Optimal Sensor Placement of Acoustic Sensor for Compressor Blade Crack Detection based on Multi-objective Optimization

Song, Di; Ma, Tianchi; Shen, Jun‐Xian; Xu, Feiyun

doi:10.1109/phm-yantai55411.2022.9941850

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…According to this relationship, the optimized structural parameters of the camshaft bearing pairs can be obtained through appropriate genetic algorithms, such as multi-objective genetic algorithm (MOGA). Recently, in some pioneering studies, DNN and MOGA have been successfully applied in performance prediction (Tian et al, 2023;Zhang et al, 2016), fault detection and status monitoring (Li et al, 2023;Song et al, 2023), structural and machining process parameters optimization in mechanical manufacturing systems (Guo et al, 2023;Canbulut et al, 2009;Wu et al, 2022). To the authors' knowledge, there are no research studies studying the relationship between structural lubrication performance parameters and optimization design of the camshaft bearing pairs.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of tribological properties of camshaft bearing pairs using DNN coupled with NSGA-II algorithm and TOPSIS

Zhao,

Li,

Xie

et al. 2024

ILT

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Purpose This study aims to propose an optimization framework using deep neural networks (DNN) coupled with nondominated sorting genetic algorithm II and technique for order preference by similarity to an ideal solution method to improve the tribological properties of camshaft bearing pairs of internal combustion engine. Design/methodology/approach A lubrication model based on the theory of elastohydrodynamic lubrication and flexible multibody dynamics was developed for a V6 diesel engine. Setting DNN model as fitness function, the multi-objective optimization genetic algorithm and decision-making method were used to optimize the bearing pair structure with the goal of minimizing the total friction loss and the difference of the average values of minimum oil film thickness. Findings The results show that the lubrication state corresponding to the optimized bearing pair structure is elastohydrodynamic lubrication. Compared with the original structure, the optimized structure significantly reduces the total friction loss. Originality/value The optimized performance and corresponding structural parameters are obtained, and the optimization results were verified through multibody dynamics simulation. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0417/

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