Nonlinear multi body dynamic modeling and vibration analysis of a double drum coal shearer

Zhang, Xiaoli; Yao, Guo; Zhang, Yimin

doi:10.1007/s11771-021-4757-z

Cited by 6 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The non-linear vibration response of the system is calculated by the Runge-Kutta numerical method. The effects of shear loads, equal damping and stiffness of the walking room, rocker arm angle, and equal damping and stiffness of the hydraulic room were determined from the vibration characteristics [4]. Lu, Z.…”

Section: Literature Reviewmentioning

confidence: 99%

Retracted: Research on intelligent elevation control method of shearer drum based on deep learning

Zhang

2023

IET Software

View full text Add to dashboard Cite

Research on deep learning and shearer drum elevation control is a hot topic in recent years. The shearer drum elevation control based on deep learning has been paid attention to by the majority of researchers and a series of studies have been carried out. In order to solve the problem that the traditional memory cutting method of shearer is not accurate, the research on intelligent elevation control method of shearer drum based on deep learning is proposed. In this essay, the intelligent control system of drum elevation of shearer is modelled mathematically, and the improved deep-learning firefly algorithm is used to optimise the PID parameters of the drum control system, and the Simulink simulation model of the intelligent control system of drum elevation is built. The response of the system using the improved and basic firefly algorithm is analysed. The experimental results show that the overshoot of the optimised system decreases by 21.6% under the step signal. Under the ramp signal, the overshoot of the optimised system decreased by 38.4%. Under the pulse interference signal, the overshoot of the optimised system is reduced by 21.9%. Under the step interference signal, the overshoot of the optimised system is reduced by 33.2%. Under the random interference signal, the wave range of the optimised response curve is reduced by 40.1%. Conclusion: After adding the PID controller optimised by the improved deep learning firefly algorithm to the drum elevation control system, the response performance and anti-interference performance of the system are greatly improved and enhanced. The research of this article provides important guidance for deep learning and shearer drum elevation control. K E Y W O R D S biomedical electronics, combinatorial mathematics | INTRODUCTIONThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Retracted: Research on intelligent elevation control method of shearer drum based on deep learning

Zhang

2023

IET Software

View full text Add to dashboard Cite

show abstract

“…The experimental test results showed that the new type of toothed track improves the traction stability of the shearer, reduces traction vibration more than two times, and reduces the wear volume by 35%. Considering the motion attitude, Zhang et al [ 14 ] established a nonlinear multi-body dynamics system model of the shearer and analyzed the effects of equivalent elasticity-damping coefficient, rocker arm angle, and cutting load on the system vibration. The results showed that the vibration period of the system was prolonged with the increase of the equivalent stiffness of the hydraulic cylinder.…”

Section: Introductionmentioning

confidence: 99%

Experimental study on shearer traction vibration considering attitude disturbances

Ma,

Wan,

Zeng

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…The shearer is one of most the important pieces of equipment to realize the mechanization and modernization of coal mine production. It is mainly composed of a cutting part, loading part, walking part, and hydraulic elevation system [1,2]. In longwall mining faces, the cutting part breaks down the coal from the coal body and loads it into the mining machine of the working conveyor, with a complex system and harsh working environment [3].…”

Section: Introductionmentioning

confidence: 99%

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

2023

View full text Add to dashboard Cite

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.

show abstract

Nonlinear multi body dynamic modeling and vibration analysis of a double drum coal shearer

Cited by 6 publications

References 27 publications

Retracted: Research on intelligent elevation control method of shearer drum based on deep learning

Retracted: Research on intelligent elevation control method of shearer drum based on deep learning

Experimental study on shearer traction vibration considering attitude disturbances

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

Contact Info

Product

Resources

About