Bearing fault classification using decision trees and neural networks

Abstract

In this study, we test three machine learning methodologies − binary tree, k-nearest neighbors (k-NN), and neural networks (NN) − using a range of hyperparameters. These methods are applied to a dataset consisting of extracted time series characteristics (root mean square (RMS), skewness, and kurtosis from vibration signals of various bearings subjected to different fault conditions from the intelligent maintenance systems (IMS) dataset. We evaluate how effectively these methods classify the condition of the bearings using the provided dataset. We observe the top two methods, artificial neural network (ANN) 99.29% and binary tree 98.84%. With a difference of 0.45%, the binary tree is preferred over the complex ANN due to its ease of interpretation, transparency, and minimal computation requirements. Its integration as code in embedded controllers or electronic control units (ECUs) is more efficient, which makes them faster for real-time processing and safety-critical electric vehicle (EV) systems.