Enhancing Vehicle Fault Diagnosis Through Multi-View Sound Analysis: Integrating Scalograms and Spectrograms in a Deep Learning Framework

Abstract

This study presents a comprehensive framework for vehicle fault diagnosis using engine sound signals, leveraging deep learning models and a multi-view approach. Traditional methods for vehicle fault diagnosis often rely on the expertise of mechanics or diagnostic tools, which can be costly, time-consuming, and may not always provide accurate results. To address these limitations, we propose CarFaultNet, a multi-view model that processes both scalograms and spectrograms simultaneously to capture complementary information from these time-frequency representations. Our approach incorporates transfer learning with pretrained convolutional neural networks, including AlexNet, GoogLeNet, ShuffleNet, SqueezeNet, and MobileNet v2, as well as CarFaultNet, which combines two MobileNet networks. The results demonstrate that CarFaultNet outperforms traditional machine learning methods and single-view deep learning models, achieving a precision of 95.32%, recall of 94.83%, F1-score of 94.99%, and accuracy of 95.00%. Class activation mapping visualizations provide valuable insights into the model's decision-making process, highlighting the regions of the input images that are most influential for the classification of different vehicle faults. By leveraging a large, diverse dataset encompassing various vehicle models and real-world operating conditions, our approach addresses the drawbacks of previous studies and demonstrates the potential of deep learning for practical and effective vehicle fault diagnosis.