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Research on preventive maintenance strategy of multi-equipment system based on the internet of things

International Journal of Wireless and Mobile Computing 2020年第2期18卷 204-214页

作者： Zhang, Guochen Shi, Hui Chen, Zhaobo Li, Xiaobo Division of Complex System and Computational Intelligence Taiyuan University of Science and Technology Taiyuan Shanxi China College of Electrical and Information Engineering Taiyuan University of Science and Technology Taiyuan Shanxi China College of Economy and Management Taiyuan University of Science and Technology Taiyuan Shanxi China

Maintenance activities of equipment are closely related to the condition of equipment and the production demand in the production system consisting of multi-equipment. The operational status of equipment determines the preventive maintenance scheme. To satisfy the maximum production demand, this research combines the Internet of Things (IoT) technology to monitor the operational status of multi-equipment in real time. Based on this, we put forward the strategies for the Preventive Maintenance (PM) by proposing the thresholds such as optimum PM interval, the minimum PM interval and maximum postponement period of PM. In the research a production demand-based PM scheduling model is proposed. Then, a genetic algorithm-based simulation optimisation was used to conduct the case solution. Results show that the strategies proposed are able to effectively coordinate the PM activities of multi-equipment, which is under the real-time condition monitoring, in the case of limits of production demand. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Internet of things

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Swarm collective behaviour driven by artificial physics

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International Journal of Wireless and Mobile Computing 2016年第1期11卷 68-74页

作者： Xie, Liping Zeng, Jianchao Jiao, Shimin Division of Industrial and System Engineering Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology No. 66 Waliu Road Wanbailin District Taiyuan Shanxi030024 China

What kind of interaction and collaborative law among social creatures gives rise to swarm intelligence behaviour? Humans have cognitive limitation and therefore lack understanding of the essence of swarm intelligence. This leads to the emergence of the internal mechanism of swarm intelligence behaviour. In this paper, an artificial physics method to construct the swarm model is introduced. In the model, each individual is regarded as a physical particle. There exists virtual force among individuals, and each individual repels other individuals that are closer than R, while attracting individuals that are farther than R in distance. The law of force between any two individuals is defined and the potential field existence of the model is proved. The influence of the different values of the parameter p to swarm collective behaviour is analysed by drawing the force law curve. The simulation examples illustrate the effectiveness of the swarm model. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Swarm intelligence

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Different force laws driving artificial physics optimisation algorithm for constrained optimisation problem

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International Journal of Wireless and Mobile Computing 2015年第3期9卷 290-299页

作者： Xie, Liping Zeng, Jianchao Yin, Jian Division of Industrial and System Engineering Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology No. 66 Waliu Road Wanbailin District Taiyuan Shanxi030024 China Institute of Computer Science and Technology Taiyuan University of Science and Technology No. 66 Waliu Road Wanbailin District Taiyuan Shanxi030024 China

Inspired by physical force, Artificial Physics Optimisation (APO) algorithm is a novel stochastic based on a physicomimetics framework. Driven by virtual force, a population of sample individuals searches for a global optimum in the problem space. The force law is a key problem associated with the performance of APO algorithm significantly. In the paper, an APO algorithm with the Feasibility and Dominance (FAD) method (FAD-APO) is employed to solve constrained optimisation problems. Three different force laws are constructed between the feasible individuals and infeasible individuals, which drive all individuals to search for a global optimum in the constrained problem space. Simulation results show that FAD3-APO algorithm may generally perform better than FAD1-APO and FAD2-APO;it is the most stable and effective among the three versions of FAD-APO algorithms. Meanwhile, a comparison with other populationbased heuristics shows that the FAD-APO algorithm is competitive on some test function. © 2015 Inderscience Enterprises Ltd.

关键词： Constrained optimization

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