scholarly journals Vibration Based Condition Monitoring of a Hydraulic Brake System through Statistical Learning Approaches: A Review

2016 ◽  
Vol 9 (48) ◽  
Author(s):  
R. Jegadeeshwaran ◽  
V. Sugumaran
Keyword(s):  
Statistical Learning ◽  
Condition Monitoring ◽  
Brake System ◽  
Learning Approaches ◽  
Hydraulic Brake

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.

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

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

Comparison of statistical learning approaches for cerebral aneurysm rupture assessment

2019 ◽  
Vol 15 (1) ◽  
pp. 141-150 ◽  
Author(s):  
Felicitas J. Detmer ◽  
Daniel Lückehe ◽  
Fernando Mut ◽  
Martin Slawski ◽  
Sven Hirsch ◽  
...  
Keyword(s):  
Cerebral Aneurysm ◽  
Statistical Learning ◽  
Aneurysm Rupture ◽  
Learning Approaches

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.

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.

Symbolic and Statistical Learning Approaches to Speech Summarization: A Scoping Review

2021 ◽  
pp. 101305
Author(s):  
Dana Rezazadegan ◽  
Shlomo Berkovsky ◽  
Juan C. Quiroz ◽  
A. Baki Kocaballi ◽  
Ying Wang ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Scoping Review ◽  
Learning Approaches ◽  
Speech Summarization
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