Abstract
Effective fault diagnosis methods can ensure the safe and reliable operation of the machines. In recent years, deep learning technology has been applied to diagnose various mechanical equipment faults. However, in real industries, the data distribution under different working conditions is often different, which leads to serious degradation of diagnostic performance. In order to solve the issue, this study proposes a new deep convolutional domain adaptation network (DCDAN) method for bearing fault diagnosis. This method implements cross-domain fault diagnosis by using the labeled source domain data and the unlabeled target domain data as training data. In DCDAN, firstly, a convolutional neural network is applied to extract features of source domain data and target domain data. Then, the domain distribution discrepancy is reduced through minimizing probability distribution distance of multiple kernel maximum mean discrepancies (MK-MMD) and maximizing the domain recognition error of domain classifier. Finally, the source domain classification error is minimized. Extensive experiments on two rolling bearing datasets verify that the proposed method can implement accurate cross-domain fault diagnosis under different working conditions. The study may provide a promising tool for bearing fault diagnosis under different working conditions.
Highlights
Rolling element bearings are an integral part of the rotating mechanical system, which are widely applied to many fields, such as gearbox, locomotive wheel, and gas turbine
In order to enhance the portability of domain feature representation and better implement domain transfer learning, we focus on the multiple kernel variant of maximum mean discrepancy (MMD) (MK-MMD)
Two experiments of rolling element bearings are taken as examples to verify the effectiveness of the proposed model
Summary
Rolling element bearings are an integral part of the rotating mechanical system, which are widely applied to many fields, such as gearbox, locomotive wheel, and gas turbine. Erefore, it is of great practical significance to propose a fault diagnosis model that can implement accurate fault diagnosis under different working conditions Targeting this issue, various signal processing methods were proposed. Ren et al [25] used multiscale permutation entropy and time-domain features as network input to train neural network, and it is verified by experiments that the proposed method can implement fault diagnosis under different working conditions. Li et al [29] proposed a novel cross-domain fault diagnosis method based on deep generative neural networks. Based on condition recognition and domain adaptation, Guo et al [30] established a deep convolutional transfer learning network. A new domain adaptation method is proposed, which can implement the accurate fault diagnosis without labeled data under various working conditions.
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