A Fault Diagnosis Method for Variable Condition Equipment Based on Knowledge Transfer and Improved Residual Neural Networks

The foundation of contemporary industry, mechanical equipment is essential to many critical fields, including industrial production, transportation, energy generation, and utilization. Residual neural networks (ResNet) have made considerable progress in the identification of mechanical equipment faults in the past few years. However, due to its relatively high model complexity and large number of parameters, ResNet is challenging to implement in industrial settings and deploy on embedded platforms with limited resources. As a result, this research suggests a knowledge-transfer-based approach for diagnosing equipment faults in variable conditions that use enhanced ResNets. This method adopts a knowledge distillation architecture, where the teacher network uses an improved ResNet50 network to enhance feature information mining capability; the student network uses a simplified depthwise separable convolutional neural network (DSCNN) to achieve lightweight deployment by reducing network size. First, the short-time Fourier transform (STFT) is used to convert the gathered variable condition data into 2-D time-frequency pictures, which are then fed into the neural network model. Then, the ResNet50 model is utilized as the teacher network model and its design is gotten to the next level. Next, a simplified DSCNN and knowledge distillation method are used to train a more lightweight and efficient student network, transferring the complex knowledge from the teacher network to the lightweight depthwise separable (DS) convolutional network. Finally, utilizing the rolling bearing experimental dataset under varied conditions, the suggested method is experimentally validated. The findings demonstrate that with a 96.14% accuracy rate, the computational and parameter complexity was reduced by approximately 238 times, and the runtime was shortened nearly three times. In addition, experimental validation is conducted on a homemade RV gearbox fault simulation test bench. The experimental results demonstrate that the method can achieve robust and efficient fault diagnosis results in different conditions and practical application scenarios.