顾客需求的动态性使产品演化成为必然，表现为现有产品功能与顾客需求期望之间的差异，称为产品功能退化.识别退化的功能是开展产品再设计和促进产品演化的前提，因此提出一种产品功能退化性评估方法.首先，基于质量功能展开将顾客需求转换为功能需求，利用粗糙集理论和卡诺指数计算功能需求重要度.其次，以产品工程特性取值范围的预测值表征顾客需求映射的期望设计范围.采用投影法计算现有产品工程特性取值范围和顾客需求期望设计范围间的差异度.基于功能需求重要度、工程特性间差异度和工程特性权重定义功能退化指数来评估产品功能退化性.最后，以某型号履带式起重机为研究对象进行案例分析.结果表明:应用所提方法得出的功能退化性评估结果与实际分析结果一致，因此所提方法具有一定有效性和实际可行性.
The dynamics of customer requirement makes product evolution inevitable. The essence of product evolution is the divergence between the function of present product and customer requirements expectation, which is defined as product functional degradation. The identification of degraded function is the precondition for product redesign and evolution. Therefore, a method for assessing the degradation of product function is proposed. First, customer requirements are converted into function requirements based on quality function deployment, and the importance rate of function requirement is calculated by using the rough set theory and Kano index. Then, the predicted value of the value range of future product engineering characteristics is used to represent the expected design range. The difference between the value range of engineering characteristics of existing products and the design range of customer expectation is calculated by using the projection method. The functional degradation index is obtained based on the importance of functional requirements, the difference degree between engineering characteristics, and the weight of engineering characteristics. Finally, a crawler crane is taken as the research object for case analysis. The results show that the functional degradation evaluation obtained by the proposed method is consistent with the actual analysis results, which indicates that the proposed method has a certain effectiveness and practical feasibility.
［1］MONTALVILLO L, DAZ O. Requirement-driven evolution in software product lines: A systematic mapping study［J］. Journal of Systems and Software, 2016, 122: 110-143.
［2］SMITH S, SMITH G, SHEN Y T. Redesign for product innovation［J］. Design Studies, 2012, 33(2): 160-184.
［3］HE Y H, WANG L B, HE Z Z, et al. A fuzzy TOPSIS and Rough Set based approach for mechanism analysis of product infant failure［J］. Engineering Applications of Artificial Intelligence, 2016, 47: 25-37.
［4］KWONG C K, BAI H. A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment［J］. Journal of Intelligent Manufacturing, 2002, 13(5): 367-377.
［5］CHEN L H, WENG M C. An evaluation approach to engineering design in QFD processes using fuzzy goal programming models［J］. European Journal of Operational Research, 2006, 172(1): 230-248.
［6］STONE R B, TUMER I Y, VAN WIE M. The function-failure design method［J］. Journal of Mechanical Design, 2005, 127(3): 397-407.
［7］PNUELI Y, ZUSSMAN E. Evaluating the end-of-life value of a product and improving it by redesign［J］. International Journal of Production Research, 1997, 35(4): 921-942.
［8］MA H Z, CHU X N, LI Y P. An integrated approach to identify function components for product redesign based on analysis of customer requirements and failure risk［J］. Journal of Intelligent and Fuzzy Systems, 2019, 36(2): 1743-1757.
［9］ZHANG L, CHU X N, XUE D Y. Identification of the to-be-improved product features based on online reviews for product redesign［J］. International Journal of Production Research, 2019, 57(8): 2464-2479.
［10］YU J B. Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models［J］. Mechanical Systems and Signal Processing, 2011, 25(7): 2573-2588.
［11］李书明, 任沛, 黄燕晓. 航空发动机基线方程的拟合［J］. 机械工程与自动化, 2016(1): 153-154.
LI Shuming, REN Pei, HUANG Yanxiao. Baseline equation fitting of aeroengine［J］. Mechanical Engineering & Automation, 2016(1): 153-154.
［12］VICHARE N, RODGERS P, EVELOY V, et al. Environment and usage monitoring of electronic pro-ducts for health assessment and product design［J］. Quality Technology & Quantitative Management, 2007, 4(2): 235-250.
［13］MA H Z, CHU X N, LYU G L, et al. An integrated approach for design improvement based on analysis of time-dependent product usage data［J］. Journal of Mechanical Design, 2017, 139(11): 111401.
［14］孟祥慧, 谢友柏, 戴旭东. 面向复杂产品时变性能设计的理论与方法［J］. 机械工程学报, 2010, 46(1): 128-133.
MENG Xianghui, XIE Youbai, DAI Xudong. Methodology of designing for time-varying performance of complex products［J］. Journal of Mechanical Engineering, 2010, 46(1): 128-133.
［15］LIU C, RAMIREZ-SERRANO A, YIN G F. An optimum design selection approach for product customization development［J］. Journal of Intelligent Manufacturing, 2012, 23(4): 1433-1443.
［16］GANGURDE S R, AKARTE M M. Customer preference oriented product design using AHP-modified TOPSIS approach［J］. Benchmarking: An International Journal, 2013, 20(4): 549-564.
［17］AKAO Y. Quality function deployment: Integrating customer requirements into product design［M］. Cambridge: Productivity Press, 1990.
［18］CHAN L K, WU M L. Quality function deployment: A literature review［J］. European Journal of Operational Research, 2002, 143(3): 463-497.
［19］梁洁, 张鹏, 韩侠. 面向顾客满意度改进决策的I-Kano 模型研究［J］. 统计与决策, 2009, 20: 152-153.［知网］
LIANG Jie, ZHANG Peng, HAN Xia. Research on I-Kano model for customer satisfaction improvement decision［J］. Statistics and Decision, 2009, 20: 152-153.
［20］胡东方, 李奕辰, 李彦兵. 基于卡诺和人工免疫系统的顾客需求产品设计［J］. 计算机集成制造系统, 2018, 24(10): 2536-2546.
HU Dongfang, LI Yichen, LI Yanbing. Design for customer requirement product based on KANO and artificial immune system［J］. Computer Integrated Manufacturing Systems, 2018, 24(10): 2536-2546.
［21］李延来, 唐加福, 蒲云, 等. 质量功能展开中顾客需求的最终重要度确定方法［J］. 计算机集成制造系统, 2007, 13(4): 791-796.
LI Yanlai, TANG Jiafu, PU Yun, et al. Final importance ratings determining of customer requirements in quality function deployment［J］. Computer Integrated Manufacturing Systems, 2007, 13(4): 791-796.
［22］黄衍, 王应明, 杨隆浩. 基于 SBM 区间模型的决策单元相似度［J］. 控制与决策, 2017, 32(17): 2090-2098.
HUANG Yan, WANG Yingming, YANG Longhao. Similarity of decision making unit based on SBM interval model ［J］. Control and Decision, 2017, 32(17): 2090-2098.
［23］党耀国, 叶璟. 基于残差思想的区间灰数预测优化模型［J］. 控制与决策, 2018, 33(6): 182-187.
DANG Yaoguo, YE Jing. Interval grey number prediction optimization model based on residual thought［J］. Control and Decision, 2018, 33(6): 182-187.
［24］YUE Z, JIA Y. A direct projection-based group decision-making methodology with crisp values and interval data［J］. Soft Computing, 2015, 21(9): 2395-2405.
