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

Si, Lina; Wang, Zhongbin; Liu, Ze; Liu, Xinhua; Tan, Chao; Xu, Rongxin

doi:10.3390/app6060171

Cited by 12 publications

(4 citation statements)

References 24 publications

(27 reference statements)

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“…PSO, which is similar to a genetic algorithm, is an iterative optimization algorithm [18,19]. The system is initialized with a set of random solutions.…”

Section: Pso Optimization Combined With the Bp Neural Networkmentioning

confidence: 99%

A Precise Positioning Method for a Puncture Robot Based on a PSO-Optimized BP Neural Network Algorithm

et al. 2017

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The problem of inverse kinematics is fundamental in robot control. Many traditional inverse kinematics solutions, such as geometry, iteration, and algebraic methods, are inadequate in high-speed solutions and accurate positioning. In recent years, the problem of robot inverse kinematics based on neural networks has received extensive attention, but its precision control is convenient and needs to be improved. This paper studies a particle swarm optimization (PSO) back propagation (BP) neural network algorithm to solve the inverse kinematics problem of a UR3 robot based on six degrees of freedom, overcoming some disadvantages of BP neural networks. The BP neural network improves the convergence precision, convergence speed, and generalization ability. The results show that the position error is solved by the research method with respect to the UR3 robot inverse kinematics with the joint angle less than 0.1 degrees and the output end tool less than 0.1 mm, achieving the required positioning for medical puncture surgery, which demands precise positioning of the robot to less than 1 mm. Aiming at the precise application of the puncturing robot, the preliminary experiment has been conducted and the preliminary results have been obtained, which lays the foundation for the popularization of the robot in the medical field.

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“…PSO, which is similar to a genetic algorithm, is an iterative optimization algorithm [18,19]. The system is initialized with a set of random solutions.…”

Section: Pso Optimization Combined With the Bp Neural Networkmentioning

confidence: 99%

A Precise Positioning Method for a Puncture Robot Based on a PSO-Optimized BP Neural Network Algorithm

et al. 2017

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“…Intelligent mines are trying to introduce big data analysis and other technologies, with the key equipment of the comprehensive release working face as the research object. It forms the multi-source heterogeneous big data-driven intelligent identification of the fault state of the comprehensive coal mine equipment and the fault prediction technology system through multi-source heterogeneous data collection and the cleaning and distribution storage [2], the state identification of the equipment of the comprehensive working face [3][4][5], and the quantitative fault diagnosis and state 2 of 16 degradation trend prediction of the key equipment [6,7]. In addition, it provides technical support for the guarantee of the safe and efficient production of the intelligent comprehensive working face and the reasonable maintenance of the equipment.…”

Section: Introductionmentioning

confidence: 99%

Research on Large Language Model for Coal Mine Equipment Maintenance Based on Multi-Source Text

Cao,

Xu,

Zhao

et al. 2024

Applied Sciences

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The efficient management and utilization of coal mine equipment maintenance knowledge is an indispensable foundation for advancing the establishment of intelligent mines. This knowledge has problems such as scattered, low sharing, and insufficient management, which restricts the development of coal mine intelligence. For the above-mentioned problems, a large language model for the maintenance of coal mine equipment based on multi-source text (XCoalChat) was proposed to better manage and utilize the existing massive knowledge of coal mine equipment maintenance. The dataset of coal mine equipment maintenance based on ReliableCEMK-Self-Instruction was constructed to obtain a wide and diverse amount of knowledge through sample generation. Aiming at the illusory problem of the large language model, a knowledge graph enhancement method based on the “Coal Mine Equipment Maintenance System—Full Life Cycle—Specification” was proposed to improve the knowledge density. A triple-LoRA fine-tuning mechanism and DPO direct preference optimization method were introduced into the top of the baseline model, which guarantees that XCoalChat can handle multiple Q&A and maintenance decision analysis tasks with limited computing power. Compared with ChatGLM, Bloom, and LLama, the comprehensive assessment of XCoalChat was performed by experiments including coal mine dialog consulting, coal mine professional consulting, and maintenance decision analysis. The results showed that XCoalChat achieved the best response accuracy in professional consulting and maintenance decision analysis; XCoalChat also took the least reasoning time on average. XCoalChat outperformed other mainstream large language models, which verify that XCoalChat is an effective large language model in the field of coal mine equipment maintenance.

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“…As important mechanized mining equipment, the hydraulic support, scraper chain conveyor, and shearer are usually used to exploit and transport coal in highefficiency coal mining, which directly impacts the automation and intelligent level of fully mechanized mining face. [1][2][3] In the process of coal mining, the shearer is walking on the scraper chain conveyor. Therefore, it is evident that the straightness of the scraper chain conveyor directly affects the working efficiency of the shearer.…”

Section: Introductionmentioning

confidence: 99%

Optimization design of pushing distance estimation of hydraulic support using the Kalman filter with the covariance improvement

Wang

Yan

et al. 2019

Advances in Mechanical Engineering

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The accuracy of pushing displacement of hydraulic support determines the pushing ability, which is the key factor that affects the pushing straightness of the hydraulic support. In order to further improve the accuracy of pushing displacement, considering the inertial sensor as the tools for measuring the pushing distance, an improved filtering method is proposed based on the improved fruit fly optimization algorithm and Kalman filter. The parameters of the Kalman filter, processing noise covariance Q and observation noise covariance R, are optimally designed by the improved fruit fly optimization algorithm to accurately perceive the inertial data. Finally, the feasibility of the proposed method with tuning the Q and R has been verified by comparing with the tuning results of other algorithms. An integrated filtering design method of pushing distance and key parameter analysis of algorithm in the pushing distance perception of hydraulic support are formed, which have theoretical and practical values, and improve the automation level of hydraulic support and the straightness of the fully mechanized coal mining working face.

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

Cited by 12 publications

References 24 publications

A Precise Positioning Method for a Puncture Robot Based on a PSO-Optimized BP Neural Network Algorithm

A Precise Positioning Method for a Puncture Robot Based on a PSO-Optimized BP Neural Network Algorithm

Research on Large Language Model for Coal Mine Equipment Maintenance Based on Multi-Source Text

Optimization design of pushing distance estimation of hydraulic support using the Kalman filter with the covariance improvement

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