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

doi:10.19595/j.cnki.1000-6753.tces.250438

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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.

Key words： Vacuum circuit breaker vibration signal mechanical performance degradation functional principal component analysis tensor fusion

Received: 20 March 2025

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TM561

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	Zhao Hongshan
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Zhao Hongshan,Qian Yanan,Li Xibei等. Dynamic Evaluation Method of Mechanical Performance Degradation in Vacuum Circuit Breakers Via Vibration Signal Feature Prediction and Tensor Fusion[J]. Transactions of China Electrotechnical Society, 2026, 41(6): 2086-2100.

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