EaLDL: Element-Aware Lifelong Dictionary Learning for Multimode Process Monitoring

计算机科学 过程(计算) 代表(政治) 再培训 约束(计算机辅助设计) 人工智能 多模光纤 机器学习 工业工程 工程类 操作系统 业务 政治 国际贸易 光纤 法学 机械工程 电信 政治学
作者
Keke Huang,Hengxing Zhu,Dehao Wu,Chunhua Yang,Weihua Gui
出处
期刊:IEEE transactions on neural networks and learning systems [Institute of Electrical and Electronics Engineers]
卷期号:: 1-14 被引量:11
标识
DOI:10.1109/tnnls.2023.3343937
摘要

With the rapid development of modern industry and the increasing prominence of artificial intelligence, data-driven process monitoring methods have gained significant popularity in industrial systems. Traditional static monitoring models struggle to represent the new modes that arise in industrial production processes due to changes in production environments and operating conditions. Retraining these models to address the changes often leads to high computational complexity. To address this issue, we propose a multimode process monitoring method based on element-aware lifelong dictionary learning (EaLDL). This method initially treats dictionary elements as fundamental units and measures the global importance of dictionary elements from the perspective of the multimode global learning process. Subsequently, to ensure that the dictionary can represent new modes without losing the representation capability of historical modes during the updating process, we construct a novel surrogate loss to impose constraints on the update of dictionary elements. This constraint enables the continuous updating of the dictionary learning (DL) method to accommodate new modes without compromising the representation of previous modes. Finally, to evaluate the effectiveness of the proposed method, we perform comprehensive experiments on numerical simulations as well as an industrial process. A comparison is made with several advanced process monitoring methods to assess its performance. Experimental results demonstrate that our proposed method achieves a favorable balance between learning new modes and retaining the memory of historical modes. Moreover, the proposed method exhibits insensitivity to initial points, delivering satisfactory results under various initial conditions.
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