Fault detection and isolation in automated controlled process based on equipment-reliability studies

Automated controlled process is vulnerable to faults. Faults can be amplified by the closed loop control systems and they can develop into malfunction of the loop. The closed loop may alternatively hide a fault from being observed until eventually developed to a state where loop failure is inevitable. A control loop failure will easily cause production stop or malfunction at a plant. A way to achieve a stable and effective automated system is to support online fault detection and isolation through equipment- reliability studies. The objective of this paper is to present a framework to support fault diagnosis through reliability-equipment studies. The main idea is to employ Eigen Value Analysis (EVA) and Importance Analysis (IA) to provide insight on equipment performance. The equipment of offshore industries is considered according to OREDA classification. At first EVA is used for analyzing the performance of the equipment and introducing reliability as the most prominent technical performance character. IA is then performed to develop equipment reliability analysis and classify their components based on the Component Criticality Measures (CCM). The analysis of equipment reliability can ferret out the leading causes and common-cause events to pave a way toward knowledge acquisition which enhance online fault diagnosis performance.