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 is very important for industrial production. Many intelligent fault diagnosis technologies are successfully applied and achieved good results. Due to the fact that machine damages usually happen under different working conditions, and manual scale labeled data are too expensive, domain adaptation has been developed for fault diagnosis. However, the current methods mostly focus on global domain adaptation, the application of subdomain adaptation for fault diagnosis is still limited. A deep transfer learning method is proposed for rotating machinery fault diagnosis in this study, where subdomain adaptation and adversarial learning are introduced to align local feature distribution and global feature distribution separately. Experiments are performed on two rotating machinery datasets to verify the effectiveness of this method. The results reveal that this method has outstanding mutual migration ability and can improve the diagnostic performance.

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Tool wear condition monitoring plays a crucial role in intelligent manufacturing systems to enhance machining quality and efficiency. The indirect methods employ various sensor signals to monitor tool wear condition, attracting wide attention in industrial applications. Multi-information fusion technologies can promote tool wear monitoring results to be more accurate and reliable. For improving the prediction accuracy and ensuring the reliability of the indirect methods, this study proposes a tool wear prediction method based on multi-information fusion and genetic algorithm (GA)-optimized Gaussian process regression (GPR). First, wavelet packet denoising (WPD)-based signal processing is adopted to suppress the noise interference of multisensor signals. Then, kernel principal component analysis (KPCA)-based dimension reduction is employed to mine the most sensitive features to flank wear from candidate multidomain features. Next, a fusion model of GPR and GA optimization is designed to establish a nonlinear mapping relationship between sensitive characteristics and flank wear width. Finally, performance evaluations under three sets of milling tests are carried out to validate the effectiveness of the proposed method. Experimental results indicate that the proposed method can lower prediction error and uncertainty of flank wear width compared with other intelligent approaches, promoting a successful application of indirect monitoring methods in milling.