摘要
针对水电机组早期故障信号信噪比低的问题,本文将奇异值分解(SVD)和深度置信网络(DBN)相结合进行故障诊断。首先,利用包含噪声的振动信号构造Hankel矩阵,对其进行奇异值分解,采用奇异值差分谱法选取有效奇异值进行相空间重构,实现降噪的目的;然后,对降噪后的振动信号进奇异值分解,用所得的整个奇异值序列构造特征向量;最后,建立深度置信网络分类器模型,实现水电机组的故障诊断。同时,将所提方法与BP神经网络,多分类支持向量机进行对比。结果表明,本文所提方法能够更加可靠高效地识别故障类型,具有一定的应用价值。
In order to solve the problem of low signal-to-noise ratio of the early faults of hydroelectric sets, Singular Value Decomposition(SVD) and Deep Belief Network(DBN) are combined for fault diagnosis in this study. First, a Hankel matrix was constructed by using a vibration signal containing noise to decompose its singular values;and effective singular values, selected through the singular value difference spectrum method, were used to reconstruct a phase space and achieve noise reduction. Then, singular value decomposition was applied to the signals denoised, and the resulted singular value sequence was used to construct a feature vector. Finally, a DBN classifier model was developed to realize the fault diagnosis of hydroelectric sets and compared with BP neural network and multi-class support vector machine. The results show this method can identify the fault type of hydroelectric sets more reliably and efficiently.
作者
李辉
范智超
李华
白亮
贾嵘
罗兴锜
LI Hui;FAN Zhichao;LI Hua;BAI Liang;JIA Rong;LUO Xingqi(School of Electrical Engineering,Xi'an University of Technology,Xi'an 710048;State Grid Weinan Electric Power Company,Weinan,Shaanxi 714000;State Grid Shaanxi Electric Power Research Institute,Xi'an 710100;Institute of Water Resources&Hydropower Engineering,Xi'an University of Technology,Xi'an 710048)
出处
《水力发电学报》
EI
CSCD
北大核心
2020年第12期104-112,共9页
Journal of Hydroelectric Engineering
基金
国家自然科学基金(51779206)
陕西省教育厅科研计划项目(17JK0570)
关键词
水电机组
故障诊断
奇异值分解
深度置信网络
hydroelectric sets
fault diagnosis
singular value decomposition
deep belief network
