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
CN112504673AActive Publication Date: 2021-03-16CHONGQING INST OF GREEN & INTELLIGENT TECH CHINESE ACADEMY OF SCI +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHONGQING INST OF GREEN & INTELLIGENT TECH CHINESE ACADEMY OF SCI
Publication Date
2021-03-16

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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.
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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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