Abstract：The mechanical performance stability of vacuum circuit breakers (VCBs) is crucial for the long-term reliable operation of power systems. However, existing methods for evaluating mechanical performance degradation remain limited in the following respects. (1) Since the mechanical operation of a VCB involves multiple subsystems, its vibration signals contain rich information about the actions of these subsystems. Studying a single subsystem alone is insufficient to comprehensively characterize the mechanical performance degradation of the circuit breaker operating mechanism. (2) Most existing methods produce discrete diagnostic outputs, which limits their ability to quantify and track the degradation process. (3) Circuit breakers operate infrequently in actual distribution network operations, and factors such as delayed sensor deployment and premature equipment retirement result in a limited number of full-lifecycle monitoring data samples. To address these issues, this paper proposes a dynamic evaluation method for mechanical performance degradation in VCBs based on the vibration signal feature prediction and tensor fusion. First, the short-time energy algorithm is employed to detect sudden changes in signal energy and to extract features reflecting the conditions of the electromagnetic trigger unit, transmission mechanism, and spring mechanism. Secondly, due to the limited number of operations in applications and the difficulty of acquiring large-scale life-cycle data, functional principal component analysis (FPCA) combined with Bayesian inference is applied to dynamically predict the degradation trends of the extracted features under small-sample conditions. Finally, to exploit the coupling and correlations among degradation information across different subsystems, tensor fusion integrates the three features into a high-dimensional framework. Thus, a fused vibration degradation indicator is constructed to represent the circuit breaker’s mechanical performance. The following conclusions can be drawn. (1) Installing vibration sensors near the contact connection region of the circuit breaker facilitates the acquisition of clear and stable signals. Combined with the short-time energy algorithm, these signals enable the sensitive detection of abrupt energy changes. Accordingly, degradation information of the electromagnetic trigger unit, transmission mechanism, and spring mechanism is extracted, and the effectiveness of vibration signals in monitoring the mechanical degradation of VCBs is verified. (2) The small-sample degradation model based on FPCA can maintain high modeling accuracy, overcoming the unknown functional forms of the degradation process and the subjectivity introduced by artificial assumptions. By incorporating Bayesian inference to dynamically update the model parameters, the prediction accuracy can be improved as the number of operations increases. The prediction results for each feature achieve R2>0.98, with RMSE, MAE, and MSE all below 0.1. (3) By performing tensor fusion on the features extracted from the three subsystems of the operating mechanism, the proposed method preserves the individual degradation information while extracting high-dimensional correlation features, thus characterizing the mechanical performance degradation of the breaker operating mechanism. Compared with other fusion methods, the proposed fusion indicator achieves the highest similarity to the offline measurement of the complete closing time T3, with a correlation coefficient of R2= 0.973 2. (4) Taking the number of operations corresponding to the rated upper limit of the complete closing time T3=70 ms as a reference, the value of the fused vibration degradation indicator at this point is defined as the failure criterion, and the replacement threshold is set to 0.85. The results show that the maximum deviation between the predicted and actual curves at the threshold point is no more than 220 operations, representing only 0.9% of the full life cycle. The proposed method enables high-precision prediction of circuit breaker degradation states under small-sample conditions and provides a reliable quantitative criterion for guiding maintenance and overhaul strategies.
赵洪山, 钱亚楠, 李西备, 林诗雨. 基于振动信号特征预测与张量融合的真空断路器机械性能退化动态评估方法[J]. 电工技术学报, 2026, 41(6): 2086-2100.
Zhao Hongshan, Qian Yanan, Li Xibei, Lin Shiyu. Dynamic Evaluation Method of Mechanical Performance Degradation in Vacuum Circuit Breakers Via Vibration Signal Feature Prediction and Tensor Fusion. Transactions of China Electrotechnical Society, 2026, 41(6): 2086-2100.
