Cutting Pattern Identification for Coal Mining Shearer through a Swarm Intelligence–Based Variable Translation Wavelet Neural Network

Xu, Jing; Wang, Zhongbin; Tan, Chao; Si, Lina; Liu, Xinhua

doi:10.3390/s18020382

Cited by 18 publications

(8 citation statements)

References 44 publications

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“…where k is the category number, m denotes the sample number, and  is the weight decay term that using global optimization to penalize the large parameters [21].…”

Section: B Softmax Regressionmentioning

confidence: 99%

“…So a novel cutting pattern recognition model combining chaotic gravitational search algorithm and RVM is proposed. Xu et al [21] proposed an intelligent swarm optimization algorithm inspired by bat foraging behavior so as to select the model parameters of variable translation wavelet neural network, and then the shearer sound signal was processed to achieve the online cutting pattern identification and ensure the safety quality of the working face.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Unsupervised Intelligent Method for Cutting Pick State Recognition of Coal Mining Shearer

Zhang

Meng

Qi³

et al. 2020

IEEE Access

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This paper proposes an intelligent recognition method for shearer cutting state based on deep learning theory, to solve the problems where the picks are prone to various failure forms during the cutting of coal and rock masses by the shearer. The failure will lead to the decline on the stability of the entire machine of the shearer and affect the safety production. Specially, a 1:1 simulation bench is used for simulating underground mining conditions to measure and collect the cutting loads of picks and establish a sample database. Deep learning-based intelligent recognition method is an effective tool that can break away from the dependency of prior knowledge and recognition experience, and sparse. In this paper, a promising deep learning method called sparse filtering is proposed for intelligent recognition of shearer cutting. So sparse filtering is applied to construct an automatic feature extraction model, and softmax regression is adopted as a classifier for cutting pick state recognition. Furthermore, L1/2 regularization term is added to the cost function of sparse filtering to prevent the problem of excessive model training and weights. The proposed method for identifying the cutting status of the shearer can effectively monitor the cutting status of the picker, thereby improving the safety and stability of the cutting of the shearer and promote the coal mining efficiency. INDEX TERMS Shearer pick; sparse filtering; softmax regression; state recognition. K. Zhang et al.: An Unsupervised Intelligent Method for cutting pick state recognition

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“…where k is the category number, m denotes the sample number, and  is the weight decay term that using global optimization to penalize the large parameters [21].…”

Section: B Softmax Regressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Unsupervised Intelligent Method for Cutting Pick State Recognition of Coal Mining Shearer

Zhang

Meng

Qi³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Nevertheless, the dust in the coal mine affects the image collection. By installing an acoustic sensor on the rocker arm of the shearer, Xu used ensemble empirical mode decomposition (EEMD) and a neural network to identify coal and rock, which is hard to avoid using numerous computer simulations to determine method parameters [6,7]. In addition, Si proposed an improved method of coal rock recognition based on the multi-scale fuzzy entropy and support vector machine (SVM) by extracting the vibration signal of the shearer's rocker arm and combining the concepts of multi-scale entropy and fuzzy entropy [8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Acoustic Signal Processing of a Drum Shearer Based on Improved Variational Mode Decomposition and Cluster Analysis

Peng

Zhu

2020

Sensors

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During operation, the acoustic signal of the drum shearer contains a wealth of information. The monitoring or diagnosis system based on acoustic signal has obvious advantages. However, the signal is challenging to extract and recognize. Therefore, this paper proposes an approach for acoustic signal processing of a shearer based on the parameter optimized variational mode decomposition (VMD) method and a clustering algorithm. First, the particle swarm optimization (PSO) algorithm searched for the best parameter combination of the VMD. According to the results, the approach determined the number of modes and penalty parameters for VMD. Then the improved VMD algorithm decomposed the acoustic signal. It selected the ideal component through the minimum envelope entropy. The PSO was designed to optimize the clustering analysis, and the minimum envelope entropy of the acoustic signal was regarded as the feature for classification. We then use a shearer simulation platform to collect the acoustic signal and use the approach proposed in this paper to process and classify the signal. The experimental results show that the approach proposed can effectively extract the features of the acoustic signal of the shearer. The recognition accuracy of the acoustic signal was high, which has practical application value.

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“…Hydraulic support groups constitute the main supporting equipment in a coal mine. 1 The condition of these supports, 2,3 including the supporting pressure 4 and attitude, 5,6 considerably influences the safety and efficiency of the entire coal production system. Therefore, the operation state of the hydraulic supports must be understood.…”

Section: Introductionmentioning

confidence: 99%

Attitude-aware method for hydraulic support groups in a virtual reality environment

Xie

Wang

Zhao-jian

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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Current monitoring methods for hydraulic supports are designed for single machines and are unsuitable for global monitoring. This paper presents an attitude-aware method for hydraulic support groups in a virtual reality environment, which predicts the next cycle attitude in real time by a Grey–Markov prediction model. The attitude datum includes the supporting height and the corresponding attitude angles, which are obtained by installing tilt sensors on the base, the front or rear connecting rod, or the roof beam. The operation state of the hydraulic supports was determined by comparing the predicted and actual data. Next, the hydraulic support attitude was related to the coal-mining height observed through multiple cycles. The three-dimensional virtual monitoring was solved in SQL SERVER, Kingview, Unity3d, and Matlab software. Finally, the proposed method was verified on 40 hydraulic supports of a working face through 17 cycles in an underground experiment. The predictive lateral and longitudinal accuracy reached 78.9 and 82.3%, respectively. By efficiently visualizing the operation state of the hydraulic support groups in three-dimensional mode, this method can predict the future attitude with high accuracy and reliability. Therefore, it provides a theoretical approach to safe and efficient operation of a fully mechanized coal-mining face.

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Cutting Pattern Identification for Coal Mining Shearer through a Swarm Intelligence–Based Variable Translation Wavelet Neural Network

Cited by 18 publications

References 44 publications

An Unsupervised Intelligent Method for Cutting Pick State Recognition of Coal Mining Shearer

An Unsupervised Intelligent Method for Cutting Pick State Recognition of Coal Mining Shearer

A Novel Approach for Acoustic Signal Processing of a Drum Shearer Based on Improved Variational Mode Decomposition and Cluster Analysis

Attitude-aware method for hydraulic support groups in a virtual reality environment

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