Brake fault diagnosis using a voting ensemble of machine learning classifiers

Abstract

Brake fault diagnosis is a crucial aspect of enhancing 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 detection by analyzing vibration signals. Vibration signals for normal and faulty conditions were captured using a hydraulic brake test setup equipped with a piezoelectric transducer and data acquisition system. Feature extraction was performed using an auto-regressive moving average (ARMA) model. The performance of five different classifiers namely, random forest (RF), Naive Bayes (NB), instance-based k-nearest neighbours (IBk), logistic model trees (LMT) and J48 decision tree was evaluated. The LMT classifier 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 accuracy, a voting-based ensemble approach was employed by combining two, three, four and five classifiers with the application of five different voting strategies. The results obtained showcase that a combination of three classifiers LMT, IBk and NB utilizing the majority voting rule yielded an enhanced classification accuracy of 98.00 % highlighting the effectiveness of this ensemble method in brake fault diagnosis. A similar classification accuracy of 98.00 % was achieved by four (LMT-IBk-RF-J48) and five (IBk-RF-MLP-J48-NB) classifier ensembles; however, considering the computational complexity three classifier ensembles were selected. Whilst two classifier ensembles (IBk-J48) achieved a maximum classification accuracy of 96.00 %.