| 陈飞,王斌,周东东,赵志高,丁晨,陈帝伊.融合改进符号动态熵和随机配置网络的水电机组轴系故障诊断方法[J].水利学报,2022,53(9):1127-1139 |
| 融合改进符号动态熵和随机配置网络的水电机组轴系故障诊断方法 |
| A fault diagnosis method for shaft system of hydropower units based on improved symbolic dynamic entropy and stochastic configuration network |
| 投稿时间：2022-02-09 |
| DOI：10.13243/j.cnki.slxb.20220083 |
| 中文关键词: 水电机组 故障诊断 多元多尺度符号动态熵 随机配置网络 特征提取 |
| 英文关键词: hydropower units fault diagnosis multivariate multiscale symbolic dynamic entropy stochastic configuration network feature extraction |
| 基金项目:国家自然科学基金项目(51509210);陕西省重点研发计划项目(2021NY-181) |
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| 中文摘要: |
| 现有水电机组轴系故障诊断研究主要建立在单一传感器振动信号数据的基础上,存在故障信息缺失和传感器测点选择困难等问题。为此,提出了一种基于精细复合多元多尺度符号动态熵(RCMMSDE)和随机配置网络(SCN)相结合的水电机组轴系故障诊断方法。首先,将精细复合技术引入RCMMSDE模型中,改进了传统多元多尺度熵粗粒化不足的问题。然后,通过提取水电机组不同传感器振动信号的RCMMSDE值作为故障特征。最终,将故障特征输入SCN网络实现水电机组轴系故障的准确识别。仿真结果表明,RCMMSDE-SCN模型在两个不同数据集上分别取得了97.58%和99.17%的诊断率,验证了所提模型具有良好的诊断性能。同时,对比不同诊断模型在多元传感器信号和单一传感器信号两种不同情景下的诊断情况,表明融合多元振动信号可以有效改善水电机组轴系故障诊断模型的识别性能。本研究为融合水电机组多元传感器振动信号故障诊断提供了一种新的方法,具有良好的借鉴价值。 |
| 英文摘要: |
| The existing research on shafting fault diagnosis of hydropower units is mainly based on the vibration signal data of a single sensor. There are some problems such as lack of fault information and difficult in selecting sensor measurement points. Therefore, a shafting fault diagnosis method for hydropower units based on the combination of refined composite multivariate multiscale symbolic dynamic entropy (RCMMSDE) and stochastic configuration network (SCN) is proposed in this paper. First, the refined composite technique is introduced into RCMMSDE model to improve the problem of insufficient coarse-graining of traditional multivariate multiscale entropy. Then, the RCMMSDE values of vibration signals from different sensors are extracted as fault features. Finally, the fault features are input into SCN network to realize the accurate shafting fault identification of hydropower units. Simulation results show that the RCMMSDE-SCN model achieves the highest diagnostic rates of 97. 58% and 99.17% on two different data sets respectively, which verifies the good diagnostic performance of the proposed model. At the same time, the diagnosis performance of different diagnosis models under different scenarios of multiple sensor signals and single sensor signals is compared, which indicates that the fusion of multiple vibration signals can effectively improve the identification performance of hydropower unit shafting fault diagnosis model. This study provides a new method for multi-sensor vibration signals of hydropower units, and has good reference value. |
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