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Concurrent Fault Diagnosis Based on an Extended Kalman Filter | IEEE Conference Publication | IEEE Xplore

Concurrent Fault Diagnosis Based on an Extended Kalman Filter


Abstract:

This work introduces a novel methodology to detect, isolate, and estimate (i.e. to diagnose) simultaneous faults in a class of nonlinear systems, when faults are constant...Show More

Abstract:

This work introduces a novel methodology to detect, isolate, and estimate (i.e. to diagnose) simultaneous faults in a class of nonlinear systems, when faults are constant and they occur in the system's actuators. The methodology augments the system's model by adding k new states representing, each one, the dynamics of a fault. Then, it builds an extended Kalman Filter (EKF) to estimate the faults. An advantage of our approach lies in the fact that a single filter can be used to diagnose all the faults, even if they occur simultaneously, and thus, the designing process is significantly reduced. The fault estimation can be useful in the subsequent Fault Tolerant Control stages. In order to show the effectiveness of the proposed approach, experimental results computed from the actual data logged obtained from a real three-wheel omnidirectional mobile robot are shown.
Date of Conference: 10-12 November 2021
Date Added to IEEE Xplore: 14 December 2021
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Conference Location: Mexico City, Mexico

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References

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