Transfer Learning Method Based on Adversarial Domain Adaption for Bearing Fault Diagnosis

Abstract

At present, most of the intelligent fault diagnosis methods of rolling element bearings require sufficient labeled data for training. However, collecting labeled data is usually expensive and timeconsuming, and when the distribution of the test data is different from the distribution of the training data, the diagnostic performance will decrease. In order to solve the problem of unlabeled cross-domain diagnosis of bearings, this paper proposes an adversarial domain adaption method based on deep transfer learning. The short-time Fourier transform is used to transform the original data into a time-frequency image. The feature extractor is used to extract its deep features. The maximum mean discrepancy and domain confusion function are used for domain adaptation to extract domain-invariant features between two domains for cross-domain fault diagnosis. Experiments on two bearing datasets are carried out for validations. The results prove that the method in this paper is superior to other deep transfer learning methods. It shows the advantages of the improved method and can be used as an effective tool for crossdomain fault diagnosis. INDEX TERMS Transfer learning, fault diagnosis, domain adaption, deep learning.

Rotating machinery fault diagnosis by deep adversarial transfer learning based on subdomain adaptation

“…The proposed method is compared with other deep transfer learning methods: Deep Domain Confusion (DDC), 33 Deep Adaptation Network (DAN), 34 Domain-Adversarial Neural Networks (DANN), 35 Deep CORAL (D-CORAL), 36 and our previous work DADA-TL. 37 DDC embeds MMD into an adaptation layer to learn domain invariant features. DAN uses multi-kernel MMD to align different distributions optimally to learn transferable features.…”

Markov Transition Field Enhanced Deep Domain Adaptation Network for Milling Tool Condition Monitoring

“…The above-mentioned works are helpful to build a transfer learning-supported TCM. However, in the actual industrial scene, there are missing categories in the target domain and varying working conditions, leading to the distributions between training data (source domain) and testing data (target domain) being significantly different, which dramatically lowers the performance of TL-based methods [33,34].…”

Research on a Rolling Bearing Fault Detection Method With Wavelet Convolution Deep Transfer Learning

“…Shao et al proposed an adversarial domain adaptation method based on deep transfer learning. This approach used a deep residual network to process a time-frequency image to achieve cross-domain diagnosis [11]. Wen et al proposed a three-layer Sparse Auto-Encoder (SAE) to extract the fea-tures from raw data, and then the MMD term was applied to minimize the discrepancy penalty of two domains.…”