The suspension system is of paramount importance in any automobile. Thanks to the suspension system, every journey benefits from pleasant rides, stable driving and precise handling. However, the suspension system is prone to faults that can significantly impact the driving quality of the vehicle. This makes it essential to find and diagnose any faults in the suspension system and rectify them immediately. Numerous techniques have been used to identify and diagnose suspension faults, each with drawbacks. This paper’s proposed suspension fault detection system aims to detect these faults using deep transfer learning techniques instead of the time-consuming and expensive conventional methods. This paper used pre-trained networks such as Alex Net, ResNet-50, Google Net and VGG16 to identify the faults using radar plots of the vibration signals generated by the suspension system in eight cases. The vibration data were acquired using an accelerometer and data acquisition system placed on a test rig for eight different test conditions (seven faulty, one good). The deep learning model with the highest accuracy in identifying and detecting faults among the four models was chosen and adopted to find defects. The results state that VGG16 produced the highest classification accuracy of 96.70%.

Automobile clutch systems represent pivotal components within vehicles that facilitate smooth gear shifting, optimal engine operation, and efficient power transmission. Ensuring the integrity of clutch systems is paramount for maintaining vehicle performance and safety standards. Consequently, developing robust fault detection methodologies is imperative for promptly identifying potential issues. This study investigates the application of a CATboost classifier with feature fusion to analyse vibrational signals from clutch systems. Vibrational signals, collected via specialized sensors across various clutch conditions, serve as the primary dataset for fault detection analysis. A comprehensive feature fusion approach, combining statistical, histogram, and Autoregressive Moving Average (ARMA) features, aims to enhance fault detection accuracy. By integrating these feature sets, the study gains insights into clutch system behavior under varying operational circumstances. The classifier successfully identifies five distinct faults: worn release fingers, fractured pressure plates, deteriorated pressure plates, loss of friction material, and distorted tangential strips. Each fault presents unique challenges, emphasizing the significance of accurate detection mechanisms. Results underscore the remarkable performance of the CATboost classifier, achieving 100% accuracy when combining ARMA and statistical features. Impressive accuracy rates of 98.889% and 97.50% are observed with alternative feature combinations. Five other machine learning models (Decision Stump, Hoeffding Tree, REP Tree, SVM, and Random Forest) were also trained on the best feature combination set and compared to CATboost, illustrating its superiority. These findings substantiate the efficacy of feature fusion in augmenting fault detection capabilities within automobile clutch systems. The study’s outcomes highlight the potential for improving vehicle maintenance practices, reducing downtime, and enhancing overall automotive safety through advanced fault detection techniques. Future research could explore real-time implementation of these methods in vehicle diagnostics systems.