Abstract

This paper presents an adaptive sensor fault diagnosis and accommodation scheme for multiple sensor bias faults for a class of input-output nonlinear systems subject to modeling uncertainty and measurement noise. The proposed scheme consists of a nonlinear estimation model that includes an adaptive component which is initiated upon the detection of a fault, in order to approximate the magnitude of the bias faults. A detectability condition characterizing the class of detectable sensor bias faults is derived and the robustness and stability properties of the adaptive scheme are presented. The estimation of the magnitude of the sensor bias faults allows the identification of the faulty sensors and it is also used for fault accommodation purposes. The effectiveness of the proposed scheme is demonstrated through a simulation example.

Highlights

  • In recent years various fault diagnosis schemes have been proposed for detecting, isolating and accommodating faults for various classes of linear and nonlinear systems

  • Learning algorithms have been used to facilitate the identification of the fault type, that is whether the fault is a process or a sensor one, by learning the potential fault function that has occurred and, providing an estimation of the fault function so that it can be used in fault accommodation schemes

  • The measurement noise is generated from a uniform distribution with 3% uncertainty of the state and the bound used for the task of fault detection is ξd = 0.07

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Summary

INTRODUCTION

In recent years various fault diagnosis schemes have been proposed for detecting, isolating and accommodating faults for various classes of linear and nonlinear systems (see, for instance, [1]–[5] and references therein). In earlier works [14], [18], the case of single sensor faults was considered and individual estimation models utilizing learning were designed for each potential sensor fault, so that the identification of the faulty sensor could be made on an exclusion-based logic. As it is the always the case with this approach, there is the risk of non-conclusive fault isolation due to the inability to exclude potential faults. In [8] the problem of multiple sensor fault detection and isolation was investigated for the same class of systems considered in this paper and the use of learning was made for enhancing fault detectability by estimating the modeling uncertainty.

PROBLEM FORMULATION
ADAPTIVE FAULT DIAGNOSIS ARCHITECTURE
FAULT DETECTABILITY ANALYSIS
STABILITY AND APPROXIMATION PERFORMANCE
SENSOR FAULT ACCOMMODATION
SIMULATION RESULTS
VIII. CONCLUSION

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