| 余洪伍,汤占军,马锦雄.基于声纹特征融合的风机叶片异常识别方法[J].电子测量与仪器学报,2024,38(11):99-108 |
| 基于声纹特征融合的风机叶片异常识别方法 |
| Wind turbine blade anomaly recognition method based on sound feature fusion |
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| DOI： |
| 中文关键词: 梅尔频率倒谱系数 特征融合 互补集合经验模态分解 故障诊断 神经网络 |
| 英文关键词:mel frequency cepstrum coefficients feature fusion complementary ensemble empirical mode decomposition fault identification neural networks |
| 基金项目:国家能源集团科技项目(CSIEKJ230700104)资助 |
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| 摘要点击次数: 485 |
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| 中文摘要: |
| 为实现风机叶片异常时的准确监测，提出一种将互补集合经验模态分解与风机叶片声纹特征进行结合的方法。首先，采集到4种异常工作状态以及正常运行状态下的风机叶片的声纹数据，对其进行降噪、分帧和加窗操作的预处理，通过实验比对，选择互补集合经验模态分解算法进行声纹数据的二次降噪，其次，对二次降噪后的帧信号进行模态分解提取模态分量，通过计算模态分量的皮尔逊相关系数筛选有效的模态分量，并对每层的模态分量提取梅尔频率倒谱系数、线性预测倒谱系数、gammatone倒谱系数、短时能量、以及短时平均过零率特征。最后，基于这些特征组合，采用支持向量机、朴素贝叶斯以及神经网络作为故障分类模型对声纹数据进行识别。研究结果表明，基于上述5种声纹特征组合在参数寻优后的神经网络模型下可以实现叶片异常的准确识别，识别准确率达到97.5%，该模型对早期异常的风机叶片识别效果较好，具有较好的泛化性能。 |
| 英文摘要: |
| In order to achieve accurate monitoring of abnormal wind turbine blades, a method combining complementary ensemble empirical mode decomposition with the sound features of wind turbine blades was proposed. Firstly, the voiceprint data of four kinds of fan blades under abnormal working conditions and normal operating conditions are collected and pre-processed for noise reduction, frame division and window addition. Through experimental comparison, the complementary ensemble empirical mode decomposition algorithm is selected for secondary noise reduction of voiceprint data. Secondly, the modal decomposition of frame signals after secondary noise reduction is carried out to extract modal components. The effective modal components were selected by calculating the Pearson correlation coefficient of the modal components, and the characteristics of mel frequency cepstrum coefficient, linear prediction cepstrum coefficient, gammatone cepstrum coefficient, short-time energy and short-time mean zero crossing rate were extracted for each layer of modal components. Finally, based on these feature combinations, support vector machine, naive Bayes and neural network are used as fault classification models to identify voicing data. The research results show that the neural network model based on the combination of the above five vowels features and the parameter optimization can achieve the accurate recognition of blade anomalies, with the recognition accuracy of 97.5%. The model has a good recognition effect on early abnormal fan blades, and has good generalization performance. |
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