New mode cold start monitoring in industrial processes: A solution of spatial-temporal feature transfer?
New mode cold start monitoring in industrial processes: A solution of spatial-temporal feature transfer?
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DOI：
10.1016/j.knosys.2022.108851
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发表时间：
2022-05-09
影响因子：
8.8
通讯作者：
Yang, Chunhua
中科院分区：
文献类型：
--
作者：
Wang, Kai;Zhou, Wenxuan;Yang, Chunhua
在实际工业过程中，工况经常发生变化，导致模式切换频繁。因此，在新模式的启动阶段，没有足够的样本来构建有效的异常监测模型。同时，收集更多建模样本时出现的不良延迟对实时过程监控构成了威胁。我们提出了一种时空特征传递方法，通过设计传递线性动态系统（TLDS）来解决新模式冷启动监控问题。 TLDS 使我们能够建立令人满意的监控模型，而不需要来自目标模式的大量样本。与大多数迁移学习方法不同，我们的方法采用新的域适应策略，该策略同时传输源域和目标域之间的时间和空间相关性，而不是对齐两个域之间的静态相关性。因此，它特别适合动态过程工业。此外，我们使用 Kullback-Leibler (KL) 散度来对齐两个域中的状态转换和观测生成分布，并应用期望最大化 (EM) 算法来估计 TLDS 模型中的参数和状态。通过数值算例和Tennessee Eastman (TE) p(C) 2022 Elsevier B.V. 验证了该方法的有效性。保留所有权利。
In actual industrial processes, the working conditions often change, resulting in frequent mode switching. Thus, there are no sufficient samples in the start-up stage of a new mode to build an effective model for anomaly monitoring. Meanwhile, the undesirable delay in collecting more modeling samples has posed a threat for real-time process monitoring. We propose a spatial-temporal feature transfer method to address the new mode cold start monitoring by designing a transfer linear dynamic system (TLDS). TLDS enables us to establish a satisfying monitoring model without requiring many samples from the target mode. Unlike most transfer learning methods, our method features a new domain adaptation strategy that simultaneously transfers the temporal and spatial correlations between the source and target domains instead of aligning the static correlations between the two domains. Thus, it is especially well-suited for the dynamic process industry. Moreover, we use the Kullback-Leibler (KL) divergence to align the state transition and observation generation distributions in two domains and apply the expectation maximization (EM) algorithm to estimate the parameters and states in the TLDS model. The effectiveness of this method is verified through a numerical example and the Tennessee Eastman (TE) p(C) 2022 Elsevier B.V. All rights reserved.