A comprehensive diagnosis method of valve leakage faults based on bi-sensor information fusion

Acoustic emission (AE) signals caused by valve leakage exhibit obvious nonlinearity and nonstationarity characteristics. Due to the limitations of traditional valve leakage diagnosis methods, it is difficult to distinguish between internal and external valve leakage failures effectively. Recognizing this challenge, a comprehensive valve leakage diagnosis method based on a multichannel fusion convolutional neural network (MCFCNN) is proposed. First, AE signals are converted from one-dimensional time-domain signals to two-dimensional time-frequency images by the time-frequency analysis method. Then, the time-frequency images are used as model inputs, and MCFCNN fuses the features of time-frequency images from two different position. Hence, a new comprehensive diagnosis method for the bi-sensor fusion contains the time-frequency information, modal information, and position information of valve leakage is proposed. Subsequently, the effectiveness of the proposed method was verified through valve leakage simulation experiments. Furthermore, in order to study the impact of modal information on identifying internal and external valve leakage faults, the fault prediction performance of MCFCNN was compared and analyzed using short-time Fourier transform (STFT) and time-reassigned synchrosqueezing transform (TSST). Finally, according to the needs of engineering practice, the impact of sampling length on different methods is studied. The results show that compared to STFT-MCFCNN, TSST-MCFCNN required a shorter sampling length with the same diagnostic accuracy, which means that the method proposed in this study can achieve faster response time for ball valve leakage under conventional leakage flow rates.