Deep transfer learning architecture for suspension system fault diagnosis using spectrogram image and CNN

The suspension system plays a critical role in automobiles, ensuring the safety and comfort of vehicle occupants. However, extended usage, varying road conditions, external forces, and heavy loads can result in damage and faults within the internal components of the suspension system. To mitigate the occurrence of suspension system failures, the development of an effective fault diagnosis system for suspension components becomes imperative. Traditional fault diagnosis techniques often heavily rely on human expertise, which comes with certain limitations. In response, researchers have embraced intelligent fault diagnosis techniques, with transfer learning-based fault diagnosis emerging as a highly effective approach. By leveraging transfer learning, it becomes possible to extract and select fault-specific features for classification purposes. Deep learning-based methods, with their capacity to extract significant features and essential information from raw data, offer notable advantages. Despite these advantages, the implementation of deep learning-based fault diagnosis in suspension systems remains relatively unexplored and limited. In this article, a deep transfer learning architecture specifically designed for fault diagnosis in suspension systems is proposed. The approach involves employing 12 pre-trained networks and tuning them to identify the optimal model for fault diagnosis. Time domain vibration signals obtained from suspension systems under seven fault conditions and one good condition are transformed into spectrogram images. These images are then pre-processed and used as input for the pre-trained networks in fault classification. The results demonstrate that among the 12 pre-trained networks, AlexNet outperforms the others in terms of classification accuracy while requiring the least amount of training time. Therefore, AlexNet network in conjunction with the spectrogram images of time domain vibration signals for applications in suspension system fault diagnosis is highly recommend.