Computers in Industry - Special issue: industrial applications of knowledge-based/expert systems
Actuator and sensor design for operation support systems
Computers in Industry
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Integrated System for Intelligent Control
Integrated System for Intelligent Control
Knowledge-Based Intelligent Techniques in Industry
Knowledge-Based Intelligent Techniques in Industry
Data Mining and Knowledge Discovery for Process Monitoring and Control
Data Mining and Knowledge Discovery for Process Monitoring and Control
Artificial Intelligence in Industrial Decision Making, Control, and Automation
Artificial Intelligence in Industrial Decision Making, Control, and Automation
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Integration techniques in intelligent operational management: a review
Knowledge-Based Systems
Pattern discovery: a data driven approach to decision support
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Parameter estimation for continuous-time models-A survey
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
Confidence estimation of the multi-layer perceptron and its application in fault detection systems
Engineering Applications of Artificial Intelligence
A direct adaptive neural command controller design for an unstable helicopter
Engineering Applications of Artificial Intelligence
Review of fault diagnosis in control systems
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
A knowledge-based architecture for distributed fault analysis in power networks
Engineering Applications of Artificial Intelligence
Dynamic independent component analysis approach for fault detection and diagnosis
Expert Systems with Applications: An International Journal
Brief paper: Applying neuro-fuzzy model dFasArt in control systems
Engineering Applications of Artificial Intelligence
Advances in Artificial Neural Systems
Defining and monitoring strategically aligned software improvement goals
PROFES'10 Proceedings of the 11th international conference on Product-Focused Software Process Improvement
Application of a dual foundation approach for construction of an intelligent system
Engineering Applications of Artificial Intelligence
Student progress assessment with the help of an intelligent pupil analysis system
Engineering Applications of Artificial Intelligence
A system for classification of time-series data from industrial non-destructive device
Engineering Applications of Artificial Intelligence
Recommender System to Analyze Student's Academic Performance
Expert Systems with Applications: An International Journal
Intelligent Systems Research in the Construction Industry
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
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Complex processes involve many process variables, and operators faced with the tasks of monitoring, control, and diagnosis of these processes often find it difficult to effectively monitor the process data, analyse current states, detect and diagnose process anomalies, or take appropriate actions to control the processes. The complexity can be rendered more manageable provided important underlying trends or events can be identified based on the operational data (Rengaswamy and Venkatasubramanian, 1992. An Integrated Framework for Process Monitoring, Diagnosis, and Control Using Knowledge-based Systems and Neural Networks. IFAC, Delaware, USA, pp. 49-54.). To assist plant operators, decision support systems that incorporate artificial intelligence (AI) and non-AI technologies have been adopted for the tasks of monitoring, control, and diagnosis. The support systems can be implemented based on the data-driven, analytical, and knowledge-based approach (Chiang et al., 2001. Fault Detection and Diagnosis in Industrial Systems. Springer, London, Great Britain). This paper presents a literature survey on intelligent systems for monitoring, control, and diagnosis of process systems. The main objectives of the survey are first, to introduce the data-driven, analytical, and knowledge-based approaches for developing solutions in intelligent support systems, and secondly, to present research efforts of four research groups that have done extensive work in integrating the three solutions approaches in building intelligent systems for monitoring, control and diagnosis. The four main research groups include the Laboratory of Intelligent Systems in Process Engineering (LISPE) at Massachusetts Institute of Technology, the Laboratory for Intelligent Process Systems (LIPS) at Purdue University, the Intelligent Engineering Laboratory (IEL) at the University of Alberta, and the Department of Chemical Engineering at University of Leeds. The paper also gives some comparison of the integrated approaches, and suggests their strengths and weaknesses.