Scraper conveyor chain fluttering fault diagnosis method

A scraper conveyor and fault diagnosis technology, which is applied in the field of coal mining, can solve the problem of no good detection method for floating chain faults, and achieve the effects of reasonable design, simple implementation, and high recognition accuracy

Active Publication Date: 2017-02-15
XIAN UNIV OF SCI & TECH
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Problems solved by technology

However, there is no good detection method for the

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  • Scraper conveyor chain fluttering fault diagnosis method
  • Scraper conveyor chain fluttering fault diagnosis method
  • Scraper conveyor chain fluttering fault diagnosis method

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[0017] The content of the present invention will be described in detail below in conjunction with the drawings:

[0018] 1. In the production process of fully mechanized mining face, the source of equipment sound is the scraper conveyor and the shearer. The shearer is mainly the sound of the body vibration and the drum cutting the coal wall, and the scraper conveyor is mainly It is the sound of the scraper and the chain rubbing against the trough. The sound of these two devices is relatively stable under normal conditions. When a floating chain failure occurs somewhere on the scraper conveyor, there will be a scraper and chain hitting the trough. Metal impact sound. When the shearer drum cuts to the shovel plate of the scraper conveyor, there will also be a metal impact sound of the shearer knife teeth colliding with the shovel plate of the scraper conveyor. The other two equipment have encountered the cutting and transportation. Different abnormal sounds will be emitted when it ...

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Abstract

The invention discloses a scraper conveyor chain fluttering fault diagnosis method. According to the method, whether a scraper conveyor has a chain fluttering fault at a bending section thereof is diagnosed through correlation classification of a sound signal recognition model based on a deep convolutional neural network (CNN) and a support vector machine (SVM). The model is mainly composed of a related input sample, related sound signal features and a classifier, wherein the related input sample is a spectrogram during operation of fully-mechanized coal mining face equipment after principal component analysis (PCA) and whitening treatment, and the related sound signal features are sound signal features extracted by a deep convolutional neural network (CNN), and the classifier is a support vector machine (SVM) with the sound signal features as input. Whether a scraper conveyor has a chain fluttering fault at a bending section thereof is diagnosed by classifying sound signals through the classifier. The method has the advantages of simple steps, reasonable design, easy implementation, and good use effect which is proved by experiments.

Description

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Application Information

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Owner XIAN UNIV OF SCI & TECH
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