Carrier roller fault diagnosis method and system based on machine learning and storage medium

A technology of fault diagnosis and machine learning, which is applied in the field of fault diagnosis of conveyor rollers, can solve problems such as the inability to quickly and efficiently judge roller faults, and achieve the effects of reducing impact, improving accuracy, and improving safety

Active Publication Date: 2021-03-16
CHONGQING INST OF GREEN & INTELLIGENT TECH CHINESE ACADEMY OF SCI +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to overcome the shortcomings in the prior art that the characteristics of the audio signal are not fully utilized and cannot quickly and efficiently judge the fault of the idler roller, and provide a machine learning-based fault diagnosis method for idler rollers that is easy to implement and highly efficient. The system and storage medium combine the in-depth analysis of audio data with the intelligent identification and judgment of the CART model to realize real-time diagnosis of idler faults

Method used

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  • Carrier roller fault diagnosis method and system based on machine learning and storage medium
  • Carrier roller fault diagnosis method and system based on machine learning and storage medium
  • Carrier roller fault diagnosis method and system based on machine learning and storage medium

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Experimental program
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Embodiment 1

[0044] like figure 1 As shown, this embodiment provides a machine learning-based idler fault diagnosis method, including the following steps:

[0045] S1, collect the audio data of the roller;

[0046] S2, extracting features of the audio data; the features of the audio data specifically include one or any combination of sharpness, noise annoyance, and speech interference level;

[0047] S3, the characteristics of the audio data are input into the trained CART model, and the CART model recognizes the running state of the idler;

[0048] S4, if the idler is running abnormally, perform alarm, monitoring or control operations; if the idler is running normally, complete the fault diagnosis of the idler at the current moment, and perform step S5;

[0049] S5, update the time, repeatedly execute step S1 to step S4, and carry out idler fault diagnosis at the next time.

[0050]This embodiment uses voice signal processing technology to analyze the sound quality of the audio data of...

Embodiment 2

[0084] like Figure 4 As shown, this embodiment provides a machine learning-based idler fault diagnosis system, and the machine learning-based idler fault diagnosis system described below and the above-mentioned machine learning-based idler fault diagnosis method can be referred to.

[0085] see Figure 4 As shown, the system includes the following modules connected in sequence: data acquisition module, feature extraction module, CART model module and monitoring module;

[0086] In this embodiment, the data collection module is used to collect the audio data of the idler; the feature extraction module is used to extract the features of the audio data; the CART model module is used to identify the running state of the idler according to the features of the audio data; the monitoring module is used for Perform alert, monitor, or control actions based on the identification and classification results of the logistic regression model module. The CART model module stores the train...

Embodiment 3

[0091] Corresponding to the above method embodiment, this embodiment also provides a readable storage medium, a readable storage medium described below and a machine learning-based idler fault diagnosis method described above can refer to each other .

[0092] A readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the machine learning-based idler fault diagnosis method of the above method embodiment are implemented.

[0093] Specifically, the readable storage medium may be a U disk, a mobile hard disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, and other storage devices that can store program codes. Read storage media.

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Abstract

The invention discloses a carrier roller fault diagnosis method and system based on machine learning and a storage medium. The carrier roller fault diagnosis method based on machine learning comprisesthe following steps of: S1, collecting audio data of a carrier roller; S2, extracting features of the audio data, wherein the features of the audio data specifically comprise one or any combination of sharpness, noise annoyance and speech interference level; S3, inputting the features of the audio data into a trained CART model, and identifying a running state of the carrier roller by using the CART model; S4, if the carrier roller operates abnormally, executing alarm, monitoring or control operation, and if the carrier roller operates normally, completing carrier roller fault diagnosis at the current moment, and executing a step S5; and S5, updating the moment, repeatedly executing the steps S1 to S4, and carrying out carrier roller fault diagnosis at the next moment. According to the carrier roller fault diagnosis method and the system, the carrier roller fault can be diagnosed in real time, and the method is easy to implement, low in cost and low in algorithm complexity.

Description

technical field [0001] The invention relates to the field of fault diagnosis of conveyor rollers, in particular to a method, system and storage medium for fault diagnosis of rollers based on machine learning. Background technique [0002] Belt conveyors are used to transport materials and are an important part of the industrial production process. Belt conveyors can form efficient transportation lines, improve industrial production efficiency, and reduce labor intensity of workers. They are widely used in mining, electric power, docks and other industries. The belt conveyor runs under load for a long time, and various failures are prone to occur, such as: idler damage, belt tearing, etc. Among them, idler failure is one of the main reasons for belt conveyor downtime. The idler roller is an important running part of the belt conveyor, with a large number (a group of about 1 to 3 meters), which mainly plays the role of supporting the belt and bearing and reducing the running...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/028G06N20/00
CPCG01M13/028G06N20/00
Inventor 刘娟罗辛程雪峰黄学达
Owner CHONGQING INST OF GREEN & INTELLIGENT TECH CHINESE ACADEMY OF SCI
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