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Automatic crack detection method for loaded coal in vibration failure process
- Source :
- PLoS ONE, Vol 12, Iss 10, p e0185750 (2017), PLoS ONE
- Publication Year :
- 2017
- Publisher :
- Public Library of Science (PLoS), 2017.
-
Abstract
- In the coal mining process, the destabilization of loaded coal mass is a prerequisite for coal and rock dynamic disaster, and surface cracks of the coal and rock mass are important indicators, reflecting the current state of the coal body. The detection of surface cracks in the coal body plays an important role in coal mine safety monitoring. In this paper, a method for detecting the surface cracks of loaded coal by a vibration failure process is proposed based on the characteristics of the surface cracks of coal and support vector machine (SVM). A large number of cracked images are obtained by establishing a vibration-induced failure test system and industrial camera. Histogram equalization and a hysteresis threshold algorithm were used to reduce the noise and emphasize the crack; then, 600 images and regions, including cracks and non-cracks, were manually labelled. In the crack feature extraction stage, eight features of the cracks are extracted to distinguish cracks from other objects. Finally, a crack identification model with an accuracy over 95% was trained by inputting the labelled sample images into the SVM classifier. The experimental results show that the proposed algorithm has a higher accuracy than the conventional algorithm and can effectively identify cracks on the surface of the coal and rock mass automatically.
- Subjects :
- Fossil Fuels
Support Vector Machine
Image Processing
0211 other engineering and technologies
lcsh:Medicine
Statics
02 engineering and technology
Machine Learning
Mathematical and Statistical Techniques
021105 building & construction
0202 electrical engineering, electronic engineering, information engineering
Rock mass classification
lcsh:Science
Principal Component Analysis
Multidisciplinary
Noise (signal processing)
Physics
Classical Mechanics
Structural engineering
Cameras
Coal
Optical Equipment
Physical Sciences
Engineering and Technology
020201 artificial intelligence & image processing
Safety
Organic Materials
Algorithms
Statistics (Mathematics)
Geology
Research Article
Computer and Information Sciences
Imaging Techniques
Materials Science
Feature extraction
Equipment
Fuels
Research and Analysis Methods
Vibration
complex mixtures
Artificial Intelligence
Support Vector Machines
mental disorders
otorhinolaryngologic diseases
Statistical Methods
Materials by Attribute
Histogram equalization
business.industry
lcsh:R
Coal mining
technology, industry, and agriculture
Models, Theoretical
Coal Mining
respiratory tract diseases
Energy and Power
Support vector machine
Signal Processing
Multivariate Analysis
lcsh:Q
business
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 12
- Issue :
- 10
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....427834a7feb00991e94484ee5ffee363