When the preferred measurement signal for equipment condition monitoring in industrial field is acoustic signal for various reasons, it is especially necessary to propose an equipment condition monitoring method based on acoustic signal. In this paper, a certain type of centrifugal pump is taken as the basis object, and the Mel-scale Frequency Cepstral Coefficients（MFCC） are extracted from the acoustic signals collected in the field as the initial features of the signals, and then the Dispersion Entropy（DE） values of these MFCC initial features are calculated, and the matrix is downscaled by Principal Component Analysis（PCA）, so as to construct the feature matrix. The penalty coefficients and kernel function parameters of the Support Vector Machine（SVM） are optimized by using the Bat Algorithm（BA） to carry out diagnosis of various fault conditions of centrifugal pumps and compared with various diagnostic methods. The experimental results show that the model optimized by BA improves the diagnostic accuracy by 21.7%; on the basis of this model, the deep mining of the signals extracted by MFCC using DE improves the diagnostic accuracy of the model by 2.05%.