Intelligent Sound Diagnostics - Acoustic Fault Detection in Truck Steering Systems Using Machine Learning and Deep Learning

Abstract

This thesis investigates acoustic fault diagnosis for heavy duty truck steering components, focusing on two safety critical parts, the Power Steering Pump (PSP) and the Steering Gear, using a real world dataset of 326 recordings collected under operational conditions. The problem is formulated as a seven class classification task covering four sound types under normal and faulty conditions. Classical machine learning models trained on hand crafted acoustic features are compared against a convolutional neural network trained from scratch and Pretrained Audio Neural Network (PANN) models used as frozen feature extractors, to identify which modelling approach is most effective for this task. Seven individual acoustic descriptors, MFCC, log-Mel spectrogram statistics, sub-band energy ratios, spectral shape descriptors, time-domain statistics, amplitude envelope kurtosis, and discrete wavelet transform coefficients, are also evaluated in isolation to determine which is most discriminative for fault detection. All models are evaluated using StratifiedGroupKFold cross validation with file level grouping to prevent leakage between overlapping audio segments, and performance is reported using the Matthews Correlation Coefficient (MCC) and macro F1, both robust to class imbalance. A linear Support Vector Machine trained on a 180-feature ANOVA selected set achieves the strongest overall performance, reaching a held out test MCC of 1.000, ahead of every deep learning approach evaluated, including a pretrained PANN backbone with over 80 million parameters. A compact CNN trained entirely from scratch matches the best pretrained PANN variant despite requiring no external pretraining, while the largest pretrained backbone performs weakest of all models compared. Among the individual descriptors, log-Mel spectrogram statistics and MFCCs prove the most discriminative, while amplitude envelope kurtosis alone carries almost no discriminative power. These findings suggest that carefully engineered acoustic features combined with a simple classical classifier can outperform learned and transferred deep representations on a dataset of this scale, offering a lightweight and interpretable alternative for industrial predictive maintenance.