Journal of Process Control最新文献_第10页

Semi-supervised concept drift detection and adaptation based on conformal martingale framework 基于共形马丁格尔框架的半监督概念漂移检测与适应

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-01-22 DOI: 10.1016/j.jprocont.2025.103374

Yu Zhang, Ping Zhou, Ruiyao Zhang, Shaowen Lu, Tianyou Chai

{"title":"Semi-supervised concept drift detection and adaptation based on conformal martingale framework","authors":"Yu Zhang, Ping Zhou, Ruiyao Zhang, Shaowen Lu, Tianyou Chai","doi":"10.1016/j.jprocont.2025.103374","DOIUrl":"10.1016/j.jprocont.2025.103374","url":null,"abstract":"<div><div>In the realm of industrial applications for machine learning, multiple challenges are frequently encountered, such as concept drift (CD) and the prohibitive costs associated with data labeling. CD refers to the scenario where the underlying data distribution of the model shifts over time, potentially deteriorating model performance. Addressing these challenges, this paper proposes an innovative semi-supervised CD detection method, specifically designed to tackle both CD and the high costs of data labeling in regression tasks. Initially, considering the high expense of acquiring labeled data in industrial application scenarios, a semi-supervised learning strategy based on self-training is utilized. In this strategy, prediction intervals generated by Conformal Prediction (CP) are used to select high-reliability pseudo-labels. Furthermore, to effectively address CD in real-world industrial settings, the Conformal Martingale (CM) is employed for real-time detection. This framework detects changes by identifying increases in martingale values when CD occurs. Upon detection, the model is promptly retrained using the most recent data following the drift. Finally, the proposed method is validated through experiments conducted on three datasets: the UCI dataset, the alumina evaporation process dataset, and the blast furnace ironmaking dataset. Experimental results demonstrate that the proposed semi-supervised method significantly enhances the performance of the original training model. The detection method accurately identifies CD and notably reduces test errors through model retraining, thereby improving the effectiveness of the model in real-world industrial applications.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"147 ","pages":"Article 103374"},"PeriodicalIF":3.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182256","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

BP neural network-based explicit MPC of nonlinear boiler-turbine systems 基于BP神经网络的非线性锅炉-汽轮机系统显式MPC

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-01-01 DOI: 10.1016/j.jprocont.2024.103353

Jing Li , Defeng He , Xiuli Wang , Yu Kang

引用次数: 0

Integrated fault estimation and fault-tolerant control for constrained LPV systems subject to bounded disturbances 有界扰动约束LPV系统的综合故障估计与容错控制

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-01-01 DOI: 10.1016/j.jprocont.2024.103343

Damiano Rotondo , Marcin Pazera , Marcin Witczak

引用次数: 0

Input-Output Cross Autocorrelation Diagram (IO-CAD) for control loop performance assessment in offshore oil production 用于海上石油生产控制回路性能评价的输入-输出交叉自相关图（IO-CAD）

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-01-01 DOI: 10.1016/j.jprocont.2024.103345

Leonardo M. De Marco , Jorge Otávio Trierweiler , Fábio César Diehl , Marcelo Farenzena

{"title":"Input-Output Cross Autocorrelation Diagram (IO-CAD) for control loop performance assessment in offshore oil production","authors":"Leonardo M. De Marco , Jorge Otávio Trierweiler , Fábio César Diehl , Marcelo Farenzena","doi":"10.1016/j.jprocont.2024.103345","DOIUrl":"10.1016/j.jprocont.2024.103345","url":null,"abstract":"<div><div>Severe slugging is a prevalent issue in offshore oil wells that significantly hampers oil production. While active pressure control has proven effective in mitigating this problem, determining optimal setpoints remains a manual and labor-intensive procedure. This study introduces the Input-Output Cross Autocorrelation Diagram (IO-CAD), which examines both input and output autocorrelations to provide a more comprehensive assessment of control loops compared to previous methods that only use output autocorrelation data. Four indicators based on IO-CAD were developed and tested in two case studies involving offshore oil production. They were compared to an oscillation detection method published in the literature, as oscillations in the control loop may indicate the slugging flow. Early detection of slugging patterns is crucial in offshore oil production to prevent severe slugging and stabilize control loops. The results demonstrated that the IO-CAD indicators are robust against setpoint changes, disturbances, and noise in the control loop performance assessment, while the oscillation detection method from the literature is sensitive to measurement and process noise, as well as control loop oscillations.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"145 ","pages":"Article 103345"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096454","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

