Clark R, Zucker N, Urpelainen J. The future of coal-fired power generation in Southeast Asia. Renew Sustain Energy Rev. 2020;121:109650.
Jiang X, Yang S, Wang F, et al. OrbitNet: A new CNN model for automatic fault diagnostics of turbomachines. Appl Soft Comput. 2021;110:107702.
Ma T, Liu S, Liu J, et al. Fault diagnosis of laminar cooling roller motor based on morphological recognition and combination patterns mining of multi-current signatures. IEEE Trans Instrum Meas. 2023;72(3):1–15.
Liu J, Xu L, Xie Y, et al. Toward robust fault identification of complex industrial processes using stacked sparse-denoising autoencoder with softmax classifier. IEEE Trans Cybern. 2021;53(1):428–42.
Liu J, Wang J, Liu X, et al. MWRSPCA: online fault monitoring based on moving window recursive sparse principal component analysis. J Intell Manuf. 2021;1–17. https://doi.org/10.1007/s10845-020-01721-8.
Huang Z, Lei Z. A multisource dense adaptation ad-versarial network for fault diagnosis of machinery. IEEE Trans Industr Electron. 2021;69(6):6298–307.
Xu K, Li S, Jiang X, Lu J, Yu T, Li R. A novel transfer diagnosis method under unbalanced sample based on dis-crete-peak joint attention enhancement mechanism. Knowl-Based Syst. 2021;212:106645.
Bao H, Yan Z, Ji S, et al. An enhanced sparse filtering method for transfer fault diagnosis using maximum classifier discrepancy. Meas Sci Technol. 2021;32(8):085105. https://doi.org/10.1088/1361-6501/abe56f.
Li X, Hu Y, Zheng J, Li M, Ma W. Central moment discrepancy based domain adaptation for intelligent bearing fault diagnosis. Neurocomputing. 2021;429:12–24.
Xiong P, Tang B, Deng L, Zhao M, Yu X. Multi-block domain adaptation with central moment discrepancy for fault diagnosis. Measurement. 2021;169:108516.
Li X, Zhang W, Ding Q, Sun J. Multi-layer domain adaptation method for rolling bearing fault diagnosis. Sig-nal Processing. 2019;157:180–97.
Jiao J, Zhao M, Lin J, Liang K. Residual joint adapta-tion adversarial network for intelligent transfer fault diagno-sis. Mech Syst Signal Process. 2020;145:106962.
Zhu J, Shao J, Huang Z. Transfer learning method based on adversarial domain adaption for bearing fault di-agnosis. IEEE Access. 2020;8:119421–30.
Chai Z, Zhao C. A fine-grained adversarial network method for cross-domain industrial fault diagnosis. IEEE Trans Autom Sci Eng. 2020;17(3):1432–42.
Chen Z, He G, Li J, Liao Y, Gryllias K, Li W. Domain adversarial transfer network for cross-domain fault diagno-sis of rotary machinery. IEEE Trans Instrum Meas. 2020;69(11):8702–12.
Li F, Tang T, Tang B, He Q. Deep convolution do-main-adversarial transfer learning for fault diagnosis of roll-ing bearings. Measurement. 2021;169:108339.
Li X, Zhang W, Ma H, Luo Z, Li X. Domain generalization in rotating machinery fault diagnostics using deep neural networks. Neurocomputing. 2020;403:409–20.
Zhang M, Wang D, Lu W, Yang J, Li Z, Liang B. A deep transfer model with wasserstein distance guided multi-adversarial networks for bearing fault diagnosis under dif-ferent working conditions. IEEE Access. 2019;7:65303–18.
Yang B, Lei Y, Jia F, et al. An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings. Mech Syst Signal Process. 2019;122:692–706.
Zhang K, Chen J, Zhang T, et al. A compact convolutional neural network augmented with multiscale feature extraction of acquired monitoring data for mechanical intelligent fault diagnosis. J Manuf Syst. 2020;55:273–84.
Li X, Zhang W, Ma H, et al. Deep learning-based adversarial multi-classifier optimization for cross-domain machinery fault diagnostics. J Manuf Syst. 2020;55:334–47.
Qin Y, Yao Q, Wang Y, et al. Parameter sharing adversarial domain adaptation networks for fault transfer diagnosis of planetary gearboxes. Mech Syst Signal Process. 2021;160:107936.
Wang Z, He X, Yang B, Li N. Subdomain adaptation transfer learning network for fault diagnosis of roller bearings. IEEE Trans Industr Electron. 2021;69(8):8430–9.
She D, Jia M, Pecht MG. Weighted entropy minimization based deep conditional adversarial diagnosis approach under variable working conditions. IEEE/ASME Trans Mechatron. 2020;26(5):2440–50.
Tang Z, Bo L, Liu X, et al. An autoencoder with adaptive transfer learning for intelligent fault diagnosis of rotating machinery. Meas Sci Technol. 2021;32(5):055110.
Zhao K, Jiang H, Wu Z, et al. A novel transfer learning fault diagnosis method based on manifold embedded distribution alignment with a little labeled data. J Intell Manuf. 2022;33:151–65.
Rezaeianjouybari B, Shang Y. A novel deep multi-source domain adaptation framework for bearing fault diagnosis based on feature-level and task-specific distribution alignment. Measurement. 2021;178:109359.
Zhu J, Chen N, Shen C. A new multiple source domain adaptation fault diagnosis method between different rotating machines. IEEE Trans Industr Inf. 2020;17(7):4788–97.
Wei D, Han T, Chu F, et al. Weighted domain adaptation networks for machinery fault diagnosis. Mech Syst Signal Process. 2021;158:107744.
Li X, Jiang H, Xie M, et al. A reinforcement ensemble deep transfer learning network for rolling bearing fault diagnosis with multi-source domains. Adv Eng Inform. 2022;51:101480.
Saito K, Watanabe K, Ushiku Y, et al. Maximum classifier discrepancy for unsupervised domain adaptation. In: 2018 IEEE/CVF conference on computer vision and pattern recognition. IEEE; 2018. pp. 3723–32. https://doi.org/10.1109/CVPR.2018.00392.
Li X, Zhang W, Ma H, Luo Z, Li X. Deep learning-based adversarial multi-classifier optimization for cross-domain machinery fault diagnostics. J Manufactur-ing Syst. 2020;55:334–47.
Tzeng E, et al. Deep domain confusion: maximizing for domain invariance. Computer Science. 2014.
Ganin Y, Ustinova E, Ajakan H, Germain P. Domain-adversarial training of neural networks. J Mach Learn Res. 2016;17(1):2096–2030.
MathSciNet MATH Google Scholar
Sun B, Saenko K. Deep CORAL: correlation alignment for deep domain adaptation. In: Hua G, Jégou H, editors. Computer vision – ECCV 2016 workshops. ECCV 2016. Lecture notes in computer science. vol. 9915. Cham: Springer; 2016. pp. 443–50. https://doi.org/10.1007/978-3-319-49409-8_35.
Zhao H, Zhang S, Wu G, et al. Multiple source domain adaptation with adversarial training of neural networks. 2017.
Zhu Y, Zhuang F, Wang D. Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources. In: Proceedings of the AAAI conference on artificial intelligence. vol. 33, issue 1. 2019. pp. 5989–996. https://doi.org/10.1609/aaai.v33i01.33015989.
Comments (0)