An Improved Lightweight Variant of EfficientNetV2 Coupled With Sensor Fusion and Transfer Learning Techniques for Motor Fault Diagnosis

Although deep learning methods based on single sensors are widely applied in fault diagnosis, leveraging multi-sensor data to learn useful information remains a challenge. To fully utilize multi-sensor information, this paper proposes a lightweight improvement of the EfficientNetV2 architecture, combined with sensor fusion technology and transfer learning techniques, to develop an efficient and reliable new method specifically for motor fault diagnosis. First, the continuous wavelet transform is utilized to convert the signals from various sensors into time-frequency images, and the Mallat algorithm is employed to decompose each image into sub-band coefficients at different levels. Secondly, a fusion reconstruction method is constructed using coefficient absolute maximum and weighted average fusion rules to integrate the sub-band coefficients of multi-sensor time-frequency images at different levels. Subsequently, EfficientNetV2 is improved to enhance the model’s feature extraction capabilities, computational efficiency, and achieve lightweight effects. The EfficientNetV2-M0 network modifies the model’s depth and width multiplicity factors, reducing parameters and computational complexity. Furthermore, this network incorporates Diverse Branch Block (DBB) and Multidimensional Collaborative Attention (MCA) to enhance feature extraction under complex backgrounds, and the maximum cross-entropy loss function is improved by using label smoothing and focal loss to dynamically adjust the classification weights for improved accuracy. The network leverages pre-trained models obtained through transfer learning techniques for deployment, combining multi-sensor information fusion and the improved lightweight model for fault diagnosis applications. Finally, a fault diagnosis experiment is conducted using a motor state dataset. The experimental results demonstrate that the proposed method outperforms the control method in terms of diagnostic performance and robustness, with an accuracy of 100%, and it exhibits excellent performance even under conditions of small sample data, with an accuracy of 98.81%.