Adversarial relationship graph learning soft sensor via negative information exclusion 基于负信息排除的对抗关系图学习软传感器

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-01-01 DOI: 10.1016/j.jprocont.2024.103354

Mingwei Jia , Chao Yang , Zhouxin Pan , Qiang Liu , Yi Liu

引用次数: 0

Partially precise instrument measurements-aided deep learning for industrial quality prediction 用于工业质量预测的部分精密仪器测量辅助深度学习

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-01-01 DOI: 10.1016/j.jprocont.2024.103346

Zhiyi Ji, Xiang Lei, Sijia Wang, Kai Wang, Chunhua Yang

{"title":"Partially precise instrument measurements-aided deep learning for industrial quality prediction","authors":"Zhiyi Ji, Xiang Lei, Sijia Wang, Kai Wang, Chunhua Yang","doi":"10.1016/j.jprocont.2024.103346","DOIUrl":"10.1016/j.jprocont.2024.103346","url":null,"abstract":"<div><div>Material composition is a kind of important quality index in the process industry. Even though instruments for online measuring these compositions have been widely applied, the precision of material composition measurements is suspicious due to corrosion, scaling and other factors. Laboratory values are more convinced, while these instruments are largely idle in real applications. Nevertheless, despite suspicious precision, partially precise trends exist in these measurements, which are also useful for indicating the variation in quality. This means that a wealth of information directly related to quality variables can provide positive guidance for quality prediction. Enlightened by the requirement of information utilization, a long short-term memory network with embedded trend consistency criteria (TCC-LSTM) is proposed for industrial quality prediction through extremely efficient utilization of partially precise quality instrument data. Specifically, based on the property that the trends of the measured values for quality variable are similar to that of the corresponding laboratory values over time, six trend consistency criteria are designed to evaluate the reliability of instrument data, so as to determine the contribution weights of these data in deep learning-based quality prediction. Moreover, in the neural network structure, the space-wise and time-wise attention mechanisms are designed for capturing important variables and time information. Extensive experiments on an actual alumina digestion process demonstrate the efficiency of TCC-LSTM, whose correlation coefficient is averagely improved by 0.2247 and mean absolute error is as low as 0.008079.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"145 ","pages":"Article 103346"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135884","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

Multi large language model collaboration framework for few-shot link prediction in evolutionary fault diagnosis event graphs 基于演化故障诊断事件图的多语言模型协作框架

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-01-01 DOI: 10.1016/j.jprocont.2024.103342

Tian Wang , Ping Wang , Feng Yang , Shuai Wang , Qiang Fang , Meng Chi

{"title":"Multi large language model collaboration framework for few-shot link prediction in evolutionary fault diagnosis event graphs","authors":"Tian Wang , Ping Wang , Feng Yang , Shuai Wang , Qiang Fang , Meng Chi","doi":"10.1016/j.jprocont.2024.103342","DOIUrl":"10.1016/j.jprocont.2024.103342","url":null,"abstract":"<div><div>Fault-tolerant control is crucial for ensuring flight safety in aircraft. However, existing methods for fault diagnosis in nonlinear systems face challenges such as data sparsity, limited generalization, and lack of explainability. To address these challenges, this paper proposes a multi-large language model (LLM) collaboration framework for few-shot link prediction in evolutionary fault diagnosis event graphs. The framework consists of two modules: the Clustering Language Model (LMc) and the Prediction Language Model (LMP). LMc utilizes the semantic understanding capabilities of LLMs to cluster entities and decompose large-scale graph data into smaller subgraphs, mitigating the impact of data sparsity on link prediction. LMP leverages the reasoning capabilities of LLMs to perform link prediction within each subgraph and fuses the prediction results to enhance accuracy and generalization. The completion of the link serves as a means to an end, which is to conduct fault diagnosis reasoning on a more detailed knowledge graph, thereby significantly improving the accuracy of fault diagnosis. Experimental results demonstrate that the proposed framework outperforms traditional embedding models and existing meta-learning methods on multiple datasets, particularly for sparse and background-rich datasets. This approach offers a novel solution for fault diagnosis in nonlinear systems, with significant theoretical and practical value.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"145 ","pages":"Article 103342"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096540","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

Fault-tolerant safe control for water networks: A backstepping neural control barrier function approach 水网容错安全控制：一种后退神经控制障碍函数方法

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-01-01 DOI: 10.1016/j.jprocont.2024.103344

