Pump Fault Detection Based on MFCC-MLCNN

Abstract

Detection of industrial water pumping systems is both a practical and important area of research in industrial production. The accuracy of fault detection is crucial because failure of fault detection can lead to pump damage and reduced productivity. In order to timely and accurately identify the working status of water supply pumps, a pump fault detection method based on Mel Frequency Cepstrum Coefficient with Migration Learning Convolutional Neural Networks (MFCC-MLCNN) is proposed. The sound signals of water pumps under different operating conditions are preprocessed to calculate their MFCC features as static features, and further processed to obtain the first-order difference MFCC features as well as the second-order difference MFCC features as dynamic features. In this study, the audio dataset of water pumps recorded with ambient noise in a real industrial environment is used. Usually, fault detection datasets are unbalanced because the amount of fault data is limited. A practical way to deal with this problem is to use deep migration learning, where high accuracy can be achieved with limited labeled data. In this study, a migration learning convolutional neural network is introduced to establish a pump fault diagnosis model. The experimental results show that the method proposed in this study can effectively recognize the working state of water supply pumps with a small amount of sample data and model parameters.