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Resistance-changing-type single-fault-source diagnosing method for mine ventilation system based on air volume characteristics

A fault diagnosis and single fault technology, which is applied in mine/tunnel ventilation, mining equipment, mining equipment, etc., can solve problems such as strong subjectivity of diagnostic methods, complex parameter adjustment, and large diagnostic errors, so as to avoid the disaster of data dimensionality , good robustness, and the effect of a simple method

Inactive Publication Date: 2018-07-10
LIAONING TECHNICAL UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, although the existing fault diagnosis methods for ventilation network systems have certain effects in analyzing the relationship between branch air volume changes and related branch wind resistance, the diagnostic methods are highly subjective and the parameter adjustment is complicated, resulting in large diagnostic errors and existing problems. Slow convergence and other characteristics

Method used

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  • Resistance-changing-type single-fault-source diagnosing method for mine ventilation system based on air volume characteristics
  • Resistance-changing-type single-fault-source diagnosing method for mine ventilation system based on air volume characteristics
  • Resistance-changing-type single-fault-source diagnosing method for mine ventilation system based on air volume characteristics

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Experimental program
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Effect test

Embodiment 1

[0056] according to figure 2 Take the T-shaped angle-connected mine ventilation network as an example to describe in detail. In the figure, the number of branches n=10, the node m=8, and the fan characteristic curve is: H(q)=1035.92+51.73q-0.43q 2 ,Depend on figure 1 The ventilation network of , generates 52 training samples and simultaneously generates 95 test failure samples;

[0057] And the network topology relationship, branch wind resistance and air volume of the mine ventilation network are shown in Table 1 below:

[0058] The fault sample data space constructed in the first embodiment of Table 1

[0059]

[0060]

[0061] Using the SVM method to train 52 training samples, the specific fault diagnosis classification model and regression model are obtained, which contain 44 support vectors stored in the form of sparse matrix, and these support vectors are the key samples supporting the model.

[0062] Then, the fault diagnosis classification model and regressio...

Embodiment 2

[0067] Image 6 Shown is a complex mine ventilation network that uses three fans to operate together, the number of branches n=100, the node m=69, and the characteristic curves of the three fans are: H(q)=1032.25+44.84q-0.64q 2 ; H(q)=1828.13+19.2q-0.08q 2 ; H(q)=3054.94+8.64q-0.05q 2 . According to the aforementioned method, a total of 4752 training samples and 4751 test samples were generated. Without sensor location optimization, the accuracy rate of fault location diagnosis was 78.11%.

[0068] Due to the special safety requirements of coal mines, it is not possible to conduct underground fault industrial experiments. The research team simulated faults by opening the dampers in the No. 2 Mine of Jinchuan Group, and conducted mine ventilation fault source location diagnosis experiments for 15 dampers. The diagnostic accuracy rate was 100%. %, 70% of the locations where the relative error of the equivalent wind resistance prediction is less than 5%.

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Abstract

The invention discloses a resistance-changing-type single-fault-source diagnosing method for a mine ventilation system based on air volume characteristics. A training sample of the fault and air volume relation is generated through a mine ventilation simulating system (MVSS), a classification model and a regression model based on a support vector machine (SVM) are built, and the fault position andequivalent air resistance are diagnosed. The topology structure of a ventilation system network is complex, change of the air resistance of one of branches of the ventilation system network gives rise to change of the air volume of the branch and the ventilation network of a partial area, the air volume serves as a vector mode so as to distinguish the topology position where a resistance-changing-type fault occurs, and the single-fault-source SVM diagnosing method is put forward to diagnose the resistance-changing-type single fault source position and fault degree of the mine ventilation system; a final decision model is built according to the method, the method is simple, human interference is less, connection between data is objectively reflected, and the method is suitable for solvingthe essential non-linear problem; and the model is determined by a support vector, data dimension disaster is avoided, and good robustness is achieved.

Description

Technical field: [0001] The invention relates to the technical field of mine ventilation, in particular to a method for diagnosing a resistance-variable single fault source of a mine ventilation system based on air volume characteristics. Background technique: [0002] Changes such as falling and deformation of the mine roadway, damper switch or damage, performance degradation of the fan, roadway extension and scrapping, mine car operation, cage lifting, coal bunker emptying, etc., will cause changes in the air volume of the ventilation system, which may easily cause gas, fire, dust and other accidents , quickly diagnose and determine the location of the fault source of the mine ventilation system, which can effectively prevent the occurrence of secondary disasters. The above-mentioned changes can be described by the equivalent wind resistance changes of the corresponding network branches in the calculation of the ventilation network (called the resistance deformation fault ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): E21F1/08E21F17/18E21F17/00
CPCE21F1/08E21F17/00E21F17/18
Inventor 高科刘剑邓立军郭欣
Owner LIAONING TECHNICAL UNIVERSITY
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