Open AccessResearch on Mechanical Fault Diagnosis Algorithm Based on Sound Signal and CNN
Author(s) -
Lemei Han,
Zhan Wen,
Haoning Pu,
Wenzao Li
Publication year - 2022
Publication title -
international journal of scientific research in science, engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-1990
pISSN - 2394-4099
DOI - 10.32628/ijsrset229141
Subject(s) - convolutional neural network , computer science , fault (geology) , sensitivity (control systems) , artificial neural network , signal (programming language) , pattern recognition (psychology) , mel frequency cepstrum , signal processing , algorithm , artificial intelligence , feature extraction , speech recognition , engineering , digital signal processing , electronic engineering , seismology , computer hardware , programming language , geology
Failure diagnosis is of great significance for the timely detection of the safety hazard of the equipment and the guarantee of the normal operation of the production. In fault diagnosis, the way based on the processing of sound signal has the advantages of strong fault sensitivity, easy acquisition, and noncontact measurement, and the way of using neural network provides a more efficient and generally applicable method for fault diagnosis efficiency. For the poor diagnostic accuracy of traditional methods, which requires manual extraction of features and poor general applicability of the model, in this paper, we propose a mechanical failure diagnosis method based on acoustic signals and CNNs. The sound signals were first sampled and features extracted by MFCC, then the data were split into training and test sets in a 6:4 ratio and input to the convolutional neural network. After adjusting the parameters for the comparison experiment, the final experimental model was able to achieve 97.05% test accuracy over 20 training test iterations.