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Industrial equipment general fault detection method and system based on sound signals

A fault detection and industrial equipment technology, applied in neural learning methods, measurement devices, measurement of ultrasonic/sonic/infrasonic waves, etc., can solve problems such as low fault detection accuracy, achieve fast and accurate fault detection, easy deployment, and scalability. strong effect

Active Publication Date: 2021-06-25
SHANDONG COMP SCI CENTNAT SUPERCOMP CENT IN JINAN
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The inventors found that most of the current research in the field of abnormal sound recognition is still in the laboratory. Most of the recognition models adopt multi-classification methods and supervised methods for training, but most of their recognition models use the more traditional support vector machine method (Support Vector Machine, SVM), the accuracy of fault detection is low

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  • Industrial equipment general fault detection method and system based on sound signals
  • Industrial equipment general fault detection method and system based on sound signals
  • Industrial equipment general fault detection method and system based on sound signals

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Experimental program
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Embodiment 1

[0056] Such as Figure 1-4 As shown, Embodiment 1 of the present disclosure provides a general fault detection method for industrial equipment based on sound signals, and the method involves two types of equipment: a sending end and a control processing end.

[0057] The sending end is composed of an acoustic signal acquisition device and a microcomputer with a Wi-Fi module. In the same network, there can be multiple sending ends.

[0058] The control processing end is a processing device that can run the python programming language. In the same network, the control processing end is unique.

[0059] Each sending end maintains a bidirectional path to the control processing end, the uplink path is used for transmission of collected audio signals, and the downlink path receives management configuration data from the control processing end.

[0060] figure 1 ① in ① is used for audio signal transmission and state transmission from the sending end to the control processing end, ...

Embodiment 2

[0139] Embodiment 2 of the present disclosure provides a general fault detection method for industrial equipment based on sound signals, including the following process:

[0140] Obtain audio data of industrial equipment to be identified;

[0141] Input the acquired audio data into the preset classification model to obtain the fault detection result;

[0142] Among them, in the preset classification model based on Deep-SVDD, the features of Mel frequency cepstrum coefficient and short-term zero-crossing rate are extracted, and the acquired features are encoded by BP neural network and the distance between the encoded vector and the center of the hypersphere is calculated. According to the distance, the detection score is obtained, and the fault detection result is obtained according to the comparison between the score and the preset threshold.

[0143] The detailed working method is the same as that provided in Embodiment 1, and will not be repeated here.

Embodiment 3

[0145] Embodiment 3 of the present disclosure provides a general fault detection system for industrial equipment based on sound signals, including:

[0146] The data acquisition module is configured to: acquire the audio data of the industrial equipment to be identified;

[0147] The fault detection module is configured to: input the acquired audio data into a preset classification model constructed based on Deep-SVDD to obtain a fault detection result;

[0148] Among them, in the preset classification model based on Deep-SVDD, the features of Mel frequency cepstrum coefficient and short-term zero-crossing rate are extracted, and the acquired features are encoded by BP neural network and the distance between the encoded vector and the center of the hypersphere is calculated. According to the distance, the detection score is obtained, and the fault detection result is obtained according to the comparison between the score and the preset threshold.

[0149] The working method of ...

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Abstract

The invention provides an industrial equipment general fault detection method and system based on sound signals. The method comprises the following steps: acquiring to-be-identified industrial equipment audio data; inputting the acquired audio data into a preset classification model to obtain a fault detection result; in a preset classification model constructed based on Deep-SVDD, extracting Mel-frequency cepstral coefficient features and short-time zero-crossing rate features, encoding the obtained features through a BP neural network, calculating the distance between an encoded vector and the circle center of a hyper-sphere, obtaining a detection score according to the distance, and obtaining a fault detection result according to comparison of the score and a preset threshold value. According to the method, the preset classification model constructed based on the Deep-SVDD is utilized, so that the judgment behavior of a person can be simulated to the maximum extent, and rapid and accurate fault detection is realized.

Description

technical field [0001] The present disclosure relates to the field of acoustic information and artificial intelligence, and in particular to a general fault detection method and system for industrial equipment based on acoustic signals. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] With the development of computer information technology and artificial intelligence technology, the research on sound signals has also expanded from speech recognition to environmental sound recognition. Sound signal recognition is one of the research contents in the field of information processing, covering acoustics, speech processing, information Deal with multiple aspects of artificial intelligence. [0004] In the research of sound signal recognition, the neural network algorithm is usually used. The design of the neural network algorithm is inspired b...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06F21/60G06N3/04G06N3/08G01H17/00
CPCG06F21/602G06N3/08G01H17/00G06N3/048G06N3/045G06F2218/08G06F2218/12G06F18/214
Inventor 汪付强朱庆晨吴晓明李阳李昌盛王京首张鹏刘祥志张建强刘宏
Owner SHANDONG COMP SCI CENTNAT SUPERCOMP CENT IN JINAN