RETRACTED ARTICLE: Predicting safety hazards and safety behavior of underground coal mines

Di, Hongxi; Sbeih, Asma; Shibly, F. H. A.

doi:10.1007/s00500-021-06115-3

Cited by 2 publications

(3 citation statements)

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“…By analyzing the causes of coal mine accidents, it was found that man-made unsafe behaviors accounted for more than 85% of accidents [2]. At present, the management of miners in coal mines mainly involves manually supervising the real-time behaviors of coal miners through surveillance video [3]. This method makes it difficult to have a timely response to emergencies, and a large number of cameras are unattended, resulting in a waste of resources.…”

Section: Introductionmentioning

confidence: 99%

“…In order to solve the problem of structural information loss when transforming the node data to fit the input format of CNN or RNN, Zheng et al [23] proposed an attention cycle relationship network that modeled the temporal and spatial dynamics and added an adaptive attention module for behavior recognition. (3) The method based on a GCN can transform the data of the joint points by considering the human body joint points and limbs as the vertices and edges of the topological graph, respectively; this method can better retain the feature information of the joint points compared with other methods. The ST-GCN proposed by Yan et al [24] is the first network that uses graph convolution for behavior recognition.…”

Section: Introductionmentioning

confidence: 99%

“…X(V,T,C) is the input node information, where V is the number of nodes, T is the number of video frames, and C represents the characteristics of different nodes. A is an adjacency matrix, which was divided into a three-dimensional matrix A[3] after a new partitioning strategy and given a learnable weight. After the data were normalized, a GCN was used to extract spatial information and a TCN was used to extract time information.…”

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confidence: 99%

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Unsafe Mining Behavior Identification Method Based on an Improved ST-GCN

et al. 2023

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Aiming to solve the problems of large environmental interference and complex types of personnel behavior that are difficult to identify in the current identification of unsafe behavior in mining areas, an improved spatial temporal graph convolutional network (ST-GCN) for miners’ unsafe behavior identification network in a transportation roadway (NP-AGCN) was proposed. First, the skeleton spatial-temporal map constructed using multi-frame human key points was used for behavior recognition to reduce the interference caused by the complex environment of the coal mine. Second, aiming to solve the problem that the original graph structure cannot learn the association relationship between the non-naturally connected nodes, which leads to the low recognition rate of climbing belts, fighting and other behaviors, the graph structure was reconstructed and the original partitioning strategy was changed to improve the recognition ability of the model for multi-joint interaction behaviors. Finally, in order to alleviate the problem that the graph convolution network has difficulty learning global information due to the small receptive field, multiple self-attention mechanisms were introduced into the graph convolution to improve the recognition ability of the model for unsafe behaviors. In order to verify the detection ability of the model regarding identifying unsafe behaviors of personnel in a coal mine belt area, our model was tested on the public datasets NTU-RGB + D and the self-built datasets of unsafe behaviors in a coal mine belt area. The recognition accuracies of the proposed model in the above datasets were 94.7% and 94.1%, respectively, which were 6.4% and 7.4% higher than the original model, which verified that the proposed model had excellent recognition accuracies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Unsafe Mining Behavior Identification Method Based on an Improved ST-GCN

et al. 2023

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Accident Prediction in Coal Mine Drilling Using Fuzzy Inference Based on Multi-Scale Feature Extraction

Zhang,

Yao,

2024

JACIII

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To ensure the safety of coal mine drilling operation and reduce losses caused by accidents, this study proposes a fuzzy-reasoning-based early prediction method for in-hole accidents during underground coal mine drilling processes. First, the mechanism of in-hole accidents during underground drilling in coal mines was analyzed, and the changes in different accident-related parameters were summarized. Second, based on the suddenness of different accidents, they were distinguished into sudden-change and slow-change accidents, and the corresponding features were extracted from short- and long-timescale information, respectively. Subsequently, a rule base was constructed based on the analysis of field data and manual experience, and different accident occurrence probabilities were determined using fuzzy reasoning to realize the early prediction of in-hole accidents in the coal mine drilling process. Finally, WinCC was used to design the upper computer interface for in-hole accident prediction in underground drilling processes in coal mines. It displays the results of three types of in-hole accident prediction: drill bit failure, stuck pipe, and bit drop.

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RETRACTED ARTICLE: Predicting safety hazards and safety behavior of underground coal mines

Cited by 2 publications

References 25 publications

Unsafe Mining Behavior Identification Method Based on an Improved ST-GCN

Unsafe Mining Behavior Identification Method Based on an Improved ST-GCN

Accident Prediction in Coal Mine Drilling Using Fuzzy Inference Based on Multi-Scale Feature Extraction

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