Intelligent fault diagnosis scheme for rolling bearing based on domain adaptation in one dimensional feature matching

Abstract

The rapid development of intelligent manufacturing technology has made data-driven fault diagnosis technology an important research at present. Most existing intelligent approaches, on the other hand, have a flaw: training and testing data are collected under the same operational conditions. As a result, a feature knowledge transfer learning method for rotating machinery fault detection is presented in this study to encourage the effective implementation of intelligent fault diagnosis. This method combines the domain adaptive ability of transfer learning and the ability of deep learning to automatically extract features Firstly, unsupervised convolutional auto-encoder is used for learning information from multiple source domains to construct feature subspace. Then adaptive learning rate, weight initialization and weight change algorithm are used to construct feature matching algorithm and incorporated into the stack-encoder. Finally, the encoder is used for diagnostic recognition of feature subspace information. Three datasets from various experimental platforms are used to validate the proposed method’s efficacy. The experimental findings demonstrate the method’s capacity to diagnose and identify multi-domain faults in bearings while having strong generalization and flexibility. Comparatively speaking, the proposed model may attain higher diagnostic accuracy with a rate of accuracy of above 98%. In addition, two additional analytical experiments were conducted. The findings demonstrate that the method may provide over-task transfer learning and reliable diagnostic results. And, it demonstrates how improved diagnostic models may be built with a good dataset of unlabeled source domains.