水利学报2024，Vol.55Issue(6)：744-755,12.DOI:10.13243/j.cnki.slxb.20230694
基于UPEMD融合RCMCSE和ALWOA-BP的水电机组故障诊断
Fault diagnosis of hydropower units based on UPEMD integrating RCMCSE and ALWOA-BP
摘要
Abstract
The diagnosis of vibration signals in hydropower units is crucial to the safe and stable operation of the u-nits.This article proposes a fault diagnosis method for hydropower units based on uniform phase empirical mode de-composition(UPEMD)combined with refined composite multiscale cosine similarity entropy(RCMCSE)and an im-proved whale optimization algorithm(ALWOA)optimized back propagation neural network(BP).The UPEMD is used to decompose the original signal,and then a WOA-BP fault diagnosis model is established.To solve the problem of WO A algorithm quickly falling into local optimum and premature convergence,an adaptive weight and Levy flight are used to optimize the WO A algorithm.Experimental results show that the accuracy of this method reached 100％.To explore the noise resistance performance of the proposed model,a noise with a signal-to-noise ratio of 2 dB was introduced for re-analysis,and the diagnostic result was 94.44％,which was significantly better than other unoptimized models.This study can provide a valuable complement to existing fault diagnosis methods for hydropower units.关键词
水电机组/精细复合多尺度熵/余弦相似熵/ALWOA-BP/故障诊断Key words
hydropower units/refined composite multiscale entropy/cosine similarity entropy/ALWOA-BP/fault diagnosis分类
信息技术与安全科学引用本文复制引用
李想,钱晶,曾云..基于UPEMD融合RCMCSE和ALWOA-BP的水电机组故障诊断[J].水利学报,2024,55(6):744-755,12.基金项目
国家自然科学基金项目(52079059,52269020) （52079059,52269020）
