首页|Study Findings on Machine Learning Detailed by Researchers at Vellore Institute of Technology (Brake fault diagnosis using a voting ensemble of machine learning classifiers)
Study Findings on Machine Learning Detailed by Researchers at Vellore Institute of Technology (Brake fault diagnosis using a voting ensemble of machine learning classifiers)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting out of Chennai, India, by N ewsRx editors, research stated, “Brake fault diagnosis is a crucial aspect of en hancing driving safety, as various faults such as air in brake fluid, brake oil spill, reservoir leak, mechanical fade and distinct types of brake pad wear can compromise vehicle safety. This study presents a method for timely fault detecti on by analyzing vibration signals.” The news reporters obtained a quote from the research from Vellore Institute of Technology: “Vibration signals for normal and faulty conditions were captured us ing a hydraulic brake test setup equipped with a piezoelectric transducer and da ta acquisition system. Feature extraction was performed using an autoregressive moving average (ARMA) model. The performance of five different classifiers name ly, random forest (RF), Naive Bayes (NB), instance-based k-nearest neighbours (I Bk), logistic model trees (LMT) and J48 decision tree was evaluated. The LMT cla ssifier achieved the highest accuracy at 95.00 % followed by IBk, RF, J48 and NB with accuracies of 92.00 %, 90.00 %, 90 .00 % and 87.00 %. To further improve the diagnosis a ccuracy, a voting-based ensemble approach was employed by combining two, three, four and five classifiers with the application of five different voting strategi es. The results obtained showcase that a combination of three classifiers LMT, I Bk and NB utilizing the majority voting rule yielded an enhanced classification accuracy of 98.00 % highlighting the effectiveness of this ensembl e method in brake fault diagnosis.”
Vellore Institute of TechnologyChennaiIndiaAsiaCyborgsEmerging TechnologiesMachine Learning
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