基于WPHM和非支配性遗传算法的燃气轮机透平叶片多目标优化维护决策

陈丁聚; 郭怡凡; 姜孝谟; 赵海心; 邓乃源; 章旋; 闫斌斌

doi:10.19805/j.cnki.jcspe.2026.250306

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基于WPHM和非支配性遗传算法的燃气轮机透平叶片多目标优化维护决策

动力设备与系统 | 更新时间：2026-09-15

- 基于WPHM和非支配性遗传算法的燃气轮机透平叶片多目标优化维护决策
- Multi-objective Optimal Maintenance Decision-making of Gas Turbine Blades Based on WPHM and Non-dominated Genetic Algorithm
- 动力工程学报 2026年46卷第9期页码：59-67
- 作者机构：
  
  1. 大连理工大学能源与动力学院,辽宁,大连,116024
  2. 中国联合重型燃气轮机技术有限公司,北京,100016
- 作者简介：
  
  陈丁聚(2001—),男,山东威海人,硕士研究生,研究方向为燃气轮机多目标优化维护决策。姜孝谟(通信作者),男,教授,博士,E-mail:xiaomojiang2019@dlut.edu.cn。
- 基金信息：
  
  国家重点项目两机专项资助项目（J040）;沈阳市揭榜挂帅资助项目;大连市领军人才基金资助项目;国家自然科学基金面上资助项目（52375231）
- DOI：10.19805/j.cnki.jcspe.2026.250306
  中图分类号： TK14
- 纸质出版：2026
- 稿件说明：
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陈丁聚, 郭怡凡, 姜孝谟, 赵海心, 邓乃源, 章旋, 闫斌斌. 基于WPHM和非支配性遗传算法的燃气轮机透平叶片多目标优化维护决策[J]. 动力工程学报, 2026, 46(9): 59-67.
陈丁聚, 郭怡凡, 姜孝谟, 赵海心, 邓乃源, 章旋, 闫斌斌. 基于WPHM和非支配性遗传算法的燃气轮机透平叶片多目标优化维护决策[J]. 动力工程学报, 2026, 46(9): 59-67. DOI： 10.19805/j.cnki.jcspe.2026.250306.

DOI： 10.19805/j.cnki.jcspe.2026.250306

摘要

针对燃气轮机透平叶片维护中的可靠性-经济性权衡问题，提出一种融合威布尔比例风险模型（WPHM）与多目标优化算法的动态维护决策方法。该方法以运行状态参数构建健康指标，建立叶片失效概率评估模型，并以累计失效概率和维护总成本为目标，采用非支配排序遗传算法（NSGA）-Ⅱ、NSGA-Ⅲ和多目标粒子群优化（MOPSO）算法求解Pareto最优维护方案。算例结果表明，该方法可动态优化维护时刻、维护类型和维护时长，在满足可靠性要求的同时降低维护成本。相较于传统定期维护方式，3种算法分别节省维护成本29.6%、36.2%和42.7%，其中NSGA-Ⅲ在高可靠性场景下表现最优，可以为透平叶片维护决策提供有效支持。

Abstract

A dynamic maintenance decision-making method

integrating Weibull proportional hazards model (WPHM) and multi-objective optimization algorithm

was proposed to address the reliability-economy trade-off in gas turbine blade maintenance. Health indicators were constructed from operational state parameters to establish a failure probability assessment model for the blades. The cumulative failure probability and total maintenance cost were selected as dual optimization objectives

and Pareto-optimal maintenance schemes were derived using non-dominated sorting genetic algorithm (NSGA)-Ⅱ

NSGA-Ⅲ

and multi-objective particle swarm optimization (MOPSO) algorithm. Case study results demonstrate that the proposed method is capable of dynamically optimizing maintenance timing

maintenance type

and maintenance duration

thereby satisfying reliability constraints while minimizing operational expenditures. Compared with conventional periodic maintenance strategy

the three algorithms achieve maintenance cost reductions of 29.6%

36.2%

and 42.7%

respectively. Among them

NSGA-Ⅲ exhibits superior performance under high-reliability operational scenarios

offering an effective decision-support framework for turbine blade maintenance planning.

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references

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