工业工程

工业工程 ›› 2020, Vol. 23 ›› Issue (6): 131-137.doi: 10.3969/j.issn.1007-7375.2020.06.018

• 实践与应用 • 上一篇    下一篇

工业生产过程数据稳定性评价方法的适应性研究

唐军1, 周冰1, 文里梁2, 陈爱明2, 何邦华1, 唐丽1, 曾仲大2   

  1. 1. 云南中烟工业有限责任公司，云南 昆明 650231;
    2. 大连达硕信息技术有限公司，辽宁 大连 116023
  • 收稿日期:2019-08-03 发布日期:2020-12-18
  • 作者简介:唐军(1984-)，男，广西壮族自治区人，高级工程师，博士，主要研究方向为卷烟工艺技术
  • 基金资助:
    烟草行业卷烟工艺与装备研究重点实验室开放资助项目(2017GYSYS04)

A Study of Adaptability of Different Statistical Methods for Stability Assessment of Complex Industrial Data

TANG Jun1, ZHOU Bing1, WEN Liliang2, CHEN Aiming2, HE Banghua1, TANG Li1, ZENG Zhongda2   

  1. 1. China Tobacco Yunnan Industrial Co., Ltd., Kunming 650231, China;
    2. Dalian ChemDataSolution Information Technology Co. Ltd., Dalian 116023, China
  • Received:2019-08-03 Published:2020-12-18

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摘要： 在分析工业生产过程的多形态数据特征基础上，模拟具有不同形态特征、不同稳定性等级的工业生产过程数据，对比分析6种常用方法对不同形态工业生产数据稳定性分析的适应性。研究结果表明，极差法、偏差程度 (z-分数) 法、假设检验p值法、方差法、信息熵法仅适应于部分工业过程数据的稳定性分析，且信息熵法所得的量化指标值与生产过程时间有关；变异系数法具有更大范围的适应性，可定量评价具有典型结构特征的工业生产过程数据稳定性。卷烟制丝生产数据分析结果表明，变异系数法适用于卷烟制丝等工业生产过程的稳定性定量评价。

关键词: 工业生产过程, 数据稳定性分析, 适应性研究, 变异系数法, 卷烟制丝工艺

Abstract: Six widely-used methods for stability analysis of simulated data with multi-morphological features and different levels of stability were theoretically compared, and the adaptability for statistical assessment was further studied to suggest optimal selection of these methods, which include range method, variance method, deviation degree method, p-value method, information entropy, and CV method. After summarizing the five different cases of multi-morphological data, an integrated strategy was proposed to simulate the variation of these data and then strictly evaluate the objectivity of these methods used for stability analysis. It shows that the range method, deviation degree method and p-value method are not suitable for the stability analysis of industrial production process data with high complexity. In contrast to these three methods, the performance of variance method and information entropy method is improved for the analysis of some types of data. Fortunately, the CV method has high robustness and wide adaptability to all the five cases of multi-morphological industrial data. The analysis of real data obtained from cigarette processing process further validate the results and conclusions introduced above. The results reported in this study provide an efficient way for stability analysis of industrial data with high complexity.

Key words: industrial production process, data stability analysis, adaptability study, coefficient of variation, cigarette production process

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