[1] 张登奎, 张立岩, 李志兵, 等. 基于经验小波变换的真空断路器关合预击穿燃弧时间分析[J]. 电工技术学报, 2024, 39(增刊1): 106-116. Zhang Dengkui, Zhang Liyan, Li Zhibing, et al.Analysis of pre-strike arcing time for vacuum circuit breaker closure based on empirical wavelet transform[J]. Transactions of China Electrotechnical Society, 2024, 39(S1): 106-116. [2] 董华军, 孙鹏, 李东恒, 等. 考虑电动斥力的真空断路器刚柔耦合系统机械特性研究[J]. 电机与控制学报, 2023, 27(3): 147-157. Dong Huajun, Sun Peng, Li Dongheng, et al.Research on structural characteristics of rigid-flexible coupling system of VCB considering electro-dynamic repulsion force[J]. Electric Machines and Control, 2023, 27(3): 147-157. [3] Franck C M, Hsu C C, Xiao Yu, et al.Transmission and distribution equipment: providing intelligent maintenance[J]. IEEE Power and Energy Magazine, 2023, 21(2): 18-29. [4] Zhang Chunwei, Mousavi A A, Masri S F, et al.Vibration feature extraction using signal processing techniques for structural health monitoring: a review[J]. Mechanical Systems and Signal Processing, 2022, 177: 109175. [5] Zhou Nan, Xu Yan, Cho S, et al.A systematic review for switchgear asset management in power grids: condition monitoring, health assessment, and maintenance strategy[J]. IEEE Transactions on Power Delivery, 2023, 38(5): 3296-3311. [6] 杨秋玉, 阮江军, 黄道春, 等. 基于振动信号时频图像识别的高压断路器分闸缓冲器状态评估[J]. 电工技术学报, 2019, 34(19): 4048-4057. Yang Qiuyu, Ruan Jiangjun, Huang Daochun, et al.Opening damper condition evaluation based on vibration time-frequency images for high-voltage circuit breakers[J]. Transactions of China Electrotechnical Society, 2019, 34(19): 4048-4057. [7] Sun Shuguang, Wen Zhitao, Du Taihang, et al.Remaining life prediction of conventional lowvoltage circuit breaker contact system based on effective vibration signal segment detection and MCCAE-LSTM[J]. IEEE Sensors Journal, 2021, 21(19): 21862-21871. [8] 豆龙江, 何玉灵, 万书亭, 等. 基于振动信号的高压断路器弹簧疲劳程度检测方法[J]. 电工技术学报, 2022, 37(24): 6420-6430. Dou Longjiang, He Yuling, Wan Shuting, et al.Detecting method of high voltage circuit breaker spring fatigue based on vibration signal[J]. Transactions of China Electrotechnical Society, 2022, 37(24): 6420-6430. [9] 刘会兰, 常庚垚, 赵书涛, 等. 溯源弹簧形变过程的断路器振动信号递归量化分析辨识方法[J]. 电工技术学报, 2024, 39(8): 2567-2577. Liu Huilan, Chang Gengyao, Zhao Shutao, et al.Circuit breaker state identification method based on tracing spring deformation process and recurrence quantitative analysis of vibration signals[J]. Transactions of China Electrotechnical Society, 2024, 39(8): 2567-2577. [10] Cao Shi, Zhao Tong, Wang Gang, et al.A mechanical defect localization and identification method for highvoltage circuit breakers based on the segmentation of vibration signals and extraction of chaotic features[J]. Sensors, 2023, 23(16): 7201. [11] 黄南天, 陈怀金, 林琳, 等. 基于S变换和极限学习机的高压断路器机械故障诊断[J]. 高压电器, 2018, 54(6): 74-80. Huang Nantian, Chen Huaijin, Lin Lin, et al.Mechanical fault diagnosis of high voltage circuit breakers based on S-transform and extreme learning machine[J]. High Voltage Apparatus, 2018, 54(6): 74-80. [12] 陈欣昌, 冯玎, 林圣. 基于深度自编码网络的高压断路器操作机构机械故障诊断方法[J]. 高电压技术, 2020, 46(9): 3080-3088. Chen Xinchang, Feng Ding, Lin Sheng.Mechanical fault diagnosis method of high voltage circuit breaker operating mechanism based on deep auto-encoder network[J]. High Voltage Engineering, 2020, 46(9): 3080-3088. [13] 谭佳明, 叶奕君, 李国玉, 等. 基于振动信号的交流中压真空断路器故障诊断技术研究[J]. 高压电器, 2019, 55(11): 218-226. Tan Jiaming, Ye Yijun, Li Guoyu, et al.Fault diagnosis technology of AC medium-voltage vacuum circuit breaker based on vibration signal[J]. High Voltage Apparatus, 2019, 55(11): 218-226. [14] 万书亭, 马晓棣, 陈磊, 等. 基于振动信号短时能熵比与DTW的高压断路器状态评估及故障诊断[J]. 高电压技术, 2020, 46(12): 4249-4257. Wan Shuting, Ma Xiaodi, Chen Lei, et al.State evaluation and fault diagnosis of high-voltage circuit breaker based on short-time energy entropy ratio of vibration signal and DTW[J]. High Voltage Engineering, 2020, 46(12): 4249-4257. [15] Qu Yuehan, Zhao Hongshan, Zhao Shice, et al.Power transformer oil-paper insulation degradation modelling and prediction method based on functional principal component analysis[J]. IET Science, Measurement & Technology, 2022, 16(8): 441-453. [16] 曲岳晗, 赵洪山, 程晶煜, 等. 