| 李卓睿,陈杨,马军.尺度形态滤波的风电机组齿轮故障特征提取方法[J].电子测量与仪器学报,2026,40(4):144-153 |
| 尺度形态滤波的风电机组齿轮故障特征提取方法 |
| Extraction for wind turbine based on scale morphological filtering |
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| DOI： |
| 中文关键词: 风电机组 齿轮 故障特征提取 自适应多尺度形态滤波 SE尺度 |
| 英文关键词:wind turbines gear fault feature extraction adaptive multi-scale morphological filtering SE scale |
| 基金项目:国家自然科学基金（62563018）、云南省基础研究计划项目（202401AW070014，202401BN070001-007）、云南省智能控制与应用重点实验室开放基金（2025ICA06）、校人培基金（KKZ3202503076）项目资助 |
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| Author | Institution |
| Li Zhuorui | 1.Faculty of Information Engineering and Automation，Kunming University of Science and Technology, Kunming 650500,China;
2.Yunnan Key Laboratory of Intelligent Control and Application, Kunming 650500,China |
| Chen Yang | 1.Faculty of Information Engineering and Automation，Kunming University of Science and Technology, Kunming 650500,China;
2.Yunnan Key Laboratory of Intelligent Control and Application, Kunming 650500,China |
| Ma Jun | 1.Faculty of Information Engineering and Automation，Kunming University of Science and Technology, Kunming 650500,China;
2.Yunnan Key Laboratory of Intelligent Control and Application, Kunming 650500,China |
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| 摘要点击次数: 280 |
| 全文下载次数: 140 |
| 中文摘要: |
| 风电机组齿轮因长期处于多噪声、高转速工况下运行，振动信号呈现非线性特性，致形态学滤波中单尺度滤波方法难以准确有效提取故障特征的问题，融合复合故障特征指标选取的最优结构元素（structuring element，SE）尺度，提出一种自适应多尺度组合差分积形态滤波（adaptive multi-scale composite differentiation product morphological filter，AMCDPMF）的齿轮故障特征提取方法。首先，引入3种形态学差分算子，构造多尺度组合差分积滤波器（multi-scale composite differentiation product morphological filter，MCDPMF）；其次，引用复合故障特征指标自适应选择多尺度滤波信号的最优SE尺度；最后，融合MCDPMF和最优SE尺度，计算每个尺度的权重系数，构造AMCDPMF滤波器实现自适应提取信号特征。结果表明，AMCDPMF能够有效的提取齿轮仿真和实验信号故障特征，相较于单尺度和3种经典多尺度滤波器，AMCDPMF故障能量比（energy ratio，ER）和改进的信噪比（improvement in signal to-noise ratio, ISNR）分别提高了5.15%、3.5 dB，提取齿轮故障特征能力更佳，有效提升风电机组运行的可靠性，推动可再生能源的高效利用。 |
| 英文摘要: |
| The gears of wind turbines operate under multiple noise and high-speed conditions for a long time, and the vibration signals exhibit nonlinear characteristics, which makes it difficult for single scale filtering methods in morphological filtering to accurately and effectively extract fault features, a method for gear fault feature extraction using the adaptive multi-scale composite differentiation product morphological filter (AMCDPMF) is proposed. Firstly, three morphological differential operators are introduced to construct the muti-scale composite differentiation product morphological filter (MCDPMF). Secondly, an adaptive selection of the optimal structural element (SE) scale for multi-scale filtering signals is achieved using a composite fault feature index. Subsequently, by integrating the MCDPMF with the optimal SE scale, the AMCDPMF filter is formulated to enable adaptive filtering, thereby effectively extracting fault features from simulated and experimental signals of gear dynamic models. Finally, through comparative analysis with single-scale filters and three classical multi scale filters, The energy ratio (ER) and improvement in signal-to-noise ratio (ISNR) of AMCDPMF have increased by 5.15% and 3.5 dB, respectively. The ability to extract gear fault features is better, effectively improving the reliability of wind turbine operation and promoting the efficient utilization of renewable energy. |
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