GGLCM: A Real-time Early Anomaly Detection Method for Mechanical Vibration Data with Missing Labels

[1] Cao, H., Zhou, K., Chen, X., Zhang, X. (2017). Early chatter detection in end milling based on multi-feature fusion and 3σ criterion. The International Journal of Advanced Manufacturing Technology, 92 (9), 4387–4397. https://doi.org/10.1007/s00170-017-0476-x CaoH. ZhouK. ChenX. ZhangX. 2017 Early chatter detection in end milling based on multi-feature fusion and 3σ criterion The International Journal of Advanced Manufacturing Technology 92 9 4387 4397 https://doi.org/10.1007/s00170-017-0476-x Search in Google Scholar

[2] Climente-Alarcon, V., Antonino-Daviu, J. A., Strangas, E. G., Riera-Guasp, M. (2015). Rotor-bar breakage mechanism and prognosis in an induction motor. IEEE Transactions on Industrial Electronics, 62 (3), 1814–1825. https://doi.org/10.1109/TIE.2014.2336604 Climente-AlarconV. Antonino-DaviuJ. A. StrangasE. G. Riera-GuaspM. 2015 Rotor-bar breakage mechanism and prognosis in an induction motor IEEE Transactions on Industrial Electronics 62 3 1814 1825 https://doi.org/10.1109/TIE.2014.2336604 Search in Google Scholar

[3] Zhang, J., Xu, Z., Wang, J., Zhao, J., Din, Z., Cheng, M. (2021). Detection and discrimination of incipient stator faults for inverter-fed permanent magnet synchronous machines. IEEE Transactions on Industrial Electronics, 68 (8), 7505–7515. https://doi.org/10.1109/TIE.2020.3009563 ZhangJ. XuZ. WangJ. ZhaoJ. DinZ. ChengM. 2021 Detection and discrimination of incipient stator faults for inverter-fed permanent magnet synchronous machines IEEE Transactions on Industrial Electronics 68 8 7505 7515 https://doi.org/10.1109/TIE.2020.3009563 Search in Google Scholar

[4] Rojas, G. A., Quiroga Rubiano, E. L., Caratar Chaux, J. F., Pinedo Jaramillo, C. R., Garcia Melo, J. I. (2017). Supervisory system for fault detection and diagnosis in drinking water treatment plants using fuzzy engine. IEEE Latin America Transactions, 15 (11), 2071–2076. https://doi.org/10.1109/TLA.2017.8070410 RojasG. A. Quiroga RubianoE. L. Caratar ChauxJ. F. Pinedo JaramilloC. R. Garcia MeloJ. I. 2017 Supervisory system for fault detection and diagnosis in drinking water treatment plants using fuzzy engine IEEE Latin America Transactions 15 11 2071 2076 https://doi.org/10.1109/TLA.2017.8070410 Search in Google Scholar

[5] Xu, X., Yan, X., Sheng, C., Yuan, C., Xu, D., Yang, J. (2020). A belief rule-based expert system for fault diagnosis of marine diesel engines. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50 (2), 656–672. https://doi.org/10.1109/TSMC.2017.2759026 XuX. YanX. ShengC. YuanC. XuD. YangJ. 2020 A belief rule-based expert system for fault diagnosis of marine diesel engines IEEE Transactions on Systems, Man, and Cybernetics: Systems 50 2 656 672 https://doi.org/10.1109/TSMC.2017.2759026 Search in Google Scholar

[6] De Angelo, C. H., Bossio, G. R., Giaccone, S. J., Valla, M. I., Solsona, J. A., Garcia, G. O. (2009). Online model-based stator-fault detection and identification in induction motors. IEEE Transactions on Industrial Electronics, 56 (11), 4671–4680. https://doi.org/10.1109/TIE.2009.2012468 De AngeloC. H. BossioG. R. GiacconeS. J. VallaM. I. SolsonaJ. A. GarciaG. O. 2009 Online model-based stator-fault detection and identification in induction motors IEEE Transactions on Industrial Electronics 56 11 4671 4680 https://doi.org/10.1109/TIE.2009.2012468 Search in Google Scholar

[7] Wang, Q., Jin, T., Mohamed, M. A., Chen, T. (2020). A minimum hitting set algorithm with prejudging mechanism for model-based fault diagnosis in distribution networks. IEEE Transactions on Instrumentation and Measurement, 69 (7), 4702–4711. https://doi.org/10.1109/TIM.2019.2951866 WangQ. JinT. MohamedM. A. ChenT. 2020 A minimum hitting set algorithm with prejudging mechanism for model-based fault diagnosis in distribution networks IEEE Transactions on Instrumentation and Measurement 69 7 4702 4711 https://doi.org/10.1109/TIM.2019.2951866 Search in Google Scholar

[8] Wu, P., Ferrari, R. M. G., Liu, Y., van Wingerden, J.-W. (2021). Data-driven incipient fault detection via canonical variate dissimilarity and mixed kernel principal component analysis. IEEE Transactions on Industrial Informatics, 17 (8), 5380–5390. https://doi.org/10.1109/TII.2020.3029900 WuP. FerrariR. M. G. LiuY. van WingerdenJ.-W. 2021 Data-driven incipient fault detection via canonical variate dissimilarity and mixed kernel principal component analysis IEEE Transactions on Industrial Informatics 17 8 5380 5390 https://doi.org/10.1109/TII.2020.3029900 Search in Google Scholar

[9] Wang, B., Lei, Y., Li, N., Li, N. (2020). A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Transactions on Reliability, 69 (1), 401–412. https://doi.org/10.1109/TR.2018.2882682 WangB. LeiY. LiN. LiN. 2020 A hybrid prognostics approach for estimating remaining useful life of rolling element bearings IEEE Transactions on Reliability 69 1 401 412 https://doi.org/10.1109/TR.2018.2882682 Search in Google Scholar

[10] Mao, W., Chen, J., Liang, X., Zhang, X. (2020). A new online detection approach for rolling bearing incipient fault via self-adaptive deep feature matching. IEEE Transactions on Instrumentation and Measurement, 69 (2), 443–456. https://doi.org/10.1109/TIM.2019.2903699 MaoW. ChenJ. LiangX. ZhangX. 2020 A new online detection approach for rolling bearing incipient fault via self-adaptive deep feature matching IEEE Transactions on Instrumentation and Measurement 69 2 443 456 https://doi.org/10.1109/TIM.2019.2903699 Search in Google Scholar

[11] Ni, X., Yang, D., Zhang, H., Qu, F., Qin, J. (2023). Time-series transfer learning: An early stage imbalance fault detection method based on feature enhancement and improved support vector data description. IEEE Transactions on Industrial Electronics, 70 (8), 8488–8498. https://doi.org/10.1109/TIE.2022.3229351 NiX. YangD. ZhangH. QuF. QinJ. 2023 Time-series transfer learning: An early stage imbalance fault detection method based on feature enhancement and improved support vector data description IEEE Transactions on Industrial Electronics 70 8 8488 8498 https://doi.org/10.1109/TIE.2022.3229351 Search in Google Scholar