Multi-fault diagnosis and damage assessment of rolling bearings based on IDBO-VMD and CNN-BiLSTM

The development trend of high precision of mechanical equipment, the reliability of bearings work increasingly demanded. Therefore, it is crucial for the reliable operation of mechanical equipment to evaluate the health status of bearings. It combines IDBO (Improved Dung beetle optimizer) optimised VMD (Variational mode decomposition) and CNN-BiLSTM (convolutional neural network-Bi-directional Long Short-Term Memory) to achieve rolling bearing conformity fault diagnosis and damage assessment. Chaotic mapping, Golden sine algorithm and cosine iteration strategy are introduced to improve the performance of DBO, and the hyperparameters of VMD are optimised using IDBO to improve the signal pre-processing. Feature extraction and fault classification of signals using CNN-BiLSTM is used to compensate for the poor diagnosis of CNN timing signals by BiLSTM instead of Softmax classifier. The HUST dataset is used to discuss the application of signal processing methods and neural network models in bearing composite fault diagnosis. The advantages of the proposed scheme in bearing composite fault and damage assessment are verified, effectively solving the challenge of rolling bearing composite fault diagnosis.