一种面向订单剩余完工时间预测的SOM-FWFCM特征选择算法#br#

doi:10.3969/j.issn.10041132X.2021.09.008

摘要/Abstract

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

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)

中图分类号:

TP18

刘道元, 郭宇, 黄少华, 方伟光, 杨能俊, 崔世婷. 一种面向订单剩余完工时间预测的SOM-FWFCM特征选择算法#br#[J]. 中国机械工程, 2021, 32(09): 1073-1079.

LIU Daoyuan, GUO Yu, HUANG Shaohua, FANG Weiguang, YANG Nengjun, CUI Shiting. A SOM-FWFCM Based Feature Selection Algorithm for Order Remaining Completion Time Prediction#br#[J]. China Mechanical Engineering, 2021, 32(09): 1073-1079.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.cmemo.org.cn/CN/10.3969/j.issn.10041132X.2021.09.008

http://www.cmemo.org.cn/CN/Y2021/V32/I09/1073

参考文献

［1］朱海平, 刘繁茂, 刘琼, 等. 基于车间实时状态的订单完工周期预测方法［J］. 中国机械工程, 2009, 20(3):300-304.
ZHU Haiping, LIU Fanmao, LIU Qiong, et al. Application of Neural Network Ensemble in Prediction of Product Due Date［J］. China Mechanical Engineering, 2009, 20(3):300-304.
［2］ZHANG Y, REN S, LIU Y, et al. A Framework for Big Data Driven Product Lifecycle Management［J］. Journal of Cleaner Production, 2017, 159(15):229-240.
［3］李胜, 张培林, 李兵, 等. 基于量子遗传算法的轴向柱塞泵故障特征选择［J］. 中国机械工程, 2014, 25(12):1659-1644.
LI Sheng, ZHANG Peilin, LI Bin, et al.Fault Feature Selection Method for Axial Piston Pump Based on Quantum Genetic Algorithm［J］. China Mechanical Engineering， 2014, 25(12):1659-1644.
［4］张震, 魏鹏, 李玉峰, 等. 改进粒子群联合禁忌搜索的特征选择算法［J］. 通信学报, 2018, 39(12):64-72.
ZHANG Zhen, WEI Peng, LI Yufeng, et al. Feature Selection Algorithm Based on Improved Particle Swarm Joint Taboo Search［J］. Journal on Communications, 2018, 39(12):64-72.
［5］翟俊海, 刘博, 张素芳. 基于粗糙集相对分类信息熵和粒子群优化的特征选择方法［J］. 智能系统学报, 2017, 12(3):397-404.
ZHAI Junhai, LIU Bo, ZHANG Sufang. A Feature Selection Approach Based on Rough Set Relative Classification Information Entropy and Particle Swarm Optimization［J］. CAAI Transactions on Intelligent Systems, 2017, 12(3):397-404.
［6］ZHONG J, WANG J, PENG W, et al. A Feature Selection Method for Prediction Essential Protein［J］. Tsinghua Science and Technology, 2015, 20(5):491-499.
［7］DY J G, BRODLEY C E. Feature Selection for Unsupervised Learning［J］. Journal of Machine Learning Research, 2004, 5(4):845-889.
［8］CAI J , LUO J , WANG S , et al. Feature Selection in Machine Learning:a New Perspective［J］. Neurocomputing, 2018, 300(26):70-79.
［9］朱雪初, 乔非. 基于工业大数据的晶圆制造系统加工周期预测方法［J］. 计算机集成制造系统, 2017, 23(10):2172-2179.
ZHU Xuechu, QIAO Fei. Cycle Time Prediction Method of Wafer Fabrication System Based on Industrial Big Data［J］. Computer Integrated Manufacturing Systems, 2017, 23(10):2172-2179.
［10］PENG H , LONG F , DING C . Feature Selection Based on Mutual Information:Criteria of Max-dependency, Max-relevance, and Min-redundancy［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(8):1226-1238.
［11］夏虎, 庄健, 周璠, 等. 采用多目标进化模型的无监督故障特征选择算法［J］. 振动与冲击, 2014, 33(8):61-65.
XIA Hu, ZHUANG Jian, ZHOU Fan, et al. Unsupervised Feature Selection Algorithm with a Multi-objective Evolutionary Model for Fault Diagnosis［J］. Journal of Vibration and Shock,2014,33(8):61-65.
［12］NOVOVICOVA J , PUDIL P , KITTLER J. Divergence Based Feature Selection for Multimodal Class Densities［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(2):218-223.
［13］LIU H, WU X, ZHANG S. Feature Selection Using Hierarchical Feature Clustering［C］∥International Conference on Information and Knowledge Management.Glasgow, 2011:979-984.
［14］ZHOU P , CHAN K. An Unsupervised Attribute Clustering Algorithm for Unsupervised Feature Selection［C］∥Data Science and Advanced Analytics (DSAA).Paris, 2015:1-7.
［15］WITTEN D , TIBSHIRANI R. A Framework for Feature Selection in Clustering［J］. Journal of the American Statistical Association, 2010, 105(490):713-726.
［16］WANG C, JIANG P . Deep Neural Networks Based Order Completion Time Prediction by Using Real-time Job Shop RFID Data［J］. Journal of Intelligent Manufacturing, 2017, 30(3):1303-1318.
［17］KINNEY J, ATWAL G. Equitability, Mutual Information, and the Maximal Information Coefficient［J］. Proceedings of the National Academy of Sciences, 2014, 111(9):3354-3359.
［18］VERGARA J, ESTVEZ P. A Review of Feature Selection Methods Based on Mutual Information［J］. Neural Computing and Applications, 2014, 24(1):175-186.
［19］MINGOTI S, LIMA J. Comparing SOM Neural Network with Fuzzy C-means, K-means and Traditional Hierarchical Clustering Algorithms［J］. European Journal of Operational Research, 2006, 174(3):1742-1759.
［20］SUN H, WANG S, JIANG Q. FCM-based Model Selection Algorithms for Determining the Number of Clusters［J］. Pattern Recognition, 2004, 37(10):2027-2037.
［21］CHEN T. Embedding a Back Propagation Network into Fuzzy C-means for Estimating Job Cycle Time:Wafer Fabrication as an Example［J］. Journal of Ambient Intelligence and Humanized Computing, 2016, 7(6):789-800.
［22］SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout:a Simple Way to Prevent Neural Networks from Overfitting［J］. Journal of Machine Learning Research, 2014, 15(1):1929-1958.

