Fault Detection and Diagnosis for Aeronautic and Aerospace Missions

Henry, David; Simani, Silvio; Patton, Ron J.

doi:10.1007/978-3-642-11690-2_3

Cited by 34 publications

(22 citation statements)

References 83 publications

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“…The fault detection and fault tolerant control problems have received increasing attention because of its broad applicability [1]. To achieve a higher reliability level and better control performance, modern control systems relying on sophisticated control algorithms have been developed to meet these critical requirements [2].…”

Section: Introductionmentioning

confidence: 99%

Active fault tolerant control systems by the semi‐Markov model approach

Huang

Shi

Xue

2013

Adaptive Control & Signal

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This paper investigates the active fault tolerant control problem via the H 1 state feedback controller. Because of the limitations of Markov processes, we apply semi-Markov process in the system modeling. Two random processes are involved in the system: the failure process and the fault detection process. Therefore, two corresponding semi-Markov processes are integrated in the closed-loop system model. This framework can generally accommodate different types of system faults, including the randomly happening sensor faults and actuator faults. A controller is designed to guarantee the closed-loop system stability with a prescribed noise/disturbance attenuation level. The controller can be readily solved by using convex optimization techniques. A vertical take-off and landing vehicle example with actuation faults is used to demonstrate the effectiveness of the proposed technique.represent the random variations of the system parameters. Normally, continuous-time finite state Markov processes are used in continuous-time systems where the system may jump at any time instants, whereas in discrete-time systems, discrete-time finite state Markov processes are applied, to be precise, only Markov process kernels are used. Sometimes, finite state processes are also called discrete-state processes, or countable state processes. To practically formulate active fault tolerant control problems, two stochastic processes are involved in the system model: one is used to model the system faults and the other one is used to represent the fault detection and identification (FDI) process [7]. The rationale behind the two-process model is that the FDI process brings random variations into the control law. In other words, the FDI process modifies the system dynamics by realizing the control strategy reconfiguration [8]. The two-process model was proposed in [9], where necessary and sufficient conditions were provided for systems with single component failures. For the system with multiple failure occurrences, the stochastic stability analysis was presented in [10]. Further, considering the system uncertainties, detection delays, and noise/disturbances, results have been reported in [11]. Besides the results on the stability issue, optimal controller design problems have been studied for fault tolerant control systems modeled by Markov processes (see [12] and the references therein).Among the aforementioned works, most of them deploy continuous-time or discrete-time Markov processes to model the system failure. To satisfy the requirements of Markov processes, the life time of the system components should be assumed to be exponentially distributed. However, such an assumption may not be appropriate in practice for two reasons. Firstly, in the reliability engineering, a typical transition/failure rate function is in a bath tube shape instead of a constant value [13,14]. With such shapes of transition/failure rates, the system components would be more likely to fail in the 'infant' or 'senior' stage. For a more comprehensive literature rev...

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Section: Introductionmentioning

confidence: 99%

Active fault tolerant control systems by the semi‐Markov model approach

Huang

Shi

Xue

2013

Adaptive Control & Signal

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“…Moreover, the uncertainty on the center of mass due to propellant motions in the tanks makes the detection and isolation more challenging. Numerous fault diagnosis methods are applicable to this problem [3,4]. In fact, most of the model-based diagnostic techniques reported in the literature have the potential to be applied (see [5 8] for good surveys).…”

Section: Motivationmentioning

confidence: 99%

A model-based solution for fault diagnosis of thruster faults: application to the rendezvous phase of the mars sample return mission

Henry

Bornschlegl

Olivé

et al. 2013

Progress in Flight Dynamics, Guidance, Navigation, Control, Fault Detection, and Avionics

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This paper addresses the design of model-based fault diagnosis schemes to detect and isolate faults occurring in the orbiter thrusters of the Mars Sample Return (MSR) mission. The proposed fault diagnosis method is based on a H(0) ¦lter with robust poles assignment to detect quickly any kind of thruster faults and a cross-correlation test to isolate them. Simulation results from the MSR ¤high-¦delity¥ nonlinear simulator provided by Thales Alenia Space demonstrate that the proposed method is able to diagnose thruster faults with a detection and isolation delay less than 1.1 s.

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“…E©cient Global Optimization [11] uses the Kriging predictor (1) and the additional con¦dence information provided by (2) to ¦nd an (n c + 1) vector in X c at which the complex simulation should be run. This point is chosen by maximizing the Expected Improvement (EI) [8]:…”

confidence: 99%

Tuning and comparing fault diagnosis methods for aeronautical systems via kriging-based optimization

Marzat

Piet-Lahanier

Damongeot

et al. 2013

Progress in Flight Dynamics, Guidance, Navigation, Control, Fault Detection, and Avionics

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Many approaches address fault detection and isolation (FDI) based on analytical redundancy. To rank them, it is necessary to de¦ne performance indices and realistic sets of test cases on which they will be evaluated. For the ranking to be fair, each of the methods under consideration should have its internal parameters tuned optimally. The work presented uses a combination of tools developed in the context of computer experiments to achieve this tuning from a limited number of numerical evaluations. The methodology is then extended so as to provide a robust tuning in the worst-case sense.

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Fault Detection and Diagnosis for Aeronautic and Aerospace Missions

Cited by 34 publications

References 83 publications

Active fault tolerant control systems by the semi‐Markov model approach

Active fault tolerant control systems by the semi‐Markov model approach

A model-based solution for fault diagnosis of thruster faults: application to the rendezvous phase of the mars sample return mission

Tuning and comparing fault diagnosis methods for aeronautical systems via kriging-based optimization

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