Optimal symplectic geometric bi-spectrum mutual information contrastive learning framework for fault diagnosis of helicopter spiral bevel gear

Abstract The vibration of helicopter spiral bevel gears has a complex signal pattern that is nonlinear, non-stationary, and with strong background noise, which makes it difficult to meet the requirements of intelligent fault diagnosis by simply using a physical model, signal processing, or machine learning. A framework that integrates vibration mechanism models and deep learning mechanisms based on higher-order spectrum is proposed to accomplish helicopter spiral bevel gear fault diagnosis using the optimal symplectic geometry mode bi-spectrum mutual information maximization encoder (OSBMME). Firstly, recursive symplectic geometry mode decomposition is employed to extract the fault-sensitive optimal symplectic geometry mode (OSGM) from complex vibration signals by combining it with the mechanism model to eliminate the effects of interfering signals and out-of-band noise. After that, the OSGM is transformed into a bispectral contour spectrogram with higher order statistics, maintaining the signal’s phase information and enhancing noise immunity. Finally, a deep mutual information maximization encoder network based on a combination of unsupervised and supervised methods extracts the fault information from the spectrogram and completes the fault diagnosis of helicopter spiral bevel gears utilizing a comparative learning mechanism. The performance verification is carried out on the helicopter main reducer vibration experimental platform through normal as well as injected four fault modes, and the results of the ablation and comparison experiments show that OSBMME can effectively extract high-quality expression of sensitive information from nonlinear, non-stationary, and strongly noisy vibration signals, realize higher accuracy of helicopter spiral bevel gear fault diagnosis, and have better robustness under fluctuating working conditions.