近期，我院教师范伟（一作，通讯）等的研究成果“Concurrent quality and process monitoring with a probabilistic sparse nonlinear dynamic method”在中科院二区期刊《Control Engineering Practice》上发表。
论文简介如下：
在工业过程中，研究者们越来越关注如何建立能够有效刻画过程变量与质量相关变量之间相互作用的监测框架。本文提出了一种新的概率稀疏非线性动态方法——CPSINDy，用于实现工业系统的过程与质量并行监测。该方法通过构建基于概率状态空间模型的整体框架，引入非线性动态特性；随后，结合粒子滤波技术，采用期望最大化（Expectation-Maximization, EM）算法进行参数估计。在此基础上，设计了四个动态指标用于识别异常运行工况。通过三个真实工业故障案例验证了所提模型的可行性与优越性。结果表明，基于CPSINDy的模型在故障检测率与虚警率方面均优于传统方法。
In industrial processes, significant attention has been directed toward developing monitoring frameworks that effectively capture the interactions between process variables and quality-related variables. This paper presents a novel probabilistic sparse nonlinear dynamic method, CPSINDy, for concurrent quality and process monitoring in industrial systems. The proposed method incorporates nonlinear dynamics by formulating a comprehensive framework based on a probabilistic state-space model. Subsequently, leveraging the particle filtering technique, parameter estimation is performed using the Expectation-Maximization algorithm. After that, four dynamic indices are introduced to detect abnormal operating conditions. Both feasibility and superiority of the presented model are confirmed through three realistic industrial fault cases. Results demonstrate that CPSINDy based model outperforms traditional approaches in terms of fault detection rates and false alarm rates.
