工业工程

工业工程 ›› 2024, Vol. 27 ›› Issue (4): 1-8.doi: 10.3969/j.issn.1007-7375.240227

• 质量工程与生产可靠性 •    

面向多目标优化的航空发动机装配特征选择

陆文灏1,2, 柯勇伟1, 郭永强3, 司书宾1   

  1. 1. 西北工业大学 机电学院，陕西 西安 710072;
    2. 苏州工业职业技术学院 汽车工程学院，江苏 苏州 215104;
    3. 中国航发 航空动力股份有限公司，陕西 西安 710021
  • 收稿日期:2024-05-30 发布日期:2024-09-07
  • 通讯作者: 司书宾(1974—)，男，陕西省人，教授，博士，主要研究方向为复杂网络、可靠性建模和优化。Email：sisb@nwpu.edu.cn E-mail:sisb@nwpu.edu.cn
  • 作者简介:陆文灏(1980—)，男，江苏省人，副教授，硕士，主要研究方向为故障诊断。Email：luwh@siit.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(72231008, 72271200)

Feature Selection for Aero-Engine Assembly Using Multi-objective Optimization

LU Wenhao1,2, KE Yongwei1, GUO Yongqiang3, SI Shubin1   

  1. 1. School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China;
    2. Department of Automotive Engineering, Suzhou Vocational Institute of Industrial Technology, Suzhou 215104, China;
    3. Aviation Power Co., Ltd., Aero Engine Corporation of China, Xi'an 710021, China
  • Received:2024-05-30 Published:2024-09-07

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摘要： 由于航空发动机装配工艺和试车工艺的复杂性，收集到的航空发动机装配数据的装配特征非常庞大，严重干扰了对航空发动机装配质量的准确预测，如何选择航空发动机装配的关键质量特征实现质量预测成为极具挑战性的问题。因此，针对航空发动机装配特征选择难题，本文提出面向多目标优化的航空发动机装配数据的两阶段特征选择方法。明确特征选择的优化目标，在第1阶段，基于最大相关最小冗余算法的相关特征选择过程，计算装配特征与试车指标的互信息值，筛选出与试车指标相关性最大的相关特征，并剔除有干扰影响的冗余特征。在第2阶段，通过引入种群初始化策略和自适应遗传算子，提出基于改进的二代非支配排序遗传算法的关键质量特征选择过程，得到航空发动机装配的关键质量特征子集的帕累托前沿。实验表明，本文所提出的两阶段特征选择方法比传统的方法有更好的适用性和有效性，实现了对航空发动机装配特征选择，提高了对航空发动机装配质量的预测准确率。

关键词: 航空发动机装配, 多目标优化, 高维数据, 关键质量特征

Abstract: Due to the complexity of assembly and testing processes in aero-engine manufacturing, the collected assembly data encompass a large number of assembly features, which seriously interferes with the accurate prediction of assembly quality. Selecting the key quality features of aero-engine assembly to achieve quality prediction becomes a highly challenging task. Therefore, to address this issue, a two-stage feature selection method for aero-engine assembly data based on multi-objective optimization is proposed. Firstly, the optimization objectives of feature selection are defined. In the first stage, the relevant features are selected based on the max relevance and min redundancy (MRMR) algorithm to calculate the mutual information of assembly features and testing indicators. This process filters out the most relevant features related to testing indicators while removing redundant features with interference effects. In the second stage, by introducing a population initialization strategy and adaptive genetic operators, a key quality feature selection process based on the improved non-dominated sorting genetic algorithm-Ⅱ (NSGA-Ⅱ) is proposed to obtain the Pareto front of key quality feature subsets for aero-engine assembly. Finally, experimental results demonstrate that the proposed two-stage feature selection method has better applicability and effectiveness than traditional methods, which enhances the feature selection performance and improves the accuracy of quality prediction for aero-engine assembly.

Key words: aero-engine assembly, multi-objective optimization, high-dimensional data, key quality features

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