Artificial intelligence for monitoring and supervisory control of process systems
Engineering Applications of Artificial Intelligence
Fault diagnosis using dynamic trend analysis: A review and recent developments
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Fuzzy-genetic algorithm for automatic fault detection in HVAC systems
Applied Soft Computing
A fuzzy inference system for fault detection and isolation: Application to a fluid system
Expert Systems with Applications: An International Journal
A hybrid intelligent system for fault detection and sensor fusion
Applied Soft Computing
A neural network-based multi-agent classifier system
Neurocomputing
Features extraction and analysis for classifying causable patterns in control charts
Computers and Industrial Engineering
Trends extraction and analysis for complex system monitoring and decision support
Engineering Applications of Artificial Intelligence
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In this paper, we review the major achievements on the research of fault diagnosis in control systems (FDCS) from three aspects which including fault detection, fault isolation and hybrid intelligent fault diagnosis. Fault detection and isolation (FDI) are two important stages in the diagnosis process while hybrid intelligent fault diagnosis is the hot issue in current research field. The particular feature of FDCS is using the closed-loop monitoring information in control system to establish the quantitative and qualitative process model, detecting and then isolating the main failures in sensors, actuators, and the controlled process; the main challenge of FDCS is reducing the false alarm rate and missing alarm rate, improving the sensitivity and rapidity. The robust fault detection in the transition process, the knowledge acquisition for quantitative and qualitative diagnosis based on process history data, and hybrid intelligent fault diagnosis system architecture are worthy of a deeper research.