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Fault diagnosis method for mine fan

A fault diagnosis and fan technology, which is applied in mine/tunnel ventilation, mining equipment, earthwork drilling, etc., can solve problems such as gas explosion, ventilation equipment damage, accumulation, etc., and achieve high accuracy and high fault identification accuracy Effect

Active Publication Date: 2019-05-31
BEIJING UNIV OF TECH
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AI Technical Summary

Problems solved by technology

Once the mine ventilation device breaks down or the ventilation becomes unstable, without taking timely measures, it will cause damage to the ventilation equipment, causing the accumulation of toxic and harmful gases such as underground gas and carbon monoxide, gas explosions, fires and other catastrophic accidents

Method used

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  • Fault diagnosis method for mine fan
  • Fault diagnosis method for mine fan
  • Fault diagnosis method for mine fan

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Experimental program
Comparison scheme
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Embodiment Construction

[0055] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0056] figure 1 It is a flowchart of a two-step mine fan diagnosis model based on GBDT-CNN, including the following steps:

[0057] S1. Use sensors to monitor the operating status of the equipment through the SCADA system;

[0058] S2. Preprocessing the monitored data;

[0059] S3. Build the GBDT integrated learning structure according to the selected features, and use the data in S2 to train the model, such as figure 2 ;

[0060] S4. According to image 3 , use TensorFlow to build a CNN network structure, and use the data in S2 to train the model.

[0061] The present invention adopts the integrated learning model and the deep learning model to learn and train the monitoring data of the mine fan, obtains the fault diagnosis model to gradually determine the fault location and fault degree of the mine fan, and exerts the advantages of the ...

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Abstract

The invention discloses a fault diagnosis method for a mine fan. The fault diagnosis method comprises the following steps: utilizing SCADA to detect a real-time state of fan operation; adopting a waycombining machine learning and deep learning to train data monitored under various working states of the fan; and adopting a GBDT-CNN model to perform fault diagnosis on fan equipment. After determining and judging the fault position of the integral equipment, the diagnosis method determines specific fault degree including light fault degree, medium fault degree and heavy fault degree, of the device. A worker is reminded to select a proper equipment maintenance way while cost is saved as much as possible. Fault diagnosis of equipment is performed in step, and specific fault judgment is not performed while faults of the equipment are judged at the first step, so that diagnosis efficiency is improved while calculation ability of a computer is calculated. Relatively easy-to-capture relation exists between attributes of monitored data and judged fault types. A GBDT integrated learning algorithm is adopted, so that an accuracy rate is relatively high.

Description

technical field [0001] The invention relates to a fault diagnosis method for a mine fan, in particular to a detection method for fault diagnosis based on deep learning, and belongs to the technical field of mine fan fault diagnosis. Background technique [0002] For mine fans, mine ventilation devices are responsible for the important task of conveying fresh air underground, diluting and discharging toxic and harmful gases and dust such as gas and carbon monoxide, and are important equipment to ensure safe production in coal mines. Once the mine ventilation device breaks down or the ventilation is unstable, if measures are not taken in time, the ventilation equipment will be damaged, and toxic and harmful gases such as underground gas and carbon monoxide will accumulate, gas explosions, fires and other catastrophic accidents will occur. If the faults arising and evolving during operation can be accurately and timely identified, it is possible to carry out necessary maintenan...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): F04D27/00E21F1/00
Inventor 付胜黄腾达
Owner BEIJING UNIV OF TECH
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