scholarly journals Application of Meta family Classifiers for monitoring hydraulic brake system using vibration based statistical learning approach

2021 ◽  
Vol 1969 (1) ◽  
pp. 012050
Author(s):  
Mohit Nikhil Wagh ◽  
T M Alamelu Manghai ◽  
R Jegadeeshwaran ◽  
D Saravanakumar ◽  
N Raghukiran
Keyword(s):  
Statistical Learning ◽  
Brake System ◽  
Learning Approach ◽  
Hydraulic Brake

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.

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 A statistical learning approach for stock selection in the Chinese stock market

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Wenbo Wu ◽  
Jiaqi Chen ◽  
Liang Xu ◽  
Qingyun He ◽  
Michael L. Tindall
Keyword(s):  
Stock Market ◽  
Statistical Learning ◽  
Learning Approach ◽  
Chinese Stock Market ◽  
Stock Selection

Autonomous Emergency Braking Control Based on Hierarchical Strategy Using Integrated-Electro-Hydraulic Brake System

2017 ◽  
Author(s):  
Xiangkun He ◽  
Xuewu Ji ◽  
Kaiming Yang ◽  
Yulong Liu ◽  
Jian WU ◽  
...  
Keyword(s):  
Brake System ◽  
Emergency Braking ◽  
Hydraulic Brake ◽  
Braking Control

Optimization of Magnetorheological Brake

2011 ◽  
Author(s):  
Snegdha Gupta ◽  
Harish Hirani
Keyword(s):  
Minimum Weight ◽  
Hybrid Genetic Algorithm ◽  
Brake System ◽  
Outer Diameter ◽  
Current Accounting ◽  
Hydraulic Brake ◽  
Rate Dependent ◽  
Design Variables ◽  
Braking Torque ◽  
Gradient Based

Quick response and rheological properties as a function of magnetic field are well known features of MR fluids which inspire their usage as brake materials. Controllable torque and minimum weight of brake system are the deciding functions based on which the viability of the MR brake against the conventional hydraulic brake system can be judged. The aim of this study is to optimize a multi-disk magneto-rheological brake system considering torque and weight as objective functions and geometric dimensions of conventional hydraulic brake as constraints. The electric current accounting magnetic saturation, MR gap, number of disk, thickness of disk, and outer diameter of disk have been considered as design variables. To model the behavior of MR Fluid, Bingham and Herschel Bulkley models have been compared. To implement these models in estimating the braking torque a modification in shear rate dependent component has been proposed. The overall design of MR brake has been optimized using a hybrid (Genetic algorithm plus gradient based) optimization scheme of MATLAB software.

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