[1] ZHANG X L, CHEN W, WANG B J et al. Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization[J]. Neurocomputing, 167, 260-279(2015).

[2] JEGADEESHWARAN R, SUGUMARAN V. Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines[J]. Mechanical Systems & Signal Processing, 52-53, 436-446(2015).

[4] ZHAO M H, ZHONG S S, FU X Y et al. Deep residual shrinkage networks for fault diagnosis[J]. IEEE Trans-actions on Industrial Informatics, 16, 4681-4690(2019).

[5] [5] INCE T , KIRANYAZ S , EREN L , et al. Realtime mot fault detection by 1D convolutional neural wks[C]. IEEE Transactions on Industrial Electronics, 2016, 63(11): 70677075.

[8] [8] CSURKA G. Domain adaptation in computer vision applications [M]. Cham: Springer International Publishing AG, 2017: 135.

[9] PAN S J, YANG Q. A survey on transfer learning[J]. IEEE Transactions on knowledge and data engineering, 22, 1345-1359(2009).

[10] [10] ZHUANG F Z, CHENG X H, LUO P, et al. Supervised representation learning: transfer learning with deep autoencoders[C] Proceedings of the TwentyFourth International Joint Conference on Artificial Intelligence, 2015: 4119−4125.

[11] ZHUANG F Z, QI Z Y, DUAN K Y et al. A comprehensive survey on transfer learning[J]. Proceedings of the IEEE, 109, 43-76(2020).

[12] YANG B, LEI Y, JIA F et al. An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings[J]. Mechanical Systems and Signal Processing, 122, 692-706(2019).

[13] CHEN H T, CHAI Z, JIANG B et al. Data-driven fault detection for dynamic systems with performance degradation: a unified transfer learning framework[J]. IEEE Transactions on Instrumentation and Measurement, 70, 1-12(2020).

[14] CHEN Z Y, WU J, DENG C, et, al. Residual deep subdomain adaptation network: a new method for intelligent fault diagnosis of bearings across multiple domains[J]. Mechanism and Machine Theory, 169, 104635(2022).

[15] PAN S J, TSANG I W, KWOK J T et al. Domain adaptation via transfer component analysis[J]. IEEE transactions on neural networks, 22, 199-210(2010).

[16] YANG B, XU S, LEI Y et al. Multi-source transfer learning network to complement knowledge for intelligent diagnosis of machines with unseen faults[J]. Mechanical Systems and Signal Processing, 162, 108095(2022).

[17] QIN Y, YAO Q W, WANG Y et al. Parameter sharing adversarial domain adaptation networks for fault transfer diagnosis of planetary gearboxes[J]. Mechanical Systems and Signal Processing, 160, 107936(2021).

[18] [18] WOO S , PARK J , LEE J Y , et al. CBAM: convolutional block attention module[C]Proceedings of ECCV 2018: 15th European Conference on Computer Vision.Munich, Germany: SpringerVerlag, 2018: 3−19.

[19] ZHU Y C, ZHUANG F Z, WANG J D et al. Deep sub-domain adaptation network for image classification[J]. IEEE transactions on neural networks and learning systems, 32, 1713-1722(2020).

[20] HUANG H, BADDOUR N. Bearing vibration data collected under time-varying rotational speed conditions[J]. Data in brief, 21, 1745-174(2018).