Journal of Process Control最新文献_第4页

A cloud–edge collaborative hierarchical diagnosis framework for key performance indicator-related faults in manufacturing industries 制造业关键绩效指标相关故障的云边缘协同分层诊断框架

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-06-11 DOI: 10.1016/j.jprocont.2025.103462

Xueyi Zhang , Liang Ma , Kaixiang Peng , Chuanfang Zhang , Muhammad Asfandyar Shahid , Yangfan Wang

{"title":"A cloud–edge collaborative hierarchical diagnosis framework for key performance indicator-related faults in manufacturing industries","authors":"Xueyi Zhang , Liang Ma , Kaixiang Peng , Chuanfang Zhang , Muhammad Asfandyar Shahid , Yangfan Wang","doi":"10.1016/j.jprocont.2025.103462","DOIUrl":"10.1016/j.jprocont.2025.103462","url":null,"abstract":"<div><div>In the context of intensifying global market competition and the accelerated advancement of industrial intelligence powered by the Industrial Internet of Things, manufacturing enterprises face pressing challenges in achieving sustainable development through quality and efficiency enhancement. Effective key performance indicators (KPIs) related fault diagnosis plays a crucial role in ensuring product quality stability and efficient production within modern manufacturing industries. However, manufacturing industries are characterized by numerous production sub-processes, hierarchical cooperation and interaction, and complex spatio-temporal features, making the implementation of comprehensive KPI-related fault diagnosis methods challenging. To overcome these challenges and fully leverage the hierarchical and multi-scale nature of manufacturing systems, an innovative hierarchical KPI-related fault diagnosis framework based on cloud–edge collaboration is proposed in this paper. First, a hierarchical information enhancement method utilizing dual-scale slow feature analysis and minimal gated units is developed to handle the multi-scale nature of system levels. Second, graph attention networks are combined with minimal gated units to capture the spatio-temporal dynamics across all levels, and KPI constraints are incorporated to fully extract the KPI-related spatio-temporal features. In addition, bottom-up propagation and top-down updation strategies are designed to facilitate information interaction between levels. Building on this, a cloud–edge collaborative architecture is developed, with specific tasks assigned each side. Finally, the framework is applied to a collaborative prototype system for cloud–edge- device in the hot rolling process, and its effectiveness and applicability are thoroughly evaluated.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103462"},"PeriodicalIF":3.3,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Late lumping controlled variable design for transport reaction processes 输运反应过程的后期集总控制变量设计

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-06-11 DOI: 10.1016/j.jprocont.2025.103464

Xinhui Tang , Chenchen Zhou , Hongxin Su , Yi Cao , Shuang-Hua Yang

{"title":"Late lumping controlled variable design for transport reaction processes","authors":"Xinhui Tang , Chenchen Zhou , Hongxin Su , Yi Cao , Shuang-Hua Yang","doi":"10.1016/j.jprocont.2025.103464","DOIUrl":"10.1016/j.jprocont.2025.103464","url":null,"abstract":"<div><div>Transport reaction processes (TRPs) are inherently infinite-dimensional in space, and achieving optimal distributions of their physical quantities under disturbances is a common challenge. Discretizing such systems into finite-dimensional models and then devising an optimization scheme, is the mainstream route. Among these strategies, a relatively new distributed parameter self-optimizing control (SOC) yields acceptable losses by maintaining controlled variables (CVs) designed offline at constants, which avoids repeated online optimization and is suitable for sufficiently precise discrete TRPs without incurring significant online computational costs. However, any model reduction can cause the loss of critical system information and compromises the optimality of SOC. In this work, a late lumping SOC method that avoids spatial approximation entirely during design phases is developed for TRPs. Using the late lumping null space theorem and the analytical derived sensitivity operator, optimal CVs can be identified to achieve a near-zero loss. The sensitivity for typical TRPs is determined analytically through the theories of differential equations and adjoint operators. Two simulation experiments involving TRPs with convection and diffusion phenomena demonstrate the effectiveness of the proposed method.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103464"},"PeriodicalIF":3.3,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Feature hybrid fusion-based fault diagnosis of multi-scale and multi-stage industrial processes 基于特征混合融合的多尺度多阶段工业过程故障诊断

