Efficient bearing fault diagnosis with neural network search and parameter quantization based on vibration and temperature

Abstract

In this work, we propose a deep-learning method to diagnose bearing faults of electric motors based on vibration and bearing housing temperature. Our methods can accurately diagnose faults related to bearing cracking and lubricant shortages. The proposed method is effective in terms of computational complexity and model capacity thanks to the advantages of neural architecture search (NAS) and parameter quantization in the model establishment. The experimental results found that the information on bearing temperature improved the diagnostic accuracy for the bearing fault diagnosis task. The proposed method has explored the most optimal model in terms of computational resources and model capacity with a pre-defined accuracy target. The searched model has a relatively high diagnostic accuracy of 98.7% and a size of about 27.3 kB. After quantization, the obtained model maintained 96.9% accuracy and reduced 4 times in size. All experiments are executed elaborately on our custom bearing fault dataset.

Wave-ConvNeXt: An Efficient and Precise Fault Diagnosis Method for IIoT Leveraging Tailored ConvNeXt and Wavelet Transform

“…This sophisticated model serves as the foundation for subsequent stages, where the compressed model is meticulously crafted using an array of techniques. These techniques, including knowledge distillation [11], network pruning [12], quantization [13], and low-rank factorization [14] are strategically employed to systematically reduce the size and computational demands of the model. The artful application of these methods ensures that the compressed model maintains a delicate balance, preserving its overall performance even as it undergoes a process of size and computational optimization.…”