近期，我院教師范偉（一作，通訊）等的研究成果“Concurrent quality and process monitoring with a probabilistic sparse nonlinear dynamic method”在中科院二區(qū)期刊《Control Engineering Practice》上發(fā)表。
論文簡介如下：
在工業(yè)過程中，研究者們越來越關(guān)注如何建立能夠有效刻畫過程變量與質(zhì)量相關(guān)變量之間相互作用的監(jiān)測框架。本文提出了一種新的概率稀疏非線性動(dòng)態(tài)方法——CPSINDy，用于實(shí)現(xiàn)工業(yè)系統(tǒng)的過程與質(zhì)量并行監(jiān)測。該方法通過構(gòu)建基于概率狀態(tài)空間模型的整體框架，引入非線性動(dòng)態(tài)特性；隨后，結(jié)合粒子濾波技術(shù)，采用期望最大化（Expectation-Maximization, EM）算法進(jìn)行參數(shù)估計(jì)。在此基礎(chǔ)上，設(shè)計(jì)了四個(gè)動(dòng)態(tài)指標(biāo)用于識(shí)別異常運(yùn)行工況。通過三個(gè)真實(shí)工業(yè)故障案例驗(yàn)證了所提模型的可行性與優(yōu)越性。結(jié)果表明，基于CPSINDy的模型在故障檢測率與虛警率方面均優(yōu)于傳統(tǒng)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.
