A Homogeneous Stacking Ensemble Learning Model for Fault Diagnosis of Rotating Machinery With Small Samples

Abstract

As important equipment, rotating machinery has been widely used in many industrial fields. Because rotating machinery is prone to failure, its fault diagnosis will be of great significance. In the industrial scene, rotating machinery is usually in normal operation, so it is difficult to accumulate fault samples. Therefore, it will face the problem of fault diagnosis with small samples, which seriously affects the accuracy and stability of fault diagnosis. To solve the above problems, the author proposes a fault diagnosis method based on ACWGAN-GP and homogeneous stacking ensemble learning. Firstly, the method utilizes the argmax multi-class classification idea to construct multiple different training subsets. Secondly, these constructed training subsets are used to train multiple base learners based on ACWGAN-GP, Finally, the meta learner based on Softmax Regression is used to fuse these trained basic learners. So far, the complete fault diagnosis is realized. In this paper, the proposed method is applied to the gearbox data set and the bearing data set. Through a series of experiments, it is proved that this method can not only effectively solve the problem of fault diagnosis with small samples, but also effectively improve the classification accuracy and stability.