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为了提高水电机组导轴承振动信号特征提取的敏感性与自适应性，提出了一种基于复信号模态分解构建特征矩阵的水电机组导轴承振动信号特征提取方法。选取同一截面内正交的两组摆度信号构建复信号，分离其正负频率分量，分别对正负频率分量时域信号进行逐次变分模态分解，计算各阶模态分量的关键时、频域特征指标并构建特征矩阵，以有效区分不同机组状态。该方法提供了新的信号处理框架，其复信号构建策略与模态分解机制对水电机组导轴承系统振摆分析具有应用价值。
Abstract：To improve the sensitivity and adaptability of feature extraction for the vibration signals of the guide bearings of hydropower units, a feature extraction method based on the construction of a feature matrix through the decomposition of complex signal modes is proposed. Two orthogonal swing signals from the same cross-section are chosen to form a complex signal, from which positive and negative frequency components are separated. Iterative variational mode decomposition is then applied to the time-domain signals of each component. The key time-and frequency-domain indicators of the decomposed modal components are calculated and integrated into a feature matrix, enabling effective discrimination of different operating states of the unit. The proposed method provides a new signal-processing framework, and its complex signal construction strategy coupled with the modal decomposition mechanism demonstrates significant potential for swing analysis of guide bearing systems of hydropower units.
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基本信息:
DOI：10.20040/j.cnki.1000-7709.2026.20251628
中图分类号:TV738
引用信息:
[1]刘东,孔丽君,胡文庆,等.基于CSVMD的水电机组导轴承故障振动信号特征提取[J].水电能源科学,2026,44(08):203-208+186.DOI:10.20040/j.cnki.1000-7709.2026.20251628.
基金信息:
中国长江电力股份有限公司科研项目(Z152302031)
2025-09-16
2025
2026-06-02
2025-10-17
2025
1
2026-01-16
2026-01-16
2026-01-16