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-06-10 DOI: 10.1016/j.jprocont.2025.103460

Datong Li, Jun Lu, Tongkang Zhang, Jinliang Ding

{"title":"Feature hybrid fusion-based fault diagnosis of multi-scale and multi-stage industrial processes","authors":"Datong Li, Jun Lu, Tongkang Zhang, Jinliang Ding","doi":"10.1016/j.jprocont.2025.103460","DOIUrl":"10.1016/j.jprocont.2025.103460","url":null,"abstract":"<div><div>The multi-stage production workflows of modern industrial processes pose significant challenges in establishing fault diagnosis models. In addition, the conventional methods struggle to address the inter-stage dependencies and intra-stage multi-scale pattern representations. To this end, this paper proposes a <u>F</u>ault <u>D</u>iagnosis model for <u>M</u>ulti-<u>S</u>tage industrial processes (MSFD) with hybrid domain knowledge and cosine similarity fusion strategies, which can discover a comprehensive plant-wise representation through aggregating the stage-wise multi-scale features of multivariate time-series (MTS) process data. Specifically, the MSFD framework consists of two core components. Firstly, an MTS feature extraction (MTS-FE) module independently captures the multi-scale features of the process data within each production stage on local patterns and broader trends. This module can ensure a more refined stage-wise representation. Following this module, a hybrid feature fusion (HFF) module is proposed based on two distinct strategies – domain knowledge and cosine similarity – to integrate the stage-wise features into a unified representation. This dual strategy bridges semantic process constraints of domain knowledge-guided feature weighting with statistical data patterns cosine similarity-based cross-stage dependency learning, enabling a holistic and context-aware fault diagnosis across sequential workflows. The experiments are conducted on a benchmark dataset and a real-world dataset to demonstrate the effectiveness and superiority of the proposed approach.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103460"},"PeriodicalIF":3.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144242463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross correlation-based blocking whitening slow feature analysis for coupled-data industrial process monitoring 耦合数据工业过程监测中基于交叉相关的分块白化慢特征分析

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-06-06 DOI: 10.1016/j.jprocont.2025.103461

Jiao Meng, Xin Huo, Hewei Gao, Changchun He

{"title":"Cross correlation-based blocking whitening slow feature analysis for coupled-data industrial process monitoring","authors":"Jiao Meng, Xin Huo, Hewei Gao, Changchun He","doi":"10.1016/j.jprocont.2025.103461","DOIUrl":"10.1016/j.jprocont.2025.103461","url":null,"abstract":"<div><div>Modern industrial systems are characterized by high complexity, strong coupling, and multi-source data interactions. Practical industrial process data are mostly nonlinear, dynamical, and manifested as temporal correlation, which makes it challenging for traditional monitoring methods to accurately capture the changes in internal state variables. To this end, a multi-block whitening slow feature analysis (MBW-SFA) approach is proposed in this paper, which utilizes the cross maximum information coefficient (CMIC) for nonlinear correlation analysis, blocking, as well as selective whitening transformation (SWT) for the nonlinear and strongly coupled process variables, so as to avoid the information loss caused by global whitening. The proposed MBW-SFA approach performs manifold mapping on the data with strong correlations, preserving key information while reducing dimensionality, and the selective whitening transformation is applied to prevent information loss across different variables. In addition, for scenarios involving partially known fault data, this study proposes a control limit optimization (CLO) function that balances fault identification and false alarms to calculate control limit thresholds based on slow feature monitoring statistics, achieving objective monitoring of industrial processes. The proposed approach is experimentally validated on Tennessee Eastman process, where the correlation between process variables is investigated, and the results show that the proposed method achieves excellent performance in process monitoring.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103461"},"PeriodicalIF":3.3,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A knowledge transfer-based intelligent decision support method for fault management 基于知识转移的故障管理智能决策支持方法

