Implementation of Machine Learning for Fault Classification on Vehicle Power Transmission System

This research presents the implementation of machine learning (ML) for fault classification and diagnosis on vehicle power transmission system (VPTS). Machine learning method can be used to classify their independent diagnostic components for each fault characteristic states. Under the internet of vehicle (IoV) demands, early prediction system is necessary to notify the drivers or clouding services how the vehicle maintenance and the driving safety degree. The acoustic sensors can be carried out to realize a real-time diagnostic system for automobile engine and chassis transmission system. This method is to acquire the dynamics acoustic signals of the vehicle through the data acquisition device (DAQ). These acoustic features is firstly filtered by Mel-scale frequency cepstral coefficient (MFCC) to determine the each characteristic states of the vehicle engine and the chassis parts. Next, support vector machine (SVM), multilayer perceptron (MLP), deep neural networks (DNN),principal component analysis (PCA), ${k}$ -nearest neighbor (${k}$ -NN), and decision tree (DT) several classifier algorithms are applied to implement the feature classification of fault causes for stability and higher accuracy of VPTS. And dimension reduction model is compared and applied in proposed ML algorithms by an PCA algorithm. All training model datasets are carried out in Matlab and Python pytorch platform by using Nvidia graphics processing unit (GPU) processors, they are evaluated and discussed. The effectiveness on the filtered feature database in the experiments is classified by means of this research proposed schemes. The expected experimental results of the classification and identification with respect to different fifteen VPTS conditions are obtained and inferred.