Transfer Learning Based Fault Detection for Suspension System Using Vibrational Analysis and Radar Plots

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