References
Chen, H., Jiang, B., Ding, S. X., & Huang, B. (2022, March). Data-driven fault diagnosis for traction systems in high-speed trains: A survey, challenges, and perspectives. IEEE Transactions on Intelligent Transportation Systems, 23(3), 1700–1716. doi:10.1109/TITS.2020.3029946
Dai, J., Wang, J., Huang, W., Shi, J., & Zhu, Z. (2020, October). Machinery Health Monitoring Based on Unsupervised Feature Learning via Generative Adversarial Networks. IEEE/ASME Transactions on Mechatronics, 25(5), 2252–2263. doi:10.1109/TMECH.2020.3012179
Ding, Y., Ma, L., Ma, J., Wang, C., & Lu, C. (2019). A Generative Adversarial Network-Based Intelligent Fault Diagnosis Method for Rotating Machinery Under Small Sample Size Conditions. IEEE Access, 7, 149736149749. doi: 10.1109/ACCESS.2019.2947194 Elasha, F., Ruiz-C´arcel, C., Mba, D., Kiat, G., Nze, I., & Yebra, G. (2014, July). Pitting detection in worm gearboxes with vibration analysis. Engineering Failure Analysis, 42, 366–376. doi:10.1016/j.engfailanal.2014.04.028
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., WardeFarley, D., Ozair, S., . . . Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (Vol. 27). Curran Associates, Inc. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017, December). Improved training of wasserstein gans (No. arXiv:1704.00028). arXiv. doi:10.48550/arXiv.1704.00028
He, R., Tian, Z., & Zuo, M. J. (2022, April). A semi-supervised gan method for rul prediction using failure and suspension histories. Mechanical Systems and Signal Processing, 168, 108657. doi:10.1016/j.ymssp.2021.108657
4393–4402). PMLR. Salameh, J. P., Cauet, S., Etien, E., Sakout, A., & Rambault, L. (2018, October). Gearbox condition monitoring in wind turbines: A review. Mechanical Systems and Signal Processing, 111, 251–264. doi:
Hendriks, J., Dumond, P., & Knox, D. (2022, April). Towards better benchmarking using the CWRU bearing fault dataset. Mechanical Systems and Signal Processing, 169, 108732. doi: 10.1016/j.ymssp.2021.108732 Kim, Y., Na, K., & Youn, B. D. (2022, March). A health-adaptive time-scale representation (htsr) embedded convolutional neural network for gearbox fault diagnostics. Mechanical Systems and Signal Processing, 167, 108575. doi: 10.1016/j.ymssp.2021.108575 Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014, January). Prognostics and health management design for rotary machinery systemsreviews, methodology and applications. Mechanical Systems and Signal Processing, 42, 314–334. doi:
10.1016/j.ymssp.2018.03.052
Schlegl, T., Seeb¨ock, P., Waldstein, S. M., Schmidt-Erfurth, U., & Langs, G. (2017). Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In M. Niethammer et al. (Eds.), Information processing in medical imaging (pp. 146–157). Cham: Springer International Publishing.
Shi, J., Peng, D., Peng, Z., Zhang, Z., Goebel, K., & Wu,10.1016/j.ymssp.2013.06.004
Liu, C., & Gryllias, K. (2020, January). A semi-supervised support vector data description-based fault detection method for rolling element bearings based on cyclic spectral analysis. Mechanical Systems and Signal Processing, 140. doi: 10.1016/j.ymssp.2020.106682 Mao, W., Feng, W., Liu, Y., Zhang, D., & Liang, X. (2021, March). A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis. Mechanical Systems and Signal Processing, 150, 107233. doi: 10.1016/j.ymssp.2020.107233 ¨Ozt¨urk, H., Sabuncu, M., & Yesilyurt, I. (2008, April). Early Detection of Pitting Damage in Gears using Mean Frequency of Scalogram. Journal of Vibration and Control, 14(4), 469–484. doi:10.1177/1077546307080026
D. (2022, January). Planetary gearbox fault diagnosis using bidirectional-convolutional lstm networks. Mechanical Systems and Signal Processing, 162, 107996. doi: 10.1016/j.ymssp.2021.107996 Teng, W., Wang, F., Zhang, K., Liu, Y., & Ding, X. (2014, January). Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition. Strojniˇski vestnik – Journal of Mechanical Engineering, 60(1), 12–20. doi: 10.5545/sv-jme.2013.1295 Van Maele, D., Poletto, J. C., Neis, P. D., Ferreira, N. F., Fauconnier, D., & De Baets, P. (2023). Online visionassisted condition monitoring of gearboxes. In 8th european conference and exhibition on lubrication, maintenance and tribotechnology (lubmat 2023), proceedings. Wang, J., Li, S., Han, B., An, Z., Bao, H., & Ji, S. (2019). Generalization of Deep Neural Networks for Imbalanced Fault Classification of Machinery Using Generative Adversarial Networks. IEEE Access, 7, 111168111180. doi: 10.1109/ACCESS.2019.2924003 Xiang, L., Yang, X., Hu, A., Su, H., & Wang, P. (2022, January). Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks. Applied Energy, 305, 117925. doi:
Peng, D., Liu, C., Desmet, W., & Gryllias, K. (2023, July). Condition monitoring of wind turbines based on anomaly detection using deep support vector data description. Journal of Engineering for Gas Turbines and Power, 145(091009). doi: 10.1115/1.4062768 Qin, Y., Wang, Z., & Xi, D. (2022, January). Tree cyclegan with maximum diversity loss for image augmentation and its application into gear pitting detection. Applied Soft Computing, 114, 108130. doi:10.1016/j.apenergy.2021.117925 10.1016/j.asoc.2021.108130
Ren, L., Sun, Y., Cui, J., & Zhang, L. (2018, July). Bearing remaining useful life prediction based on deep autoencoder and deep neural networks. Journal of Manufacturing Systems, 48, 71–77. doi:
Zhang, Y., Liu, W., Wang, X., & Gu, H. (2022, July). A novel wind turbine fault diagnosis method based on compressed sensing and dtl-cnn. Renewable Energy, 194, 249–258. doi: 10.1016/j.renene.2022.05.085 Zhou, C., & Paffenroth, R. C. (2017, August). Anomaly detection with robust deep autoencoders. In Proceedings of the 23rd acm sigkdd international conference on knowledge discovery and data mining (pp. 665–674). Halifax NS Canada: ACM. doi:
10.1145/3097983.3098052
10.1016/j.jmsy.2018.04.008
Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S. A., Binder, A., . . . Kloft, M. (2018, July). Deep one-class classification. In Proceedings of the 35th international conference on machine learning (pp.
Zhou, K., Diehl, E., & Tang, J. (2023, February). Deep convolutional generative adversarial network with semisupervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations. Mechanical Systems and Signal Processing, 185, 109772. doi: 10.1016/j.ymssp.2022.109772
Zhu, R., Mousmoulis, G., & Gryllias, K. (2023, July). Wavelet-based high order spectrum for local damage diagnosis of gears under different operating conditions. In Surveillance, Vibrations, Shock and Noise. Toulouse, France: Institut Sup´erieur de l’A´eronautique et de l’Espace [ISAE-SUPAERO].