Abstract
The hydraulic braking system of the hoist is an extremely important part of the hoist. According to incomplete statistics, its failure rate accounts for more than half of the total number of hoist failures. Once the brake fails, the brake cylinder is stuck, etc. The light ones will bring economic losses to the enterprise, and the serious ones are more likely to endanger the personal safety of the staff. Therefore, it is more and more important to improve the reliability of the braking system of the hoist.The purpose of this paper is to study the fault diagnosis of hydraulic support electrical automatic control system based on particle swarm optimization algorithm. This paper first analyzes the manufacturing process of the oil seal skeleton and the working principle of the manipulator, summarizes the characteristics of the manipulator failure, analyzes the factors that cause the unstable operation of the manipulator and the possible failure types of the manipulator, and analyzes the possible types of failures of the manipulator. The fault characteristics and causes of stamping manipulators. The reasons for this were analyzed in depth. An adaptive particle swarm optimization algorithm is proposed to optimize the BP (back-propagation) neural network. The particle fitness value is better than the current best value. At the same time, the inertia factor is increased to stimulate the role of these better particles in the particle update, and conversely, the inertia factor is decreased to weaken the role of the poorer particles in the particle update, and the inertia and acceleration weighting factors are nonlinear throughout the iteration process. Dynamic fitting strategy to achieve a balance between global search and local search of particle swarms. The experimental results show that the intelligent diagnosis method proposed in this paper improves the efficiency of fault diagnosis of the transmission system, and the accuracy rate is increased by more than 50%. Provide general solutions, improve the efficiency and accuracy of fault diagnosis of nonlinear complex systems, and improve the degree of diagnosis automation.
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This work was supported by the science and technology research project of Jilin Provincial Education Department JJKH20190830KJ.
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RW: manuscript, Material preparation. WS: data collection and analysis.
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Wang, R., Sun, W. Fault diagnosis of electrical automatic control system of hydraulic support based on particle swarm optimization algorithm. J Ambient Intell Human Comput 14, 12091–12097 (2023). https://doi.org/10.1007/s12652-022-03758-4
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DOI: https://doi.org/10.1007/s12652-022-03758-4