Adaptive Kaman Filter for Fault Diagnosis of Linear Parameter-Varying Systems

Fault diagnosis of Linear Parameter-Varying (LPV)
system using an adaptive Kalman filter is proposed. The LPV model
is comprised of scheduling parameters, and the emulator parameters.
The scheduling parameters are chosen such that they are capable of
tracking variations in the system model as a result of changes in the
operating regimes. The emulator parameters, on the other hand,
simulate variations in the subsystems during the identification phase
and have negligible effect during the operational phase. The nominal
model and the influence vectors, which are the gradient of the feature
vector respect to the emulator parameters, are identified off-line from
a number of emulator parameter perturbed experiments. A Kalman
filter is designed using the identified nominal model. As the system
varies, the Kalman filter model is adapted using the scheduling
variables. The residual is employed for fault diagnosis. The
proposed scheme is successfully evaluated on simulated system as
well as on a physical process control system.

Authors:

• Rajamani Doraiswami
• Lahouari Cheded

Keywords:

• Keywords—Identification
• linear parameter-varying systems
• least-squares estimation
• fault diagnosis
• Kalman filter
• emulators

References:

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Journal of Franklin Institute, 350(5), 938-965.