| 张兼博,李想,曾云,唐跨纪.TSMSE结合IOOA-BiLSTM的水电机组轴系故障诊断方法[J].水利学报,2024,55(7):862-873 |
| TSMSE结合IOOA-BiLSTM的水电机组轴系故障诊断方法 |
| TSMSE combined with IOOA-BILSTM for the fault diagnosis method of hydropower unit shafting |
| 投稿时间：2023-12-17 |
| DOI：10.13243/j.cnki.slxb.20230790 |
| 中文关键词: 水电机组 特征提取 时移多尺度样本熵 IOOA-BiLSTM 故障诊断 |
| 英文关键词: hydroelectric generating set feature extraction TSMSE IOOA-BILSTM fault diagnosis |
| 基金项目:国家自然科学基金项目(52079059) |
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| 中文摘要: |
| 为提高水电机组轴系振动故障诊断的准确率，本文提出了一种新的诊断方法。首先，基于完全自适应噪声集合经验模态分解(CEEMDAN)进行振动信号预处理;其次，基于时移多尺度思想，引入时移多尺度样本熵(TSMSE)模型，以克服传统多尺度样本熵鲁棒性差以及粗粒化不足的问题;最终，将TSMSE提取的故障特征集输入经过改进鱼鹰算法(IOOA)优化的双向长短时记忆网络(BiLSTM)，进行故障特征分类。通过对原始信号添加SNR=5 dB噪声并引入两种特征熵与TSMSE对比，分析TSMSE的抗噪性能。仿真实验表明，在给定数据集下TSMSE特征提取能力明显优于另外两种方法。同时，所提故障诊断模型应用在原始信号和含噪信号两种情况下，分别取得了100%以及97.22%的准确率，验证了所提模型的良好性能，为水电机组故障诊断提供新的科学方法。 |
| 英文摘要: |
| In order to improve the accuracy of shafting vibration fault diagnosis of hydropower units，a new diagnostic method is proposed.Firstly，the vibration signal decomposition was carried out based on the CEEMDAN.Secondly，based on the idea of time-shifted and multi-scale，a TSMSE model is proposed to overcome the poor robustness and lack of coarse granulation of traditional MSE.Finally，the fault feature set extracted by TSMSE was input into the BiLSTM optimized by IOOA for fault feature classification.With adding SNR=5 dB noise to the original signal and introducing two multiscale entropies to compare with TSMSE，the anti-noise performance and robustness of TSMSE are analyzed.The results show that the stability and anti-noise performance of TSMSE feature extraction are obviously better than the other two in a given data set.At the same time，the accuracy of the proposed fault diagnosis model is 100% and 97.22% respectively in the case of original signal and noisy signal，which verifies the good performance of the proposed model and provides a new scientific method for fault diagnosis of hydropower units. |
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