A Light Weight Multisensory Fusion Model for Induction Motor Fault Diagnosis

Fault diagnosis keeps an essential tool to ensure the safety and reliability of a motor system. Based on deep learning, fault diagnosis models constructed by mining historical fault data of equipment have received extensive attention. However, the high computational cost constrains the application of deep learning models for fault diagnosis, especially when coping with multisource data. Inspired by the model reduction and neural network structure automatic search method, this article proposed a light weight multisensory fusion model for induction motor data fusion and diagnosis. Inverted residual block and network architecture search technology are introduced to accelerate the training speed and prediction speed of the diagnostic model, so that the diagnostic accuracy is maintained at an acceptable level. The effectiveness of the proposed model is demonstrated through motor fault diagnosis experiments. Compared with other popular neural networks, the proposed method is capable of judging fault patterns accurately with shorter prediction time.