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Si, Lei
- A Feature Extraction Method for Shearer Cutting Pattern Recognition Based on Improved Local Mean Decomposition and Multi-Scale Fuzzy Entropy
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PDF Views:89
Authors
Affiliations
1 School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, CN
1 School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, CN
Source
Current Science, Vol 112, No 11 (2017), Pagination: 2243-2252Abstract
Aiming at accurately identifying shearer cutting patterns, this article proposes a new feature extraction method based on improved local mean decomposition (LMD) and multi-scale fuzzy entropy (MFE). The cubic trigonometric Hermite interpolation was used to calculate local mean and envelope estimate functions to improve LMD decomposition results and a sum of product functions was acquired. Furthermore, MFE, referring to the calculation of fuzzy entropy over a range of scales, was designed to measure the complexity and self-similarity of vibration signals and extract the features from the decomposition results. Subsequently, the obtained feature vectors were fed into two classifiers of support vector machine and back propagation neutral network to realize the cutting pattern recognition. The experimental results indicate the applicability and effectiveness of the methodology and demonstrate that the proposed algorithm could perform better in identifying different cutting categories of shearer.Keywords
Feature Extraction, Local Mean Decomposition, Multi-Scale Fuzzy Entropy, Shearer Cutting Pattern.Full Text
References
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- Assessment of Rib Spalling Hazard Degree in Mining Face Based on Background Subtraction Algorithm and Support Vector Machine
Abstract Views :242 |
PDF Views:80
Authors
Affiliations
1 School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, CN
1 School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, CN
Source
Current Science, Vol 116, No 12 (2019), Pagination: 2001-2012Abstract
Rib spalling is one of the common hazards in a fully mechanized mining face. In order to accurately assess the hazard degree, this study proposes a new method based on background subtraction algorithm and support vector machine (SVM). First, the architecture diagram of rib spalling feature analysis is constructed, and the rib spalling feature indices are determined, including the duration area, height and the centre of ribs spalling height. Then, the specific feature analysis process of rib spalling is performed using the background subtraction algorithm. Furthermore, some virtual 3D rib spalling animations are generated using 3D Studio Max (3Ds Max) software to verify the reasonability of extracted features. Thereafter, the assessment model of rib spalling hazard degree is established based on SVM. Three assessment models based on SVM, back propagation neural network (BPNN) and artificial immune (AI) algorithm have been developed. The assessment accuracy of SVM (reaching 85%) is obviously higher than that of BP-NN (75%) and AI (70%) algorithm. The results indicate the feasibility and superiority of the proposed method in the assessment of rib spalling hazard degree.Keywords
Background Subtraction Algorithm, Hazard Degree Assessment, Mining Face, Rib Spalling, Support Vector Machine.Full Text
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