化工自动化及仪表
化工自動化及儀錶
화공자동화급의표
Control and Instruments in Chemical Industry
2015年
11期
1220-1225
,共6页
离心泵%汽蚀%压力脉动%奇异值分解%故障诊断%提升db4小波
離心泵%汽蝕%壓力脈動%奇異值分解%故障診斷%提升db4小波
리심빙%기식%압력맥동%기이치분해%고장진단%제승db4소파
centrifugal pump%cavitation%pressure fluctuation%SVD%fault diagnosis%lift“db4” wavelet
离心泵发生汽蚀故障时的空化流动会使内部流场发生改变，选择适当的算法对蕴含流场信息的出口压力脉动信号进行处理与分析，就可以判断离心泵是否发生汽蚀和汽蚀所处阶段。利用提升db4小波在频域与时域上优异的性能，将离心泵出口处的压力脉动信号分解到不同的时频子空间，通过构建小波重构系数矩阵保证时频信息的完备；随后对时频矩阵进行奇异值分解（ SVD）求取奇异值特征向量；再将所有得到的特征向量作为样本，对改进的BP神经网络进行训练，建立压力脉动信号到汽蚀不同阶段的映射。随机检测几种工况下的压力脉动信号，测试结果表明：对出口压力脉动信号进行小波奇异值分解，可以较好地识别离心泵的早期微弱汽蚀故障，抗干扰性和精确性优于传统诊断方法。
離心泵髮生汽蝕故障時的空化流動會使內部流場髮生改變，選擇適噹的算法對蘊含流場信息的齣口壓力脈動信號進行處理與分析，就可以判斷離心泵是否髮生汽蝕和汽蝕所處階段。利用提升db4小波在頻域與時域上優異的性能，將離心泵齣口處的壓力脈動信號分解到不同的時頻子空間，通過構建小波重構繫數矩陣保證時頻信息的完備；隨後對時頻矩陣進行奇異值分解（ SVD）求取奇異值特徵嚮量；再將所有得到的特徵嚮量作為樣本，對改進的BP神經網絡進行訓練，建立壓力脈動信號到汽蝕不同階段的映射。隨機檢測幾種工況下的壓力脈動信號，測試結果錶明：對齣口壓力脈動信號進行小波奇異值分解，可以較好地識彆離心泵的早期微弱汽蝕故障，抗榦擾性和精確性優于傳統診斷方法。
리심빙발생기식고장시적공화류동회사내부류장발생개변，선택괄당적산법대온함류장신식적출구압력맥동신호진행처리여분석，취가이판단리심빙시부발생기식화기식소처계단。이용제승db4소파재빈역여시역상우이적성능，장리심빙출구처적압력맥동신호분해도불동적시빈자공간，통과구건소파중구계수구진보증시빈신식적완비；수후대시빈구진진행기이치분해（ SVD）구취기이치특정향량；재장소유득도적특정향량작위양본，대개진적BP신경망락진행훈련，건립압력맥동신호도기식불동계단적영사。수궤검측궤충공황하적압력맥동신호，측시결과표명：대출구압력맥동신호진행소파기이치분해，가이교호지식별리심빙적조기미약기식고장，항간우성화정학성우우전통진단방법。
When the cavitations occur, the cavitating flow changes flow-field in the centrifugal pump.With the analysis of pressure fluctuation which containing the information of flow-field at the outlet, whether the cen-trifugal pump cavitation occurred can be judged and cavitation stages can be determined.In this paper, through utilizing the lift “db4” wavelet transform ’ s outstanding performances in the time-frequency domain and establishing wavelet reconstruction coefficient matrix, the pressure fluctuation signals at the pump’ s outlet were decomposed into different subspaces to ensure the integrity of the information; having the singular value decomposition(SVD) of the time-frequency matrix implemented to obtain the singular value’ s feature vector and then having this feature vector taken as the sample to train artificial neural network(ANN) improved to es-tablish the relations between features and stages of the cavitation.Testing some random signals show that the SVDs of pressure fluctuation at outlet can well diagnose the cavitations at early stage and it outperforms the tra-ditional methods in the anti-inference and accuracy.
