Mechanical Fault Diagnosis Method of a Disconnector Based on Improved Dung Beetle Optimizer–Multivariate Variational Mode Decomposition and Convolutional Neural Network–Bidirectional Long Short-Term Memory

As one of the main faults of a disconnector, a mechanical fault is difficult to diagnose in time because of its weak self-evidence, its wide range of fault categories, and the difficulty in obtaining fault sample data. To address this issue, this study proposes a new fault diagnosis algorithm based on multivariate variational mode decomposition optimized by the improved dung beetle optimizer, and at the same time, an experimental platform for vibration signal acquisition was built to simulate three typical mechanical faults. First, the parameters of multivariate variational mode decomposition were optimized using an improved dung beetle optimizer, and the intrinsic mode function with a Pearson correlation coefficient higher than 0.1 was retained after the signal was decomposed. Then, the energy, entropy, and time–frequency domain eigenvalues of the selected intrinsic mode function were calculated to construct the feature matrix, and its dimensions were reduced to two dimensions. Finally, this matrix was input to convolutional neural network–bidirectional long short-term memory for fault classification. The verification of the experimental data shows that the proposed algorithm can successfully diagnose different mechanical faults of the disconnector, and the accuracy rate was 96.67%. The research content provides a new idea for the fault diagnosis of disconnectors.