CAJ | 학술논문

为了对往复泵的故障进行正确诊断，提出基于紧致型小波神经网络的往复泵故障诊断方法。以往复泵单个泵缸内的压力信号作为系统特征信号通过小波包分解来提取故障特征向量，同时将此特征向量作为小波神经网络的输入，利用小波神经网络对故障做进一步的精确实时诊断。通过对往复泵液力端多故障诊断实例的检验表明，该系统故障诊断正确率达到94%以上。
위료대왕복빙적고장진행정학진단，제출기우긴치형소파신경망락적왕복빙고장진단방법。이왕복빙단개빙항내적압력신호작위계통특정신호통과소파포분해래제취고장특정향량，동시장차특정향량작위소파신경망락적수입，이용소파신경망락대고장주진일보적정학실시진단。통과대왕복빙액력단다고장진단실례적검험표명，해계통고장진단정학솔체도94%이상。
A method of fault diagnosis for reciprocating pumps was proposed based on the compact wavelet neural network. In this method, the pressure signal of a single cylinder of the reciprocating pump was used as the characteristic signal of the system to extract the feature vector of the faults by means of the wavelet packet decomposition. At the same time, this feature vector was employed as the input signal of the wavelet neural network to determine the type of the fault. The examples of faults diagnosis at the fluid end of the reciprocating pump show that the correctness rate of the system fault diagnosis can exceed 94%.