A collaborative, multi-agent based methodology for abnormal events management
Modern chemical plants have complicated unit operations with considerable recycles. The complex controls and instrumentation installed often compensate and conceal faults, causing many faults in the process to remain undetected, until serious consequences occur. This thesis strives to explore new methodologies suitable for fault detection and identification (FDI) during transient mode of operations. Though the emphasis of this thesis is mainly on transient operations, the proposed methodologies are generic and can be applied to steady-state operations as well.A novel framework based on multi-agent approach has been developed for detecting and diagnosing faults in the process industries by integrating various data driven fault detection and identification techniques. Three major data-driven approaches, namely, self-organizing map (SOM), principal components analysis (PCA), and kernel density estimator (KDE) were extended in this thesis to the domain of transient operations.
fault diagnosis, monitoring, multi-agent, data-based methods
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