China Mechanical Engineering ›› 2021, Vol. 32 ›› Issue (09): 1073-1079.DOI: 10.3969/j.issn.10041132X.2021.09.008

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A SOM-FWFCM Based Feature Selection Algorithm for Order Remaining Completion Time Prediction#br#

LIU Daoyuan;GUO Yu;HUANG Shaohua;FANG Weiguang;YANG Nengjun;CUI Shiting   

  1. College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing,210016
  • Online:2021-05-10 Published:2021-05-28



  1. 南京航空航天大学机电学院,南京,210016
  • 通讯作者: 郭宇(通信作者),男, 1971年生,教授、博士研究生导师。研究方向为制造物联网、工业大数据、制造系统仿真与数字孪生、增强装配。。
  • 作者简介:刘道元,男, 1995年生,硕士研究生。研究方向为数字化设计与制造、工业大数据。
  • 基金资助:

Abstract: Accurate ORCT prediction was helpful to adjust production schedule and optimize manufacturing processes dynamically, which ensured timely orders delivery. ORCT affected by various production factors, including materials, equipment, works-in-process, et al. The related data possessed typical characteristics of large-scale, multi-dimensions and high-redundancy. Effective feature selection might improve the prediction accuracy. On the basis of constructing candidate feature sets, a feature selection algorithm was proposed based on SOM-FWFCM algorithm. Firstly, the cluster centers of FWFCM algorithm were initialized by SOM network to reduce the reliance on initial cluster centers. Feature weights were calculated by mutual information to achieve feature clustering with guidance. Then, according to the cluster results, representational features were selected to build high-quality key feature subset. Finally, taking the production data of a machining shop as an example, the effectiveness of the proposed algorithm was verified by comparing with other four feature selection algorithms.

Key words: big data; order remaining completion time(ORCT); , feature selection; self-organizing map(SOM); feature weighted fuzzy C-means(FWFCM)

摘要: 准确的订单剩余完工时间预测有助于动态调整生产计划、优化制造过程,以满足订单产品按时交付的需求。订单剩余完工时间受到车间物料、设备、在制品等各类生产要素的综合影响,相关数据具有典型的大量、多维、高冗余的特点,有效的特征选择能够获得更高的预测精度。在构建候选特征集的基础上,提出了一种基于自组织映射(SOM)网络特征加权模糊C均值(FWFCM)的特征选择算法。通过构建SOM网络初始化FWFCM的聚类中心,减少后者对初始聚类中心的依赖;基于互信息计算特征权重,实现导向性特征聚类,根据聚类结果选择特征代表,构成高质量关键特征子集。以某机加工车间的生产数据为例,通过与其他4种特征选择算法的对比分析,验证了所提算法的有效性。

关键词: 大数据, 订单剩余完工时间, 特征选择, 自组织映射, 特征加权模糊C均值

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