Centrifugal Pump Fault Identification Technology Based on CEEMDAN-PCA-AC-CNN Model

In order to ensure the long-term healthy and stable operation of centrifugal pump, on-line monitoring and fault identification is particularly important. Therefore, this paper presents a fault-identification model based on adaptive noise, with the methods of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Principal Component Analysis (PCA) and Convolution Neural Network (CNN) combined with Autocorrelation (AC). Firstly, vibration signals are collected and decomposed by CEEMDAN. Then, we discriminate the obtained Intrinsic Mode Functions (IMF) fraction and eliminate the noise fraction to reconstruct the first round of denoised signals. After that, PCA is adopted to remove noise in the denoised signals, and the signals which have been filtered twice are then processed by AC and input to CNN to train the model. Through the experimental verification of a centrifugal pump fault, the results show that compared to traditional methods like double-layer noise reduction with CNN and CEEMD-wavelet denoising-AC-CNN, the model presented in this paper is more resistant to interference and has faster convergence speed. Also, it has the advantages of higher precision and better robustness. At the same order of magnitude, its precision can reach 97.9%.