features were extracted from the acquired vibration signals. The feature selection process was carried out using a decision tree. The selected features were classified using fuzzy unordered rule induction algorithm ( FURIA ) and Repeated Incremental Pruning to Produce Error Reduction ( RIPPER ) algorithm. The classification results of both algorithms for fault diagnosis of a hydraulic brake system were presented. Compared to RIPPER and J48 decision tree, the FURIA performs better and produced 98.73 % as the classification accuracy.

scholarly journals Health Monitoring of a Hydraulic Brake System Using Nested Dichotomy Classifier – A Machine Learning approach

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
R. Jegadeeshwaran ◽  
V. Sugumaran
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Forest Tree ◽  
Brake System ◽  
Learning Approaches ◽  
Statistical Features ◽  
Vibration Signals ◽  
Hydraulic Brake ◽  
Random Forest Tree ◽  
On The Road

Hydraulic brakes in automobiles play a vital role for the safety on the road; therefore vital components in the brake system should be monitored through condition monitoring techniques. Condition monitoring of brake components can be carried out by using the vibration characteristics. The vibration signals for the different fault conditions of the brake were acquired from the fabricated hydraulic brake test setup using a piezoelectric accelerometer and a data acquisition system. Condition monitoring of brakes was studied using machine learning approaches. Through a feature extraction technique, descriptive statistical features were extracted from the acquired vibration signals. Feature classification was carried out using nested dichotomy, data near balanced nested dichotomy and class balanced nested dichotomy classifiers. A Random forest tree algorithm was used as a base classifier for the nested dichotomy (ND) classifiers. The effectiveness of the suggested techniques was studied and compared. Amongst them, class balanced nested dichotomy (CBND) with the statistical features gives better accuracy of 98.91% for the problem concerned.

Vibration based brake health monitoring using wavelet features: A machine learning approach

2019 ◽  
Vol 25 (18) ◽  
pp. 2534-2550 ◽  
Author(s):  
T. M. Alamelu Manghai ◽  
R. Jegadeeshwaran
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Motor Vehicle ◽  
Brake System ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Vibration Signals ◽  
Hydraulic Brake ◽  
Wavelet Features ◽  
Effective Performance

In this study, the application of wavelets has been investigated for diagnosing the faults on a hydraulic brake system of a light motor vehicle using the vibration signals acquired from a brake test setup through a piezoelectric type accelerometer. An efficient brake system should provide reliable and effective performance in order to ensure safety . If it is not properly monitored, it may lead to a serious catastrophic effect such as accidents, frequent breakdown, etc. Hence, the brake system needs to be monitored continuously. The condition of the brake components and the vibration signals are interrelated. If the failure starts progressing, the vibration magnitude will also progress. Analyzing the vibration signals under the various fault conditions is the key process in fault diagnosis. In recent decades wavelets have been focused on in many fault diagnosis studies as the wavelets decompose the complex information into simple form with high precision for further analysis. The wavelet features were extracted in order to retrieve the information from the vibration signals using discrete wavelet transform. From that discretized signal under each fault condition, the relevant features were extracted and feature selection was carried out. The selected features were then classified using a set of machine learning classifiers such as best first tree (pre-pruning, post-pruning, and unpruned), Hoeffding tree (HT), support vector machine, and neural network. The classification accuracies of all the algorithms were compared and discussed. Among the considered classifier model, the HT model produced a better classification accuracy as 99.45% for the hydraulic brake fault diagnosis.

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