Data Analytics and Condition Monitoring Methods for Nonstationary Batch Processes — Current Status and Future
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摘要: 间歇过程作为制造业的重要生产方式之一, 其高效运行是智能制造的优先主题. 为了保障生产过程的高效运行, 面向间歇生产的过程数据解析与状态监控算法在最近三十年间得到大家的广泛关注, 发展速度稳步提升. 但由于间歇过程本身的多重时变大范围非平稳运行复杂特性, 以及对状态监控与故障诊断要求的提高, 现有的理论和方法仍面临着挑战. 本文从分析间歇过程的特性出发, 从数据解析的角度, 总结了近三十年来非平稳间歇过程高性能监控研究的发展. 一方面对间歇过程监控领域几种经典的方法体系进行了总结和梳理, 另一方面揭示了尚存在的问题以及未来可能的研究思路和发展脉络.Abstract: Batch process is an important class of manufacturing processes. Its condition operation has been given high priority for smart manufacturing, which closely depends on the automatic condition monitoring and fault diagnosis. Great efforts have been made in the research on data analytics and high-efficiency monitoring algorithms with significant development for batch processes during the past thirty years. However, due to its complex characteristics and increasing requirements on monitoring and diagnosis precision, there are still many challenging problems in this field. In this paper, starting from the nature of batch process and data analystics, we address the challenges in this field, review the development of monitoring and diagnosis strategies, analyze several classical algorithms, and discuss the future development of batch process high-efficiency monitoring.
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Key words:
- Data analytics /
- intelligent manufacturing /
- batch process /
- nonstationary /
- process monitoring /
- fault diagnosis /
- machine learning
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图 3 将三维数据展开成二维数据的6种方式
Fig. 3 Unfold the three dimensions data into two dimensions using six different manners
表 1 时段划分方法总结对比
Table 1 The comparison of different phase partition methods
时段划分方法 划分依据 优点 缺点 过程机理法[45, 48, 72] 利用实际间歇工业过程运行机理的变化来划分过程运行时段, 要求一定的专家经验和过程知识. 如果间歇生产过程相对简单或者工程师对此比较熟悉, 则可以比较容易地获取过程机理知识实现时段划分. 工业生产过程往往机理复杂, 很难在短时间内获取相关的知识和经验, 从而极大地限制和约束了其顺利实施施和推广应用. 特征分析方法[73—75] 时段的切换对应引起相应测量变量的变化. 对某些过程变量或从中提取的特征变量进行分析, 借助其沿时间轴上的变化判断时段信息. 指示变量方法是其中一种典型代表. 当时段发生切换或者变化, 过程特性变化, 相应的某些过程变量或是特征变量亦发生显著变化, 可用于指示不同时段. 算法较为简单. 并不是每个工业过程中都存在并能找到这样的“指示”变量. k-means[62—66] 通过相似度度量, 分析不同时间点上的潜在相关特性的相似与不同, 如果时间片具有相似特性则被归到同一类中, 具有显著差异则被分到不同类中. 该方法能够自动划分不同的多个时段, 不需借助任何过程机理和知识. 分类的结果决定于过程相关性在时间方向上的变化规律. 没有考虑间歇过程时段运行的时序性, 因此划分结果中会出现时间上不连续的具有相似过程相关性的时间片被分在同一个聚类中. 时段划分结果可读性有所欠缺, 需要针对划分结果进行进一步的后续处理. 此外, 该划分方法根据距离定义衡量过程相关特性的相似度, 聚类的结果受到相似性衡量指标的影响, 而该指标并不能与过程监测的目的直接相关. MPPCA[74—75] 一种优化策略, 通过对不同时间点进行不断尝试, 分析在该点的划分所得到的局部模型是否能够改善原有模型对数据的重构精度, 以此来确定该点的划分是否合适. 无需过程先验知识条件, 自动划分的各个时段时间连续, 解释性较强. 易陷入局部最优, 导致时段划分结果不能更好的反映过程特性变化. SSPP[76—77] 自动地按照间歇生产过程运行时间顺序捕捉潜在过程特性的发展变化, 通过评估时段划分对监测统计量的影响确定合适的时段划分点. 无需过程先验知识条件, 深入考虑了间歇过程潜在特性的时变性和实际过程运行的时序性以及时段划分结果对于之后监测性能的影响. 对过程时段特性变化的实时捕捉具有一定的时间延迟. 
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表 2 多向分析方法与子时段分析方法对比
Table 2 The comparison of multi-way methods and phase partition methods
方法 优点 缺点 多向分析法 分析方法相对简单, 直接针对展开的二维数据矩阵进行分析, 可借用传统的连续过程方法. 针对整个过程只需要建立一个模型. 无法有效分析过程特性时间上的变化规律. 子时段分析方法 1)可以更细致地揭示过程运行的潜在特征, 更好地体现过程运行的局部特征, 促进对复杂工业过程的了解;
2)在每个子时段可以很容易建立统计分析模型, 结构简单, 模型实用;
3)基于子时段可以很容易建立过程监测模型并实现在线应用而无需预估未知数据;
4)可以提高在线故障检测的精度和灵敏度, 并有利于后续准确的故障隔离和诊断;
5)可以深入分析质量指标和每个时段的具体关系, 找出影响质量的关键时段和预测变量等关键性因素, 有利于产品质量的进一步改进.需要进行时段划分, 分析过程特性在同一个操作周次内的变化.
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