Self-Supervised Metalearning Generative Adversarial Network for Few-Shot Fault Diagnosis of Hoisting System With Limited Data

Abstract

Few-shot data collected from hoisting system suffer from inadequate information in the practical industries, which reduces the diagnostic accuracy of the data-driven-based fault diagnosis approaches. To overcome this problem, in this article, a self-supervised metalearning generative adversarial network algorithm is proposed. The purpose of the proposed algorithm is to determine the optimal initialization parameters of the model by training on various data generation tasks, thus accomplishing new data generation using only a small amount of training data. Specifically, a self-supervised strategy is proposed to improve the generalization performance of the proposed algorithm. Besides, the fault data of the hoisting system are collected for data generation, and the experimental results show that the proposed algorithm can determine the optimal initialization parameters under the condition of insufficient datasets. The effectiveness of the proposed algorithm for few-shot fault diagnosis is verified by using a mixture of real data and generated data.