Qingkai Meng, Milad Shahvali, Stelios Vrachimis, Marios M. Polycarpou

{"title":"Fault-tolerant safe control for water networks: A backstepping neural control barrier function approach","authors":"Qingkai Meng, Milad Shahvali, Stelios Vrachimis, Marios M. Polycarpou","doi":"10.1016/j.jprocont.2024.103344","DOIUrl":"10.1016/j.jprocont.2024.103344","url":null,"abstract":"<div><div>As a typical nonlinear process control infrastructure, the safety and reliability of drinking water transport systems (DWTS) are affected by various factors, including their complex interconnected structures and external environments. This paper proposes a fault-tolerant control scheme for DWTS that ensures their states remain within safe boundaries despite the presence of disturbances, uncertainties and faults. Firstly, considering the impacts of random consumer behavior, unpredictable process and actuator faults, the DWTS is modeled as an interconnected stochastic nonlinear system. Secondly, combining the backstepping technique with control barrier functions, a sufficient and necessary condition for guaranteeing system safety is derived. Thirdly, by minimizing a loss function constructed based on dynamic programming, we synthesize a distributed controller using neural networks and theoretically prove the safety guarantees provided by our approach. Lastly, simulations are conducted to validate the effectiveness of the proposed approach on our benchmark water transport system.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"145 ","pages":"Article 103344"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096451","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

Machine learning enabled uncertainty set for data-driven robust optimization 机器学习实现了数据驱动鲁棒优化的不确定性集

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-12-01 DOI: 10.1016/j.jprocont.2024.103339

Yun Li , Neil Yorke-Smith , Tamas Keviczky

{"title":"Machine learning enabled uncertainty set for data-driven robust optimization","authors":"Yun Li , Neil Yorke-Smith , Tamas Keviczky","doi":"10.1016/j.jprocont.2024.103339","DOIUrl":"10.1016/j.jprocont.2024.103339","url":null,"abstract":"<div><div>The way how the uncertainties are represented by sets plays a vital role in the performance of robust optimization (RO). This paper presents a novel approach leveraging machine learning (ML) techniques to construct data-driven uncertainty sets from historical uncertainty data for RO problems. The proposed method integrates Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Model (GMM), and Principle Component Analysis (PCA) systematically to eliminate the influence of uncertainty scenarios with low occurrence probability and generate a nonconvex uncertainty set that is a union of multiple basic subsets (box or ellipsoid) without sacrificing its computational tractability. In addition to presenting a comprehensive algorithm for uncertainty set development, this paper offers detailed guidelines for parameter tuning and performance analysis. By harnessing the well-established ML packages <span>scikit-learn</span>, a Python-based toolkit for implementing the proposed approach is also provided. Furthermore, a computationally efficient solution for a two-stage linear RO problem with the proposed data-driven uncertainty set is derived, alongside establishing a probabilistic guarantee of constraint satisfaction for out-of-sample uncertainties. Extensive numerical experiments, conducted on both synthetic and real-world datasets as well as an optimization-based control problem, are performed to demonstrate the efficacy of the proposed methodology.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"144 ","pages":"Article 103339"},"PeriodicalIF":3.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fault detection for T–S fuzzy systems with unmeasurable premise variables based on a two-step interval estimation method 基于两步区间估计法的具有不可测前提变量的 T-S 模糊系统的故障检测

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-11-26 DOI: 10.1016/j.jprocont.2024.103341

Meng Zhou , Yan Wu , Jing Wang , Tarek Raïssi , Vicenç Puig

{"title":"Fault detection for T–S fuzzy systems with unmeasurable premise variables based on a two-step interval estimation method","authors":"Meng Zhou , Yan Wu , Jing Wang , Tarek Raïssi , Vicenç Puig","doi":"10.1016/j.jprocont.2024.103341","DOIUrl":"10.1016/j.jprocont.2024.103341","url":null,"abstract":"<div><div>This paper proposes a fault detection strategy based on a two-step interval estimation method for T–S fuzzy systems with unmeasurable premise variables. First, an <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> observer is designed to achieve robust point estimation under Lipschitz conditions. Then, the estimated error bounds are analyzed and optimized using the <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> performance conditions to enable interval estimation. Furthermore, the residual threshold is derived from the interval estimation to achieve robust fault detection. Finally, an activated sludge process in a wastewater treatment is considered to validate the proposed method. Simulation results demonstrate that the proposed approach can provide more accurate state interval estimation and outperforms standard <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> observer design methods in addressing fault detection problems compared with existing methods.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"144 ","pages":"Article 103341"},"PeriodicalIF":3.3,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722584","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