Industrial audio fault monitoring system and method based on deep neural network

A deep neural network and fault monitoring technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problems of small data sets, difficulty in collecting, and difficulty in determining audio features, so as to improve accuracy and enhance Accuracy, burden reduction effect

Pending Publication Date: 2021-11-02
WUHAN INSTITUTE OF TECHNOLOGY
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AI Technical Summary

Problems solved by technology

Due to the particularity of the industrial audio field, the research faces difficulties such as small data sets and difficult collection, and audio characteristics are difficult to determine

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  • Industrial audio fault monitoring system and method based on deep neural network
  • Industrial audio fault monitoring system and method based on deep neural network
  • Industrial audio fault monitoring system and method based on deep neural network

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Embodiment Construction

[0033] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0034] see figure 1 , the embodiment of the present invention provides an audio fault monitoring system for circular track equipment.

[0035] like figure 1 As shown, it includes four modules: equipment audio collection module; equipment audio fault monitoring module; audio recognition model training module; audio data and early warning maintenance record storage module.

[0036]The device audio acquisition module is mainly composed of a microphone array and an audio data preprocessing program. Among them, the distance between the microphone array and the monitoring device is kept at 0.5m, the number of sampling bits is 16 bits, the sampling rate is 22050Hz, and it is stored in .wav format. The purpose of the preprocessing program is to standardize the collected device audio data so that it can be kept standardized when it is input...

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Abstract

The invention provides an industrial audio fault monitoring system and method based on a deep neural network. The feature extraction efficiency is improved by selecting and constructing an industrial audio feature set; a deep learning model is introduced as a classifier, so that the accuracy of fault analysis in the field of industrial audio analysis is improved; the deep learning classification model is trained through the normal audio and the abnormal audio generated during the operation of the industrial equipment, so that the burden of manual decision making is reduced, the accuracy of judging the industrial audio fault probability is improved, and the functions of monitoring the audio fault of the industrial equipment in real time and performing fault early warning in a complex environment are realized. The system has the functions of online real-time monitoring, early warning and the like, has the advantages of low deployment cost, high function integration degree and high fault recognition rate, and has the capability of wide popularization.

Description

technical field [0001] The invention belongs to the technical field of industrial audio analysis, and in particular relates to an industrial audio fault monitoring system and method based on a deep neural network. Background technique [0002] With the rapid development of industrial fields in various countries in the world, various enterprises are actively building a complete intelligent manufacturing system, a human-computer integration system that simulates intelligent activities carried out by human experts with a high degree of self-discipline, and can The ability to judge and plan one's own behavior quickly. In the operation of large-scale equipment in the industrial field, it is usually checked whether it is faulty or not mainly by experts with rich experience in identifying mechanical abnormal sounds. This traditional monitoring method not only needs to invest a lot of money to train experts, but also cannot be realized. 24 Hours of uninterrupted monitoring of the p...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G10L25/30G06K9/62G06N3/04G06N3/08
CPCG10L25/30G06N3/08G06N3/045G06F18/241G06F18/214
Inventor 刘玮张俊杰陈灯邵冉张飞兰剑胡杨杨
Owner WUHAN INSTITUTE OF TECHNOLOGY
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