Fault Detection, Isolation And Reconfiguration Research Articles

A sliding mode observer approach to oscillatory fault detection in commercial aircraft

The Flight Control System (FCS) is one of the most important systems in all modern aircraft. For such systems it is required to have robust Fault Detection Isolation and Reconfiguration (FDIR) functionalities with high detection performance. In this work we specifically consider the Oscillatory Failure Cases (OFC), which, if not mitigated, can cause additional structural loads for which the aircraft is not designed. A Sliding Mode Observer (SMO) based detection method is proposed for fast and consistent detection of these OFC faults. A benchmark of a generic aircraft FCS equipped with OFC simulation capabilities, as well as the presented solution for detection, have previously been presented within a competition at the 2020 IFAC World Congress.

FDIR Strategy for Sensor Faults During Re-entry Flight

In this study, a Fault Detection, Isolation and Reconfiguration (FDIR) strategy is proposed for dealing with the problem of single sensor faults during a re-entry flight. The proposed algorithms rely on a model based Fault Detection and Isolation. After the generation of a residual through a Kalman filter observer, detection is obtained via the estimation of residual signal statistics while isolation is obtained thorough the analysis of either residual vector direction or the residual covariance matrix, depending on which fault type has been detected. Finally, for some failure conditions, an adaptive reconfiguration strategy of the control laws is also developed. The effectiveness of the FDIR strategy has been shown through numerical simulations of single fault scenarios.

A Survey of Fault Detection, Isolation, and Reconfiguration Methods

Fault detection, isolation, and reconfiguration (FDIR) is an important and challenging problem in many engineering applications and continues to be an active area of research in the control community. This paper presents a survey of the various model-based FDIR methods developed in the last decade. In the paper, the FDIR problem is divided into the fault detection and isolation (FDI) step, and the controller reconfiguration step. For FDI, we discuss various model-based techniques to generate residuals that are robust to noise, unknown disturbance, and model uncertainties, as well as various statistical techniques of testing the residuals for abrupt changes (or faults). We then discuss various techniques of implementing reconfigurable control strategy in response to faults.

Transient Effects Related To Reconfigurable Control Design (Satellite Launcher)

Degraded performance and even instability may arise from faults or failures acting on a system. Sudden faults of small amplitude can also cause unacceptable results due to the transient amplification by controller gains, where linear working limits of system's components are exceeded. Fault Tolerance requires not only the design of a suitable Fault Detection, Isolation and Reconfiguration (FDIR) scheme. It also demands for an intensive analysis regarding the effects of such faults or failures acting on the system, taking into account the new (reconfigured) control law. The current work illustrates this idea by considering the design of a Reconfigurable Controller for a satellite launcher subject to sensor faults.

Multiple Control Surface Failure Detection Via On-Line Filter Tuning Method

Modern high performance aircraft systems require advanced onboard fault detection, isolation and reconfiguration (FDIR) techniques against system failures. In this paper, we will devise a on-line FDIE (Failure Detection Isolation and Estimation) scheme that could handle multiple and time-varing control surface failure and provide precise health status for future reconfiguration design. This approach combining LQG design with newly developed fuzzy concept would be able to deal with a variety of failure types such as constant failure (hardover), linearly degraded (run-away) and their combination. A big contribution of this approach is unanticipated failure can be successfully detected without prescribing any nominal failure models in memory.