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Vibration-Based Fault Diagnosis Study on a Hydraulic Brake System Using Fuzzy Logic with Histogram Features

Structural Durability & Health Monitoring 2022年第4期16卷 383-396页

作者： alamelu manghai t.marimuthu Jegadeeshwaran Rakkiyannan Lakshmipathi Jakkamputi Sugumaran Vaithiyanathan Sakthivel Gnanasekaran School of Mechanical Engineering Vellore Institute of TechnologyChennai600127India Centre for Automation School of Mechanical EngineeringVellore Institute of TechnologyChennai600127India

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

关键词： Machine learning histogram features decision tree fuzzy logic membership function confusion matrix

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Feature-Based Vibration Monitoring of a Hydraulic Brake System Using Machine Learning

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Structural Durability & Health Monitoring 2017年第2期11卷 149-167页

作者： t.M.alamelu manghai R.Jegadeeshwaran School of Mechanical and Building Sciences VIT University Chennai CampusChennaiTamil NaduIndia-600127

Hydraulic brakes in automobiles are an important control component used not only for the safety of the passenger but also for others moving on the ***,monitoring the condition of the brake components is *** brake elements can be monitored by studying the vibration characteristics obtained from the brake system using a proper signal processing technique through machine learning *** vibration signals were captured using an accelerometer sensor under a various fault *** acquired vibration signals were processed for extracting meaningful information as *** condition of the brake system can be predicted using a feature based machine learning approach through the extracted *** study focuses on a mechatronics system for data acquisitions and a signal processing technique for extracting features such as statistical,histogram and *** results have been carried out using an experimental study for finding the effectiveness of the suggested signal processing techniques for monitoring the condition of the brake system.

关键词： Vibration signals statistical features histogram features wavelet decomposition machine learning decision tree

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Brake Fault Diagnosis through Machine Learning Approaches–A Review

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Structural Durability & Health Monitoring 2017年第1期11卷 41-61页

作者： t.M.alamelu manghai R.Jegadeeshwaran V.Sugumaran School of Mechanical and Building Sciences VIT University Chennai CampusChennaiTamil NaduIndia-600127

Diagnosis is the recognition of the nature and cause of a certain *** is generally used to determine cause and effect of a problem. Machine fault diagnosis isa field of finding faults arising in machines. to identify the most probable faults leadingto failure, many methods are used for data collection, including vibration monitoring,thermal imaging, oil particle analysis, etc. then these data are processed using methodslike spectral analysis, wavelet analysis, wavelet transform, short-term Fourier transform,high-resolution spectral analysis, waveform analysis, etc. the results of this analysis areused in a root cause failure analysis in order to determine the original cause of the *** paper presents a brief review about one such application known as machine learningfor the brake fault diagnosis problems.

关键词： Vibration analysis machine learning feature extraction feature selection feature classification brake fault diagnosis

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Vibration Based tool Insert Health Monitoring Using Decision tree and Fuzzy Logic

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Structural Durability & Health Monitoring 2019年第3期13卷 303-316页

作者： Kundur Shantisagar R.Jegadeeshwaran G.Sakthivel t.M.alamelu manghai School of Mechanical and Building Sciences Vellore Institute of TechnologyChennaiTamil NaduIndia

the productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting *** research incorporates condition monitoring of a non-carbide tool insert using vibration analysis along with machine learning and fuzzy logic approach.A non-carbide tool insert is considered for the process of cutting operation in a semi-automatic lathe,where the condition of tool is monitored using vibration *** vibration signals for conditions such as heathy,damaged,thermal and flank were acquired with the help of piezoelectric transducer and data acquisition *** descriptive statistical features were extracted from the acquired vibration signal using the feature extraction *** extracted statistical features were selected using a feature selection process through J48 decision tree *** selected features were classified using J48 decision tree and fuzzy to develop the fault diagnosis model for the improved predictive *** decision tree model produced the classification accuracy as 94.78%with five selected *** developed fuzzy model produced the classification accuracy as 94.02%with five membership ***,the decision tree has been proposed as a suitable fault diagnosis model for predicting the tool insert health condition under different fault conditions.

关键词： Statistical features J48 decision tree algorithm confusion matrix fuzzy logic weka

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