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-05-24 DOI: 10.1016/j.jprocont.2025.103452

Chang Tian , Pengcheng Gao , Feng Yin , Haidong Fan , Xiang Gao

{"title":"A knowledge transfer-based intelligent decision support method for fault management","authors":"Chang Tian , Pengcheng Gao , Feng Yin , Haidong Fan , Xiang Gao","doi":"10.1016/j.jprocont.2025.103452","DOIUrl":"10.1016/j.jprocont.2025.103452","url":null,"abstract":"<div><div>In practical operations, fault management often depends on the expertise of onsite operators, yet manual judgments are limited in timeliness and consistency. To support onsite operators, this paper proposes a decision support approach to recommend the optimal intervention action for fault by comparing risk-reward of candidate actions. A significant challenge is quantifying action rewards, due to the unavailability of data on action consequences during the decision stage. In response, we introduce a symptom description-based knowledge transfer to evaluate action rewards without such data. First, risk prototypes are introduced, which are trained by historical fault data to transform risk magnitude into quantifiable distances between the prototypes. Then, fault symptom descriptions are incorporated as risk knowledge, upon which a generalized mapping function between risk prototypes and symptoms is established. This mapping function is realized through a zero-shot learning paradigm, enabling the knowledge transfer from observed symptoms to those not yet seen. Finally, an online recommendation strategy is developed, which identifies residual symptoms post-action and maps these to the risk prototypes in the feature space. By analyzing the distances between post-action risk prototypes, the risk-reward of actions is assessed, allowing for action recommendations based on their risk-reward rankings. The proposed method is validated by the benchmark Tennessee Eastman process. The results show that with a well-designed symptom matrix, it is possible to identify the optimal intervention action for fault management under zero-sample conditions.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"151 ","pages":"Article 103452"},"PeriodicalIF":3.3,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive model predictive control with one free control move for uncertain discrete-time linear systems with bounded disturbance 具有有界扰动的不确定离散线性系统的单自由度自适应模型预测控制

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-05-21 DOI: 10.1016/j.jprocont.2025.103450

Yingjie Hu , Jian Ye , Herbert Ho-Ching Iu , Tyrone Fernando , Xinan Zhang

引用次数: 0

Noncollocated feedback stabilization of a heat conduction process on a nonuniform ring 非均匀环上热传导过程的非配位反馈稳定化

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-05-18 DOI: 10.1016/j.jprocont.2025.103447

Xiu-Fang Yu , Jun-Min Wang , Jun-Jun Liu

{"title":"Noncollocated feedback stabilization of a heat conduction process on a nonuniform ring","authors":"Xiu-Fang Yu , Jun-Min Wang , Jun-Jun Liu","doi":"10.1016/j.jprocont.2025.103447","DOIUrl":"10.1016/j.jprocont.2025.103447","url":null,"abstract":"<div><div>In this article, we study the stabilization of a 1-d heat conduction process on a nonuniform ring, where the heat flux at <span><math><mrow><mi>z</mi><mo>=</mo><mn>1</mn></mrow></math></span> is fed back to <span><math><mrow><mi>z</mi><mo>=</mo><mn>0</mn></mrow></math></span> through a recycle loop, and two noncollocated point temperatures at <span><math><mrow><mi>z</mi><mo>=</mo><mn>1</mn></mrow></math></span> and <span><math><mrow><msub><mrow><mi>z</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>∈</mo><mrow><mo>(</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>)</mo></mrow></mrow></math></span> are available to be measured. The instability of the heat system comes from two parts: one is the boundary recycle, and the other is the distributed terms of the heat equation. The static output control is designed at the left boundary <span><math><mrow><mi>z</mi><mo>=</mo><mn>0</mn></mrow></math></span> to overcome the instability, and the admissible value ranges of feedback gains are concluded by spectral analysis so that the closed-loop system is shown to be well-posed and exponentially stable. The numerical simulations are carried out to demonstrate the effectiveness of the proposed controller.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"151 ","pages":"Article 103447"},"PeriodicalIF":3.3,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nonlinear principal component analysis with random Bernoulli features for process monitoring 过程监测的随机伯努利特征非线性主成分分析

