Robust Anomaly Detection with Streaming Data for Incipient Fault Alarm of High-speed Electrical-Driven Bearing

NAVIGATE

Table of Contents

STATISTICS

Viewed20

Downloads22

Robust Anomaly Detection with Streaming Data for Incipient Fault Alarm of High-speed Electrical-Driven Bearing

PDF下载 (22)

[1]MAO Wentao,CHAO Long,ZHANG Ziyi,et al.Robust Anomaly Detection with Streaming Data for Incipient Fault Alarm of High-speed Electrical-Driven Bearing[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-10.[doi:10.13705/j.issn.1671-6833.2026.03.013]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 48 Number of periods: 2027 XX Page number: 1-10 Column: Public date: 2027-12-10

Title:: Robust Anomaly Detection with Streaming Data for Incipient Fault Alarm of High-speed Electrical-Driven Bearing

Author(s):: MAO Wentao^1,2, CHAO Long¹, ZHANG Ziyi¹, SHAO Yibo¹, ZHONG Zhidan³; 1. School of Computer and Information Engineering ( School of Artifical Intelligence) , Henan Normal University, Xinxiang 453007, China; 2. Engineering Laboratory of Intelligence Business and Internet of Things, Xinxiang 453007, China; 3. School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471023, China

Keywords:: concept drift; streaming data; anomaly detection; contrastive learning; early fault detection

CLC:: TP183 TH165+.3

DOI:: 10.13705/j.issn.1671-6833.2026.03.013

Abstract:: The vibration signals of high-speed electrical-driven bearings under rapidly-varying rotational speed are characterized by stepwise variations in their statistical characteristics, leading to concept drift in the data distribution. Current anomaly detection methods generally rely on static independent and identically distributed (i. i. d.) assumptions, but still struggle to recognize concept drift well, which further results in false alarms. To address these challenges, a concept drift-aware robust anomaly detection method with streaming data is proposed in this paper. First, an anomaly detection pre-training mechanism based on contrastive learning and tensor decomposition is designed to produce high-quality initial features with both generalization and discriminative capability. Second, a new concept drift-aware deep support vector data description (Deep SVDD) model is constructed to enable rapid fine-tuning of streaming data, while calculating the local deviation scores under hyper-sphere constraint. A distribution-aware mechanism using sliding windows and kernel density estimation (KDE) is also integrated to calculate concept drift scores. Finally, these two scores are evaluated together to determine whether the model update under new data distribution is required, with early fault occurrence precisely recognized. Experimental validation on our high-speed bearing testbed under varying operating conditions demonstrates that concept drift points can be accurately filtered out with real early fault identified. The proposed method provides an advance warning of 10 samples compared to the supervisory alarm, while maintaining a zero false alarm rate.

