In this paper, the problem of fault diagnosis in distributed parameter systems (DPS) is investigated. The behavior of DPS is best described by partial differential equation (PDE) models. In contrast to transforming the DPS into a finite set of ordinary differential equations (ODE) prior to the design of control or fault detection schemes by using significant approximations, thus reducing the accuracy and reliability of the overall system, in this paper, the PDE representation of the system is directly utilized to construct a fault detection observer. A fault is detected by comparing the detection residual, which is the difference between measured and estimated outputs, with a predefined detection threshold. Once the fault is detected, an adaptive approximator is activated to learn the fault function. The estimated fault parameters are then compared with their failure thresholds to provide an estimate of the remaining useful life of the system. The scheme is verified in simulations on a heat system which is described by parabolic PDEs.
Fault Diagnosis of Distributed Parameter Systems Modeled by Linear Parabolic Partial Differential Equations With State Faults
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received June 5, 2016; final manuscript received July 10, 2017; published online September 8, 2017. Assoc. Editor: Davide Spinello.
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Ferdowsi, H., and Jagannathan, S. (September 8, 2017). "Fault Diagnosis of Distributed Parameter Systems Modeled by Linear Parabolic Partial Differential Equations With State Faults." ASME. J. Dyn. Sys., Meas., Control. January 2018; 140(1): 011010. https://doi.org/10.1115/1.4037332
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