Diagnosis of Fault Modes Masked by Control Loops with an Application to Autonomous Hovercraft Systems

Christopher Sconyers, Young-Ki Lee, Kilsoo Kim, Sehwan Oh, Dimitri Mavris, George Vachtsevanos, Nikunj Oza, Robert Mah, Rodney A. Martin, and Ioannis A. Raptis
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Full Paper
ijphm_13_007.pdf935.55 KBJuly 3, 2013 - 4:08pm

This paper introduces a methodology for the design, testing and assessment of incipient failure detection techniques for failing components/systems of an autonomous vehicle masked or hidden by feedback control loops. It is recognized that the optimum operation of critical assets (aircraft, autonomous systems, etc.) may be compromised by feedback control loops by masking severe fault modes while compensating for typical disturbances. Detrimental consequences of such occurrences include the inability to detect expeditiously and accurately incipient failures, loss of control and inefficient operation of assets in the form of fuel overconsumption and adverse environmental impact. We pursue a systems engineering process to design, construct and test an autonomous hovercraft instrumented appropriately for improved autonomy. Hidden fault modes are detected with performance guarantees by invoking a Bayesian estimation approach called particle filtering. Simulation and experimental studies are employed to demonstrate the efficacy of the proposed methods.

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Submission Keywords: 
Fault Masking
Submission Topic Areas: 
Automated reconfiguration
Model-based methods for fault detection, diagnostics, and prognosis
Systems and platform applications
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