Fault Detection and Identification in Process Systems

Dear Colleagues,

Process systems, encompassing sectors such as metallurgy and petrochemicals, constitute a foundational pillar of the world economy by virtue of their continuous, large-scale production of primary materials. The ongoing centralization and scaling of modern industrial operations have rendered automated systems and devices indispensable for ensuring safety, stability, and efficiency in production environments. In recent years, the accelerated advancement of artificial intelligence and allied technologies including machine learning, large-language models, the Industrial Internet, digital twins, and embodied intelligence has catalyzed a profound transformation toward intelligent operation and maintenance within these industries. At the information systems level, architectural paradigms are evolving from conventional knowledge-driven frameworks toward trusted data-driven methodologies, thereby enabling precise modeling, real-time monitoring, and diagnostic analysis of energy and material flows. Concurrently, execution units in physical systems are increasingly oriented toward safety-assured operation and maintenance, progressively coalescing into an integrated cyber–physical ecosystem in which information systems serve as the intelligent decision-making core and physical systems act as reliable execution agents. This convergence establishes a robust technological foundation for achieving enhanced operational safety across process industries.

This Special Issue on “Fault Detection and Identification in Process Systems” seeks high-quality works focusing on both academia and industry that advance scholarly understanding and engineering practice. Interdisciplinary research and collaborations spanning industry, academia, and research institutions are especially encouraged. The Special Issue aims to address the full technological spectrum, from control-theoretic foundations and intelligent algorithms to the implementation and engineering demonstration of automated and intelligent systems, with the objective of illustrating the scalability and practical applicability of proposed solutions within the framework of safety-critical process systems. Topics include, but are not limited to, the following:

• Theory of Trusted Data-Driven Dynamic Modeling and Fault Characterization for Process Systems;
• Safety-Domain Modeling and Anomaly Propagation Mechanisms in Cyber-Physical Process Systems;
• Early Detection and Identification of Incipient and Intermittent Faults in Process Systems;
• Intelligent Fault Detection and Diagnosis for Non-Gaussian and Non-Stationary Process Data;
• Real-Time Health Assessment of Process Equipment via Digital Twins and the Industrial Internet;
• Application of Large-Scale Pretrained Models in Process Monitoring and Fault Knowledge Discovery;
• Safety-Constrained Fault-Tolerant Control and Autonomous Decision-Making System Design;
• Embodied Intelligence for Process Inspection and Fault Localization in Hazardous Environments;
• Interpretability, Robustness, and Safety Verification of Intelligent Operation and Maintenance Systems;
• Plant-Wide Cooperative Fault Prognostics and Intelligent Maintenance Scheduling Optimization;
• Comprehensive Application of Large and Small Models in Process Optimization;
• Application of Multi-Sensors in Fault Detection and Identification;
• Digital Twins and Machine Learning Technology for Fault Detection, Decision-making, and Control;
• System Fault Identification and Rapid Response;
• Data-Driven Prediction, Identification, and Impact Assessment of System Faults.

Dr. Siwei Lou
Dr. Hu Qiao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• fault detection and diagnosis
• process industries
• trusted data-driven methods
• intelligent operation and maintenance
• digital twins
• industrial artificial intelligence
• predictive maintenance
• explainable AI