References:: [1] Schneider V, Behrendt C, Höltje P, et al. Electrical bearing damage, a problem in the nano- and macro-range[J]. Lubricants, 2022, 10(8): 194-
[2] Li Yongbo, Xu Minqiang, Liang Xihui, et al. Application of bandwidth EMD and adaptive multiscale morphology analysis for incipient fault diagnosis of rolling bearings[J]. IEEE Transactions on Industrial Electronics, 2017, 64(8): 6506-6517.
[3] Liu Yang, Li Lingjun, Wang Yu, et al. Fault feature extraction method of rolling bearings based on FIF-CYCBD[J]. Journal of Zhengzhou University (Engineering Science), 2022, 43(4): 35-40. [刘洋, 李凌均, 王宇, 等. 基于FIF-CYCBD的滚动轴承故障特征提取方法研究[J]. 郑州大学学报(工学版), 2022, 43(4): 35-40.]
[4] Khelif M A, Bendiabdelah A, Cherif B D E. A combined RMS-MEAN value approach for an inverter open-circuit fault detection[J]. Periodica Polytechnica Electrical Engineering and Computer Science, 2019, 63(3): 169-177.
[5] Pan Yongke, He Ziping, Xia Kewen, et al. Improved co-training semi-supervised SVM and its application in oil layer recognition[J]. Journal of Zhengzhou University (Engineering Science), 2022, 43(1): 14-19. [潘用科, 贺紫平, 夏克文, 等. 改进的协同训练半监督SVM在油层识别中的应用[J]. 郑州大学学报(工学版), 2022, 43(1): 14-19.]
[6] Kopp M, Pevný T, Holeňa M. Anomaly explanation with random forests[J]. Expert Systems with Applications, 2020, 149: 113187.
[7] Mao Wentao, Chen Zongtao, Zhang Yanna, et al. Tensor-DAAD: when tensor meets online early fault detection with transfer learning[J]. Measurement, 2023, 208: 112478.
[8] Hayashi T, Cimr D, Studnička F, et al. Distance-based one-class time-series classification approach using local cluster balance[J]. Expert Systems with Applications, 2024, 235: 121201.
[9] Jana D, Patil J, Herkal S, et al. CNN and Convolutional Autoencoder (CAE) based real-time sensor fault detection, localization, and correction[J]. Mechanical Systems and Signal Processing, 2022, 169: 108723.
[10] Ruff L, Vandermeulen R A, Goernitz N, et al. Deep one-class classification[C]//Proceedings of the International Conference on Machine Learning. Stockholm: PMLR, 2018: 4393-4402.
[11] Zhang Jian, Guo Lili, Gao Song, et al. Bayes-optimized adaptive growing neural gas method for online anomaly detection of industrial streaming data[J]. Applied Sciences, 2024, 14(10): 4139.
[12] Lin Y M, Kang Jialin, Yao Yuan. Handling domain drift and unknown fault detection in rotating machinery using few-shot learning with data scaling[J]. International Journal of Adaptive Control and Signal Processing, 2025, 39(7): 1591-1607.
[13] Hu Chenye, Wu Jingyao, Sun Chuang, et al. Unified flowing normality learning for rotating machinery anomaly detection in continuous time-varying conditions[J]. IEEE Transactions on Cybernetics, 2025, 55(1): 221-233.
[14] Guo Husheng, Li Hai, Ren Qiaoyan, et al. Concept drift type identification based on multi-sliding windows[J]. Information Sciences, 2022, 585: 1-23.
[15] Zhang Lingyu, Zhao Jiabao, Li Wei. Online and unsupervised anomaly detection for streaming data using an array of sliding windows and PDDs[J]. IEEE Transactions on Cybernetics, 2021, 51(4): 2284-2289.
[16] Grill J B, Strub F, Altché F, et al. Bootstrap your own latent a new approach to self-supervised learning[C]//Proceedings of the 34th International Conference on Neural Information Processing Systems. New York: ACM, 2020: 21271-21284.
[17] Shi Qiquan, Yin Jiaming, Cai Jiajun, et al. Block Hankel tensor ARIMA for multiple short time series forecasting[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(4): 5758-5766.
[18] Nectoux P, Gouriveau R, Medjaher K, et al. PRONOSTIA: an experimental platform for bearings accelerated degradation tests[C]//Proceedings of the IEEE International Conference on Prognostics and Health Management (PHM). Piscataway: IEEE, 2012: 1-8.
[19] Wang Biao, Lei Yaguo, Li Naipeng, et al. A hybrid prognostics approach for estimating remaining useful life of rolling element bearings[J]. IEEE Transactions on Reliability, 2020, 69(1): 401-412.
[20] Lu Weining, Li Yipeng, Cheng Yu, et al. Early fault detection approach with deep architectures[J]. IEEE Transactions on Instrumentation and Measurement, 2018, 67(7): 1679-1689.
[21] Guo Xiaoping, Liu Shiyang, Li Yuan. Fault detection of multi-mode processes employing sparse residual distance[J]. Acta Automatica Sinica, 2019, 45(3): 617-625. [郭小萍, 刘诗洋, 李元. 基于稀疏残差距离的多工况过程故障检测方法研究[J]. 自动化学报, 2019, 45(3): 617-625.]
[22] Yoon S, Lee Y, Lee J G, et al. Adaptive model pooling for online deep anomaly detection from a complex evolving data stream[C]//Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York: ACM, 2022: 2347-2357.

Similar References:

Memo

Last Update: 2026-04-17