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-05-13 DOI: 10.1016/j.jprocont.2025.103449

Ke Chen, Dandan Jiang

{"title":"Nonlinear principal component analysis with random Bernoulli features for process monitoring","authors":"Ke Chen, Dandan Jiang","doi":"10.1016/j.jprocont.2025.103449","DOIUrl":"10.1016/j.jprocont.2025.103449","url":null,"abstract":"<div><div>The process generates substantial amounts of data with highly complex structures, leading to the development of numerous nonlinear statistical methods. However, most of these methods rely on computations involving large-scale dense kernel matrices. This dependence poses significant challenges in meeting the high computational demands and real-time responsiveness required by online monitoring systems. To alleviate the computational burden of dense large-scale matrix multiplication, we incorporate the bootstrap sampling concept into random feature mapping and propose a novel random Bernoulli principal component analysis method to efficiently capture nonlinear patterns in the process. We derive a convergence bound for the kernel matrix approximation constructed using random Bernoulli features, ensuring theoretical robustness. Subsequently, we design four fast process monitoring methods based on random Bernoulli principal component analysis to extend its nonlinear capabilities for handling diverse fault scenarios. Finally, numerical experiments and real-world data analyses are conducted to evaluate the performance of the proposed methods. Results demonstrate that the proposed methods offer excellent scalability and reduced computational complexity, achieving substantial cost savings with minimal performance loss compared to traditional kernel-based approaches.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"151 ","pages":"Article 103449"},"PeriodicalIF":3.3,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust predictive tracking fault-tolerant control for multiphase switched systems with asynchronous switching: A Lyapunov–Razumikhin method 具有异步切换的多相切换系统鲁棒预测跟踪容错控制：Lyapunov-Razumikhin方法

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-05-08 DOI: 10.1016/j.jprocont.2025.103451

Huiyuan Shi , Pu Jiang , Hui Li , Chengli Su , Ping Li

{"title":"Robust predictive tracking fault-tolerant control for multiphase switched systems with asynchronous switching: A Lyapunov–Razumikhin method","authors":"Huiyuan Shi , Pu Jiang , Hui Li , Chengli Su , Ping Li","doi":"10.1016/j.jprocont.2025.103451","DOIUrl":"10.1016/j.jprocont.2025.103451","url":null,"abstract":"<div><div>This paper develops a robust predictive tracking fault-tolerant control approach for a class typical of multiphase switched systems, i.e., multiphase batch processes, accompanied by asynchronous switching, small time delays, partial actuator faults and disturbances. First, an equivalent extended asynchronous switching model, including a match sub-model and a mismatch sub-model, is built. In this model, the Lyapunov–Razumikhin function method is chosen to handle time delays due to its ability to make the original states of the systems remain invariant set characteristics. Meanwhile, this method has the characteristics of small computation and low conservativeness in solving the linear matrix inequalities, which is appropriate for systems with small delays. Next, according to the stable sufficient conditions based on robust positively invariant sets and terminal constraint sets, the controller gains, the minimum and maximum dwell time are solved online to eliminate the asynchronous switching situation. Moreover, unlike the iterative learning method with globally constant controller gain, its system state cannot change in real time with the action of the desired controller gain, making state deviations occur over time. In contrast, the controller gain in this method can be corrected and updated to avoid state deviation issue in real time. Finally, a simulation case of injection molding process is used to demonstrate the feasibility of the proposed approach.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"151 ","pages":"Article 103451"},"PeriodicalIF":3.3,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and comparison of PI and boundary-based predictive controller for control of aeration in activated sludge bioreactor – Simulation and laboratory research PI与基于边界的预测控制器在活性污泥反应器曝气控制中的设计与比较——仿真与实验室研究

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-04-30 DOI: 10.1016/j.jprocont.2025.103446

Tomasz Ujazdowski , Robert Piotrowski , Witold Nocoń , Krzysztof Stebel , Jakub Pośpiech

引用次数: 0