CNN-IPINN: a method study on applying physics-informed neural networks to gear fault diagnosis

Abstract Previous deep learning-based methods for gear fault diagnosis and assessment, due to their black-box nature, result in the extracted signal features and diagnostic results lacking practical physical significance and interpretability. This study combines deep learning methods with the dynamic characteristics of gears and proposes a gear fault evaluation method named convolutional-neural-network-based inverse physics-informed neural network (CNN-IPINN). First, a neural network loss function is designed based on the gear dynamics equation to construct an IPINN for solving inverse problems in gear dynamics. This design enables the neural network to extract the actual time-varying meshing stiffness (TVMS) from gear vibration signals, which serves as the core basis for gear fault diagnosis and assessment, thereby enhancing the interpretability of the network. Considering the issues of low accuracy, model complexity, and slow operation speed in traditional physics-informed neural networks (PINNs), as well as the spatial correlation of gear vibration signals, this study introduces CNN as the backbone network of PINN to construct CNN-IPINN for extracting the TVMS of gears. Finally, based on the actual gear experimental dataset, diagnoses and evaluations are performed on gear faults involving varying degrees of wear, pitting, and crack damage. This approach achieved highly accurate and interpretable gear fault diagnosis, thereby demonstrating broad prospects in engineering applications.