Continual Learning for Multimode Dynamic Process Monitoring With Applications to an Ultra–Supercritical Thermal Power Plant
Continual Learning for Multimode Dynamic Process Monitoring With Applications to an Ultra–Supercritical Thermal Power Plant
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DOI：
10.1109/tase.2022.3144288
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发表时间：
2023-01
影响因子：
5.6
通讯作者：
Jingxin Zhang;Donghua Zhou;Maoyin Chen;Xia Hong
中科院分区：
文献类型：
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作者：
Jingxin Zhang;Donghua Zhou;Maoyin Chen;Xia Hong
提出了一种新的基于稀疏动态内主元分析（SDiPCA）的多模态动态过程监测方法。与传统的多模式监测算法不同，该算法通过记忆已有模式的重要特征来更新模型。在连续学习中引入智能突触的概念，引入二次项损失来惩罚模式相关参数的变化，并提出修正的突触智能（MSI）来估计参数的重要性。因此，所提出的算法被称为SDiPCA-MSI。当新模式到来时，应收集一组正常样本。在不显式存储训练样本的情况下合并先前的重要特征，同时从当前模式中提取新信息。因此，SDiPCA-MSI可以为连续模式提供出色的性能。讨论了该方法的特点，包括计算复杂度、优点和潜在的局限性.通过对连续搅拌釜加热炉和实际工业系统的监测，与现有的几种监测方法进行了比较，证明了该方法的有效性和优越性。从业人员注意-多模式过程监控越来越重要，因为工业系统通常在不同的操作条件下运行。然而，大多数研究集中在复杂多模态过程的多个局部监控模型，并假设所有可能的模式的数据是可用的，并在学习之前存储。当相似或新的模式出现时，相应于每种模式重新构建局部模型，模型的容量随着模式的不断出现而增加。自适应方法是多模式监测算法的一个分支，但它们在努力提取当前模式的信息以保证监测性能的同时，逐渐遗忘先前学习的知识。本文提出了一种新的稀疏动态内主元分析与连续学习能力的多模式动态过程监测，其中修改的突触智能开发，以准确地衡量参数的重要性。该方法对连续模式的计算和存储资源要求有限，便于实际应用.与当前的多模式过程监控算法类似，在学习新模式之前需要收集一组数据，这可能会给实时监控带来困难。对于工业系统，如大型发电厂和化工系统，所提出的方法具有出色的能力，监测连续的动态模式。
This paper introduces a novel sparse dynamic inner principal component analysis (SDiPCA) based monitoring for multimode dynamic processes. Different from traditional multimode monitoring algorithms, a model is updated for sequential modes by memorizing the significant features of existing modes. By adopting the concept of intelligent synapses in continual learning, a loss of quadratic term is introduced to penalize the changes of mode–relevant parameters, where modified synaptic intelligence (MSI) is proposed to estimate the parameter importance. Thus, the proposed algorithm is referred to as SDiPCA–MSI. When a new mode arrives, a set of normal samples should be collected. The previous significant features are consolidated without explicitly storing training samples, while extracting new information from the current mode. Consequently, SDiPCA–MSI can provide outstanding performance for successive modes. Characteristics of the proposed approach are discussed, including the computational complexity, advantages and potential limitations. Compared with several state-of-the-art monitoring methods, the effectiveness and superiorities of the proposed method are demonstrated by a continuous stirred tank heater case and a practical industrial system. Note to Practitioners—Multimode process monitoring is increasingly significant as industrial systems generally operate in varying operating conditions. However, most researches focus on multiple local monitoring models for complex multimode processes and assume that data of all possible modes are available and stored before learning. When similar or new modes arrive, local models are rebuilt corresponding to each mode and the model’s capacity would increase with the continuous emergence of modes. Adaptive methods are a branch of multimode monitoring algorithms, but they strive to extract information of the current mode to ensure the monitoring performance while forgetting the previously learned knowledge gradually. This paper proposes a novel sparse dynamic inner principal component analysis with continual learning ability for multimode dynamic process monitoring, where modified synaptic intelligence is developed to measure the parameter importance accurately. It requires limited computation and storage resources for successive modes, which is convenient for practical applications. Similar to current multimode process monitoring algorithms, a set of data should be collected before learning a new mode, which may bring difficulties to real–time monitoring. For industrial systems, such as large–scale power plants and chemical systems, the proposed method has outstanding ability to monitor successive dynamic modes.