An Intelligent Fault Diagnosis Scheme Using Transferred Samples for Intershaft Bearings Under Variable Working Conditions

Abstract

Machine learning methods have made great development in data-driven fault diagnosis of rolling bearings. But the intelligent fault diagnosis of intershaft bearing faces the following two dilemmas: 1) the fault vibration is extremely weak, and it is difficult to extract features that can distinguish different classes; 2) due to the complex and variable working condition, the intershaft bearing does not always fail in same working conditions. That is, the labeled training data is not sufficient for every source domain. These challenges lead to the failure of traditional machine learning based fault diagnosis for intershaft bearings. Therefore, a novel intelligent fault diagnosis scheme is investigated for intershaft bearings of dual-rotor equipment under variable working conditions. The paper focuses on two key issues: 1) developing a feature extraction approach with which the fault features with excellent clustering and separation are extracted from vibration signals. This approach addresses the dilemma of weak fault feature extraction of intershaft bearing and creates a feasible precondition for subsequent feature transfer; 2) proposing a feature transfer method transforming the labeled sample features in multiple source domains into the trainable sample features in the target domain. This new transfer achieves the sharing of labeled training samples under working conditions and enriches the trainable samples in target domain. Ultimately, the faults of intershaft bearings can be diagnosed with the help of the neural network classifier trained by the transferred samples with labels. Experimental results verify that this established scheme is effective and superior to other comparable methods for the transfer diagnosis task from multiple source domains to target domain.