Fault Diagnosis of Mine Hoist Based on MFCC-SVDD

Abstract

In the field of coal mine production, mine hoist plays a very important role in the whole mine transportation engineering. Its safety and stability directly affect the production efficiency of coal mine and the life safety of staff. In view of this, a fault diagnosis method of mine hoist based on MFCC-SVDD is proposed. By collecting the audio signal of the elevator, MFCC algorithm was used to extract the sound signal of multiple channels and the MEL frequency cepstrum coefficient was used to extract the fault characteristic parameters. Based on the one-class classifier SVDD, the hypersphere of the elevator was constructed to test and recognize the sound signals in the training, and the classification and recognition of the fault types of the elevator were completed. The MFCC characteristic parameters of 600 training samples were randomly selected as input to train the model, and 200 test samples were identified. The accuracy of fault identification reached 85%-96%, which provided a guarantee for mine production safety.