Fault Diagnosis of Shearer Based on Fuzzy Inference

Gao, Feng; Xiao, Lin; Zhong, Wei Yan; Liu, Wei

doi:10.4028/www.scientific.net/amm.52-54.1577

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…The causes of failure are diverse and uncertain [4]. When the traditional identification method is used for fault diagnosis, a large amount of redundant data is obtained, which leads to low efficiency and poor accuracy of fault diagnosis [5]. Therefore, it is of great significance to establish an efficient and accurate fault diagnosis method for the hydraulic heightening system of the shearer.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method of the Shearer Hydraulic Heightening System Based on a Rough Set and RBF Neural Network

2023

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The hydraulic heightening system is the core component of the shearer, and its stable operation directly affects the safety and reliability of the equipment, so it is of great significance to realize an efficient and accurate fault diagnosis. This paper proposes a fault diagnosis method combining a rough set and radial basis function neural network (RS-RBFNN). Firstly, the RS is used to discretize the original fault data set and attribute reduction, remove the redundant information, and mine the implicit knowledge and potential rules. Then, the topology structure of the RBFNN is determined. The mapping relationship is established between the fault symptom and category. The fault diagnosis is carried out with Python language. Finally, the method is compared with two diagnostic methods including a back propagation neural network (BPNN) and RBFNN. The research results show that the RS-RBFNN has the highest fault diagnosis accuracy, with an average of 98.68%, which verifies the effectiveness of the proposed fault diagnosis method.

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Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method of the Shearer Hydraulic Heightening System Based on a Rough Set and RBF Neural Network

2023

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“…The multiple fault classifier based on the improved support vector machine theory is used to judge the fault types of coal shearer [ 2 ]. In [ 3 ], a correct and timely diagnosis mechanism of shearer failures by knowledge acquisition through a fuzzy inference system is provided, which can approximate expert experience. Although many research achievements have been proposed, they have some common shortcomings summarized as follows.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm

Wang

et al. 2016

Computational Intelligence and Neuroscience

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In order to accurately identify the dynamic health of shearer, reducing operating trouble and production accident of shearer and improving coal production efficiency further, a dynamic health assessment approach for shearer based on artificial immune algorithm was proposed. The key technologies such as system framework, selecting the indicators for shearer dynamic health assessment, and health assessment model were provided, and the flowchart of the proposed approach was designed. A simulation example, with an accuracy of 96%, based on the collected data from industrial production scene was provided. Furthermore, the comparison demonstrated that the proposed method exhibited higher classification accuracy than the classifiers based on back propagation-neural network (BP-NN) and support vector machine (SVM) methods. Finally, the proposed approach was applied in an engineering problem of shearer dynamic health assessment. The industrial application results showed that the paper research achievements could be used combining with shearer automation control system in fully mechanized coal face. The simulation and the application results indicated that the proposed method was feasible and outperforming others.

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“…In [11], an improved neural network model was proposed based on the integration of quantum calculations and neural networks to monitor the working states of the large mining rotating machines. In [12], a fuzzy inference system was proposed to provide a correct and timely diagnosis mechanism of the shearer health condition. In [13], a shearer fault diagnosis algorithm based on a fuzzy decision tree was proposed to enhance the accuracy and efficiency of shearer fault diagnosis by analyzing the experiment.…”

Section: Relevant Studies On Shearer Health Conditionmentioning

confidence: 99%

Health Condition Evaluation for a Shearer through the Integration of a Fuzzy Neural Network and Improved Particle Swarm Optimization Algorithm

Wang

Liu

et al. 2016

Applied Sciences

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Abstract:In order to accurately evaluate the health condition of a shearer, a hybrid prediction method was proposed based on the integration of a fuzzy neural network (FNN) and improved particle swarm optimization (IPSO). The parameters of FNN were optimized by the use of PSO, which was coupled with a premature judgment and mutation mechanism to increase the convergence speed and enhance the generalization ability. The key technologies are elaborated and the flowchart of the proposed approach was designed. Furthermore, an experiment example was carried out and the comparison results indicated that the proposed approach was feasible and outperforms others. Finally, a field application example in coal mining face was demonstrated to specify the effect of the proposed system.

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Fault Diagnosis of Shearer Based on Fuzzy Inference

Cited by 3 publications

References 3 publications

A Fault Diagnosis Method of the Shearer Hydraulic Heightening System Based on a Rough Set and RBF Neural Network

A Fault Diagnosis Method of the Shearer Hydraulic Heightening System Based on a Rough Set and RBF Neural Network

A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm

Health Condition Evaluation for a Shearer through the Integration of a Fuzzy Neural Network and Improved Particle Swarm Optimization Algorithm

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