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多源信息融合的水轮机组振动测量方法

129    2021-09-23

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作者:谭丕成1, 万元2,3, 朱红平1, 胡边3, 曾辉斌1, 刘宇1

作者单位:1. 五凌电力有限公司近尾洲水电厂,湖南 衡阳 421127;
2. 湖南五凌电力科技有限公司,湖南 长沙 410004;
3. 国家电投集团水电产业创新中心,湖南 长沙 410004


关键词:振动测量;多源信息融合;频率测量;能量重心法


摘要:

水电机组设备作为水力发电的关键枢纽,其运行状态对生产发电效率、经济效益、设备安全起着重要作用。为实现在复杂条件下的对水轮机组振动的精确测量,该文提出一种基于多源信息融合的振动频率测量方法。采取基于互相关的多源融合算法,将多路传感器信号进行融合重构,实现对背景噪声与干扰信号的抑制,增强振动基频的信号强度。利用能量重心法处理重构信号,实现对振动基频频率的准确估计。通过理论推导,证明该文方法对干扰信号与背景噪声的抑制作用,通过振动台实验与水轮机组实测的方式与其他方法对比,验证该文所提方法的有效性和准确性。


Vibration measurement method of hydraulic turbine unit based on multi-source information fusion
TAN Picheng1, WAN Yuan2,3, ZHU Hongping1, HU Bian3, ZENG Huibin1, LIU Yu1
1. Jinweizhou Hydropower Plant, Wuling Power Co., Ltd., Hengyang 421127, China;
2. Hunan Wuling Power Technology Co., Ltd., Changsha 410004, China;
3. Hydropower Industry Innovation Center, State Power Investment Co., Ltd., Changsha 410004, China
Abstract: As a key hub of hydroelectric power generation, hydroelectric generator plays an important role in the power generation efficiency, economic benefits, and equipment safety. In order to achieve accurate measurement of hydraulic turbine vibration under complex conditions, this paper proposed a vibration frequency measurement method based on multi-source information fusion. A multi-source fusion algorithm based on cross-correlation is adopted to reconstruct the signals obtained from multiple sensors, which can suppress background noise and interference signals, and enhance the signal strength of the fundamental frequency of vibration. The energy centrobaric method is used to process the reconstructed signal to achieve an accurate estimation of the fundamental frequency of vibration. Through theoretical derivation, it is proved that proposed method can suppress the interference signal and background noise, and the effectiveness and accuracy of the proposed method are verified by comparing with other methods under the vibration table experiment and measurement of the water turbine unit.
Keywords: vibration measurement;multi-source information fusion;frequency estimation;energy centrobaric method
2021, 47(9):94-100  收稿日期: 2020-07-10;收到修改稿日期: 2020-08-20
基金项目: 国家电力投资集团有限公司科技资助项目(2019-12-WLD-KJ-X)
作者简介: 谭丕成(1973-),男,湖南长沙市人,高级工程师,硕士,从事水电厂运行、维护管理工作
参考文献
[1] 张德强. 水轮发电机检修系统的关键技术分析[J]. 水电与新能源, 2016(1): 49-52
[2] 陈国栋. 水轮机组振动的分析与处理[J]. 福建电力与电工, 1998(2): 23-5
[3] 陈珊珊, 杨耿杰. 水电机组振动故障诊断方法综述[J]. 电气技术, 2019, 20(6): 1-5
[4] 褚福磊, 卢文秀, 张伟, 等. 水泵水轮机组状态监测与故障诊断系统[J]. 水力发电, 1999, 2: 31-3
[5] 冯源, 葛新峰, 潘天航, 等. 基于小波变换的水电机组振动故障分析和特征提取[J]. 云南电力技术, 2014, 42(6): 1-4
[6] 陈春俊, 张振, 刘广. 轨道不平顺激扰下机车传动齿轮振动特性研究[J]. 中国测试, 2020, 46(6): 108-115
[7] 冯涛, 王杰, 方夏, 等. 基于CNN和声音时频特征图的微型振动马达故障判别[J]. 中国测试, 2019, 45(10): 120-7
[8] 李少波, 姚勇, 桂桂, 等. 基于CNN与多通道声学信号的齿轮故障诊断[J]. 中国测试, 2019, 45(10): 1-5
[9] 张虎, 谷丰收, 王铁, 等. 基于异常声发射信号的柴油机早期故障诊断[J]. 中国测试, 2018, 44(3): 28-32
[10] MIAO Y, ZHAO M, LIANG K, et al. Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal[J]. Renewable Energy, 2020, 151: 192-203
[11] PAWLIK P. The use of the acoustic signal to diagnose machines operated under variable load[J]. Archives Of Acoustics, 2020, 45(2): 263-270
[12] YIN A, YAN Y, ZHANG Z, et al. Fault diagnosis of wind turbine gearbox based on the optimized LSTM neural network with cosine loss[J]. Sensors, 2020, 20(8): 2339
[13] 丁康, 郑春松, 杨志坚. 离散频谱能量重心法频率校正精度分析及改进[J]. 机械工程学报, 2010, 46(5): 43-8
[14] SHAN X, TANG L, WEN H, et al. Analysis of vibration and acoustic signals for noncontact measurement of engine rotation speed[J]. Sensors, 2020, 20(3): 683
[15] OPPENHEIM A V, WILLSKY A S. Signals and systems[M]. New Jersey : Prentice Hall, 1983.