Chassis predictive maintenance and service solutions

Abstract: Predictive Maintenance (PdM) accumulates data from multiple sensors developing a statistical model which identifies the key failures even before they take place. The main focus of this thesis work has been the proposal of a machine learning based system designed for predicting the failure of mechanical parts that require replacement. The main investigation explores the possibilities of implementing machine learning algorithm for predicting the parts that require replacement and which is found from the electronic errors that the vehicle exhibits. A strong association between the parts that cause faults and electronic error codes helps in yielding a powerful diagnostics tool. The study has considered three error components namely; broken damper, noisy wheel hub and the reference value for the validation purpose. The model vehicle used for the study is Volvo V90. To acquire variance in this study data, diverse tracks with different speeds were used. The machine learning algorithm that was developed can classify and detect mechanical failures using an Support Vector Machine (SVM) algorithm based on various statistical learning methods. The study carried out an fast Fourier transform (FFT) analysis in association with the data acquired from front left wheel. The main area of interest is the FFT domain of 5-20hz. The study outcome indicated that the used model is capable of predicting the hysteretic responses associated with the faulty components like broken damper and noisy wheel hub. The designed model can be used for analysing the system’s response and for designing and controlling the faulty components in the car. However, the results of this thesis work can be used to implement the time-based prediction of mechanical component decay.