基于物联感知数据和张量融合的电力变压器绕组绝缘劣化评估方法[J]. 电工技术学报, 2024, 39(4): 1208-1220. Qu Yuehan, Zhao Hongshan, Cheng Jingyu, et al.Evaluation method for power transformer winding insulation degradation based on IoT sensing data and tensor fusion[J]. Transactions of China Electrotechnical Society, 2024, 39(4): 1208-1220. [17] Kossaifi J, Lipton Z C, Kolbeinsson A, et al.Tensor regression networks[J]. The Journal of Machine Learning Research, 2020, 21(1): 4862-4882. [18] 刘会兰, 许文杰, 赵书涛, 等. 基于振动信号时间历程和自适应谱融合的断路器操动机构状态辨识方法[J]. 高电压技术, 2023, 49(5): 1860-1869. Liu Huilan, Xu Wenjie, Zhao Shutao, et al.State identification method of circuit breaker operating mechanism based on time course waveform and adaptive spectrum fusion analysis of vibration signal[J]. High Voltage Engineering, 2023, 49(5): 1860-1869. [19] Meng Yongpeng, Jia Shenli, Shi Zongqian, et al.The detection of the closing moments of a vacuum circuit breaker by vibration analysis[J]. IEEE Transactions on Power Delivery, 2006, 21(2): 652-658. [20] 张彬, 章立军, 罗久飞, 等. 基于函数主元分析的多阶段退化过程建模与预测[J]. 仪器仪表学报, 2019, 40(7): 30-38. Zhang Bin, Zhang Lijun, Luo Jiufei, et al.Multiphase degradation process modeling and prediction based on functional principal component analysis[J]. Chinese Journal of Scientific Instrument, 2019, 40(7): 30-38. [21] Zadeh A, Chen M, Poria S, et al.Tensor fusion network for multimodal sentiment analysis[A]. arXiv, 2017. [22] Asghar Razi-Kazemi A, Niayesh K. Condition monitoring of high voltage circuit breakers: past to future[J]. IEEE Transactions on Power Delivery, 2021, 36(2): 740-750. [23] 赵陈琛, 张国钢, 刘洁, 等. 基于物理信息和迁移网络的高压断路器弹簧操动机构机械参数辨识及故障诊断[J/OL]. 电工技术学报, 2025: 1-13. (202507-14). https://link.cnki.net/doi/10.19595/j.cnki.10006753.tces.250521. Zhao Chenchen, Zhang Guogang, Liu Jie, et al. Mechanical parameter identification and fault diagnosis of spring operating mechanism of high-voltage circuit breaker based on physical information and transfer network[J/OL]. Transactions of China Electrotechnical Society, 2025: 1-13. (2025-07-14). https:// link.cnki.net/doi/10.19595/j.cnki.1000-6753.tces.250521. [24] 范兴明, 许洪华, 张思舜, 等. 基于改进蜣螂优化算法深度混合核极限学习机的高压断路器故障诊断[J]. 电工技术学报, 2025, 40(12): 3994-4003. Fan Xingming, Xu Honghua, Zhang Sishun, et al.Fault diagnosis of high voltage circuit breaker based on improved dung beetle optimizer algorithm deep hybrid kernel extreme learning machine[J]. Transactions of China Electrotechnical Society, 2025, 40(12): 3994-4003. [25] 郭博文, 李楠, 李松原, 等. 断路器弹簧操动机构分/合闸弹簧的状态诊断[J]. 电气技术, 2021, 22(12): 57-62. Guo Bowen, Li Nan, Li Songyuan, et al.State diagnosis for closing/opening spring in the spring operating mechanism of circuit breaker[J]. Electrical Engineering, 2021, 22(12): 57-62. [26] Kang Y H, Huang H C, Yang Boya.Optimal design and dynamic analysis of a spring-actuated camlinkage mechanism in a vacuum circuit breaker[J]. Machines, 2023, 11(2): 150. [27] 吴淑窈, 马少华. 真空断路器操动机构动力学仿真及推杆疲劳寿命分析[J]. 电器与能效管理技术, 2022(11): 14-18, 25. Wu Shuyao, Ma Shaohua.Dynamic simulation on operating mechanism of vacuum circuit breaker and fatigue life analysis on push rod[J]. Electrical & Energy Management Technology, 2022(11): 14-18, 25. [28] Liu Huilan, Li Xiang, Zhao Shutao, et al.Design and analysis of a new feature optimisation method for circuit breakers defect identification[J]. IET Electric Power Applications, 2024, 18(11): 1469-1479. [29] Weng Liting, Xiao Jiangtao, Zhang Zhaochuang, et al.Online monitoring method for opening and closing time of 10 kV spring energy storage circuit breaker based on transient electrical signal characteristic point marking and self-calibration[J]. Energies, 2024, 17(24): 6436. [30] Zhao Hongshan, Qian Yanan, Qu Yuehan.Mechanical performance degradation modelling and prognosis method of high-voltage circuit breakers considering censored data[J]. IET Science, Measurement & Technology, 2025, 19(1): e12235. [31] 刘岗. 基于时频分析与机器视觉的断路器机械特性测量方法研究[D]. 合肥: 合肥工业大学, 2023. Liu Gang.Research on the measurement method of mechanical characteristics of circuit breaker based on time-frequency analysis and machine vision[D]. Hefei: Hefei University of Technology, 2023.
2026, Vol. 41 
