Physics-guided neural network in gearbox fault diagnosis

Abstract

Intelligent fault diagnosis involves deep learning (DL) algorithms. A hybrid model that integrates both physics-based and data-driven approaches can offer effective health monitoring solutions. In this study, a physics-guided framework is developed, in comparison with classical feedforward neural network (FNN) and convolutional neural network (CNN) models, to be employed in gearbox fault detection. The proposed framework integrates a main vibration-data-driven model, that is called CNN-A trained on either envelope or fast Fourier transform (FFT) signals, with a complementary CNN-B that specifically learns from the gear mesh frequency (GMF) harmonics and their sidebands. The accuracy of fault detection is improved in the framework through a physics-and-data-driven (PDD) parameter fusion mechanism in which the model updates the weights in CNN-A by considering the weights of CNN-B, based on the healthy and faulty classes. The physics-guided DL approach is evaluated using experimental vibration measurements of a gearbox test rig. Experimental data with varying speeds and loads from another gearbox test bench is utilized for validation. It is concluded that the framework is robust under varying operating conditions, achieving higher and more stable accuracy, that is, higher performance reliability, compared to the CNN and FNN models.