水利学报2024，Vol.55Issue(7)：862-873,12.DOI:10.13243/j.cnki.slxb.20230790
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
摘要
Abstract
In order to improve the accuracy of shafting vibration fault diagnosis of hydropower units,a new diag-nostic 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 ro-bustness 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 origi-nal signal and introducing two multiscale entropies to compare with TSMSE,the anti-noise performance and robust-ness of TSMSE are analyzed.The results show that the stability and anti-noise performance of TSMSE feature ex-traction 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 veri-fies the good performance of the proposed model and provides a new scientific method for fault diagnosis of hydro-power units.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Key words
hydroelectric generating set/feature extraction/TSMSE/IOOA-BILSTM/fault diagnosis分类
信息技术与安全科学引用本文复制引用
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):862-873,12.基金项目
国家自然科学基金项目(52079059) （52079059）
