A novel metric-based model with the ability of zero-shot learning for intelligent fault diagnosis

Abstract

Intelligent fault diagnosis plays an important role in maintaining the safe and reliable operation of rotating machinery. However, the data collected in real engineering scenarios may be severely insufficient, which presents challenges to the intelligent fault diagnosis methods. To address this problem, this paper introduces a metric-based meta learning approach for gear fault diagnosis under zero shot conditions. Firstly, a gear-rotor dynamics model is established to simulate the vibration signals under different fault conditions. And the signals are converted into energy maps through wavelet transformation to provide frequency domain fault features. Secondly, a deep convolutional network is employed as the feature extraction module to construct the prototype representations by calculating the average embedding within each fault class. Then, the distances between the actual signals collected from the gear test rig and the class prototypes are computed. Finally, the softmax is applied to convert these distances into probability distributions for outputting the predicted fault classes. Furthermore, label smoothing technology is introduced to mitigate the probability distribution differences between simulated signals and real signals. The experimental results demonstrate that the average diagnostic accuracy of the proposed model reaches 98.9%, which is better than other models.