Yongsheng Zhao, Jiaqing Luo, Ying Li, Caixia Zhang, Honglie Ma
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The performance monitoring system for a hydrostatic turntable: an improved intelligent algorithm based on the IPSO-NN model
Purpose
The combination of improved PSO (IPSO) algorithm and artificial neural network (ANN) model for intelligent monitoring of the bearing performance of the hydrostatic turntable.
Design/methodology/approach
This paper proposes an artificial neural network model based on IPSO algorithm for intelligent monitoring of hydrostatic turntables.
Findings
The theoretical model proposed in this paper improves the accuracy of the working performance of the static pressure turntable and provides a new direction for intelligent monitoring of the static pressure turntable. Therefore, the theoretical research in this paper is novel.
Originality/value
Theoretical novelties: an ANN model based on the IPSO algorithm is designed to monitor the load-bearing performance of a static pressure turntable intelligently; this study show that the convergence accuracy and convergence speed of the IPSO-NN model have been improved by 52.55% and 10%, respectively, compared to traditional training models; and the proposed model could be used to solve the multidimensional nonlinear problem in the intelligent monitoring of hydrostatic turntables.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0081/
期刊介绍：
Industrial Lubrication and Tribology provides a broad coverage of the materials and techniques employed in tribology. It contains a firm technical news element which brings together and promotes best practice in the three disciplines of tribology, which comprise lubrication, wear and friction. ILT also follows the progress of research into advanced lubricants, bearings, seals, gears and related machinery parts, as well as materials selection. A double-blind peer review process involving the editor and other subject experts ensures the content''s validity and relevance.
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