A Multi-Sensor Fusion and CWT-CNN-BiLSTM-Based Approach for Small-Sample Fault Diagnosis in Rotating Machinery.

Rotating machinery has been widely used in industries, but it often faces a high incidence of sudden failures under harsh operating conditions. Therefore, ensuring its safety and reliability is of utmost importance. However, fault diagnosis frequently encounters challenges such as limited training samples and the susceptibility of individual vibration sensors to external interference and noise. To enhance the recognition accuracy of rotating machinery under noisy and small-sample conditions, a fault diagnosis method for small samples based on multi-sensor fusion and CWT-CNN-BiLSTM is proposed in the paper. Firstly, the data from the multi-sensor is concatenated and fused, and then converted into a two-dimensional feature image through CWT. This study introduces a CNN-BiLSTM model designed to extract pivotal features from images. The feasibility of this method has been verified using the rotating machinery fault diagnosis database made available by the Korean Institute of Science and Technology. The average diagnostic accuracy achieved is 99.90%. The experimental results show that the proposed method results in more accurate and robust fault classification under small-sample conditions.