A Denoising Method for AC Contactor Operating State Feature Data Based on Adaptive Multi-Scale Morphological Filter
Liu Shuxin, Qi Xinzhi, Ming Xin, Xing Chaojian, Xu Jing
Key Laboratory of Special Electric Machines and High Voltage Apparatus in the Ministry of Education Shenyang University of Technology Shenyang 110870 China
Abstract：High-quality operating state feature data is crucial for the reliability analysis of AC contactors and serves as the foundation for research on residual life prediction and state identification of AC contactors. However, factors such as the singularity of the feature extraction program, electrical interference, and measurement errors often result in a significant amount of noise in the feature data. Moreover, traditional noise reduction technologies struggle to effectively reduce noise content due to issues of modal aliasing and low denoising resolution in feature data. Therefore, this paper proposes a denoising method for feature data based on the adaptive multi-scale morphological filter (AMMF). Initially, this paper employs curve envelopes to decompose feature data into multi-scale components and introduces Triangle thresholds and adaptive structural elements to enhance traditional morphological filtering techniques. Accordingly, the method can adapt to differences in the distribution of various scale spectra, thereby constructing the AMMF denoising model. Subsequently, the autocorrelation function characteristics of the components are utilized to identify noisy components. The AMMF is then applied to extract the effective frequency bands of these noisy components, which are inversely transformed and reconstructed into feature data. Finally, this paper compares the proposed method with six denoising techniques, using signal-to-noise ratio, cross-correlation coefficient, and average autocorrelation coefficient as evaluation metrics. The results demonstrate that the proposed method effectively reduces the noise content in AC contactor feature data. Regarding the autocorrelation coefficient metric, the temporal correlation of feature data processed by AMMF is significantly enhanced. Specifically, the full-period average autocorrelation coefficients of four critical feature data—bounce time, contact resistance, release time, and closing time—improve from 0.027, 0.098, 0.035, and 0.011 to 0.154, 0.166, 0.106, and 0.079, respectively. Concurrently, the signal-to-noise ratios of these four datasets after processing reach 23.1, 28.1, 33.27, and 36.34 dB, respectively, with correlation coefficients relative to contactor life amounting to 0.68, 0.61, -0.58, and 0.48, respectively. These numerical values of the three metrics indicate that the AMMF outperforms comparison methods in processing AC contactor operating state feature data. The following conclusions are drawn from the case analysis. (1) The improved morphological filtering technique mitigates the issues of modal aliasing and low denoising resolution in feature data. (2) The method of determining the signal-to-noise boundary using the autocorrelation coefficient can effectively reduce the subjectivity of traditional methods in identifying noisy components. (3) Compared to traditional denoising techniques, the proposed method exhibits significant advantages in noise reduction and information retention.
刘树鑫, 祁新智, 明欣, 邢朝健, 许静. 基于自适应多尺度形态学滤波的交流接触器运行状态特征数据降噪方法[J]. 电工技术学报, 2025, 40(14): 4655-4667.
Liu Shuxin, Qi Xinzhi, Ming Xin, Xing Chaojian, Xu Jing. A Denoising Method for AC Contactor Operating State Feature Data Based on Adaptive Multi-Scale Morphological Filter. Transactions of China Electrotechnical Society, 2025, 40(14): 4655-4667.
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