Surface failure detection for F/A-18 aircraft using neural networks and fuzzy logic

Raza, Haider; Ioannou, Pétros; Youssef, Habib

doi:10.1109/icnn.1994.374776

Cited by 5 publications

(9 citation statements)

References 5 publications

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“…Thus, from the computational point of view, the fault detection algorithm is reduced to the comparison of the largest eigenvalue (spectral radius) of the aboveintroduced Wishart matrix (5), which is calculated on the base of a representative sample and 2 ,M , and fulfillment of a decision on the base of the decision rule (15).…”

Section: Corollarymentioning

confidence: 99%

“…In References [14][15][16][17][18] the neural network-based methods to detect sensor/actuator failures are developed and discussed. In Reference [14] a neural network is proposed as an approach to the task of failure detection, following the damage to an aerodynamic surface of an aircraft flight control system.…”

Section: Introductionmentioning

confidence: 99%

“…This structure, used for state estimation purpose, can be designed and trained on line during flight and generates a residual signal that indicates the damage as soon as it occurs. In [15] the problem of detecting control surface failures for a high-performance aircraft is considered. The detection model is developed using the linear dynamic model of an F/A-18 aircraft.…”

Section: Introductionmentioning

confidence: 99%

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Testing the covariance matrix of the innovation sequence with sensor/actuator fault detection applications

Hajiyev

2010

Adaptive Control & Signal

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Operative methods for testing the covariance matrix of the innovation sequence of the Kalman filter are proposed. The quadratic form of the random Wishart matrix is used in this process as a monitoring statistic, and the testing problem is reduced to the classical problem of minimization of a quadratic form on the unit sphere. As a result, two algorithms for testing the covariance matrix of the innovation sequence are proposed. In the first algorithm, the sum of all elements of the matrix is used as the scalar measure of the Wishart matrix that is being tested, while in the second algorithm, the largest eigenvalue of this matrix is used. In the simulations, the longitudinal and lateral dynamics of the F-16 aircraft model are considered, and the detection procedure of sensor/actuator faults, which affect the covariance matrix of the innovation sequence, is examined. Some recommendations for the fastest detection of the fault are given.

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Section: Corollarymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Testing the covariance matrix of the innovation sequence with sensor/actuator fault detection applications

Hajiyev

2010

Adaptive Control & Signal

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“…As the approach requires several parallel Kalman filters, and the faults should be known, it can be used in limited applications. In the references (Napolitano et al, 1993;Raza, et al, 1994;Napolitano, et al, 1996;Borairi & Wang, 1998;Alessandri, 2003) the neural network based methods to detect sensor/actuator failures are developed and discussed. In the reference (Napolitano et al, 1993) a neural network is proposed as an approach to the task of failure detection following damage to an aerodynamic surface of an aircraft flight control system.…”

mentioning

confidence: 99%

“…This structure, used for state estimation purpose, can be designed and trained on line in flight and generates a residual signal indicating the damage as soon as it occurs. In (Raza et al, 1994) the problem of detecting control surface failures of a high performance aircraft is considered. The detection model is developed using a linear dynamic model of an F/A-18 aircraft.…”

mentioning

confidence: 99%

Sensor and Actuator/Surface Failure Detection Based on the Spectral Norm of an Innovation Matrix

Hajiyev¹

2010

Fault Detection

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Sensor and control surface/actuator failure detection and isolation applied to F‐16 flight dynamic

Hajiyev

Çalışkan

2005

Aircraft Engineering and Aerospace Technology

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Purpose -The purpose of the paper is to present an approach to detect and isolate the aircraft sensor and control surface/actuator failures affecting the mean of the Kalman filter innovation sequence. Design/methodology/approach -The extended Kalman filter (EKF) is developed for nonlinear flight dynamic estimation of an F-16 fighter and the effects of the sensor and control surface/actuator failures in the innovation sequence of the designed EKF are investigated. A robust Kalman filter (RKF) is very useful to isolate the control surface/actuator failures and sensor failures. The technique for control surface detection and identification is applied to an unstable multi-input multi-output model of a nonlinear AFTI/F-16 fighter. The fighter is stabilized by means of a linear quadratic optimal controller. The control gain brings all the eigenvalues that are outside the unit circle, inside the unit circle. It also keeps the mechanical limits on the deflections of control surfaces. The fighter has nine state variables and six control inputs. Findings -In the simulations, the longitudinal and lateral dynamics of an F-16 aircraft dynamic model are considered, and the sensor and control surface/actuator failures are detected and isolated. Research limitations/implications -A real-time detection of sensor and control surface/actuator failures affecting the mean of the innovation process applied to the linearized F-16 fighter flight dynamic is examined and an effective approach to isolate the sensor and control surface/actuator failures is proposed. The nonlinear F-16 model is linearized. Failures affecting the covariance of the innovation sequence is not considered in the paper. Originality/value -An approach has been proposed to detect and isolate the aircraft sensor and control surface/actuator failures occurred in the aircraft control system. An extended Kalman filter has been developed for the nonlinear flight dynamic estimation of an F-16 fighter. Failures in the sensors and control surfaces/actuators affect the characteristics of the innovation sequence of the EKF. The failures that affect the mean of the innovation sequence have been considered. When the EKF is used, the decision statistics changes regardless the fault is in the sensors or in the control surfaces/actuators, while a RKF is used, it is easy to distinguish the sensor and control surface/actuator faults.

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Surface failure detection for F/A-18 aircraft using neural networks and fuzzy logic

Cited by 5 publications

References 5 publications

Testing the covariance matrix of the innovation sequence with sensor/actuator fault detection applications

Testing the covariance matrix of the innovation sequence with sensor/actuator fault detection applications

Sensor and Actuator/Surface Failure Detection Based on the Spectral Norm of an Innovation Matrix

Sensor and control surface/actuator failure detection and isolation applied to F‐16 flight dynamic

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