[1]	李方坡, 路彩虹, 赵靖宵, 李秀程, 尚成嘉. 基于机器学习的石油装备用大截面高强韧马氏体钢智能设计与性能研究[J]. 中国机械工程, 2022, 33(19): 2325-2330.
[2]	蒋佳炜, 胡以怀, 方云虎, 张陈, 芮晓松, 汪猛. 基于多尺度时域平均分解和模糊熵的船用风机故障诊断方法[J]. 中国机械工程, 2022, 33(10): 1178-1188.
[3]	陈换过, 刘培君, 俞杭, 肖雪. 基于改进PCA-SOM的电静压伺服作动器油滤堵塞故障诊断[J]. 中国机械工程, 2021, 32(07): 799-805.
[4]	吕佑龙;许鸿伟;郑城;张洁;郑鹏. 基于混合式特征选择模型的晶圆允收测试关键参数识别方法[J]. 中国机械工程, 2020, 31(16): 1978-1984,1997.
[5]	李敏波, 许鑫星, 李强, 韩乐. 基于JSON文档结构的工业大数据多维分析方法[J]. 中国机械工程, 2020, 31(14): 1700-1707,1716.
[6]	杨斌, 冉琰, 张根保. [可靠性建模及试验技术]基于元动作模块的多轴数控机床精度分析[J]. 中国机械工程, 2020, 31(13): 1621-1628.
[7]	李敏波, 丁铎, 易泳. [质量优化]基于FP-Growth改进算法的轮胎质量数据分析[J]. 中国机械工程, 2019, 30(02): 244-251.
[8]	张晨, 李嘉, 王海宁, 李思悦. [智能设计与计划调度]大数据在设备健康预测和备件补货中的应用[J]. 中国机械工程, 2019, 30(02): 183-187.
[9]	张卫, 丁金福, 纪杨建, 夏文俊, 兰虎, 章建辉. [智能设计与计划调度]工业大数据环境下的智能服务模块化设计[J]. 中国机械工程, 2019, 30(02): 167-173,182.
[10]	刘伟杰, 吉卫喜, 张朝阳, . [数据建模]面向智能生产维护的大数据建模分析方法[J]. 中国机械工程, 2019, 30(02): 159-166.
[11]	周亚勤, 汪俊亮, 鲍劲松, 张洁. [数据建模]针织生产智能管控的通用数据模型研究[J]. 中国机械工程, 2019, 30(02): 143-148,219.
[12]	姚锡凡, 雷毅, 葛动元, 叶晶. [学科发展]驱动制造业从“互联网+”走向“人工智能+”的大数据之道[J]. 中国机械工程, 2019, 30(02): 134-142.
[13]	张洁, 汪俊亮, 吕佑龙, 鲍劲松. [学科发展]大数据驱动的智能制造[J]. 中国机械工程, 2019, 30(02): 127-133,158.
[14]	杨一昕1，2;袁兆才2;皮智波2;夏纬3;姜潮1;郭孔辉1. 智能透明汽车工厂的构建与实施[J]. 中国机械工程, 2018, 29(23): 2867-2874.
[15]	顾复, 顾新建, 张武杰, 倪益华. [绿色设计]透明公平的产品生命周期评价方法[J]. 中国机械工程, 2018, 29(21): 2539-2545.