This paper addresses the key question that when faults occur either the aircraft system dynamics changes due to the fault or these dynamics are unknown (precisely). This question is addressed for the important case of...
详细信息
This paper addresses the key question that when faults occur either the aircraft system dynamics changes due to the fault or these dynamics are unknown (precisely). This question is addressed for the important case of Air Data Sensor failures, due to e.g. icing, for fixed wing aircraft operating in a nominal flight condition. The solution to this question uses basic ideas from subspace identification to cast this problem in linear least squares problem with convex constraints (nuclear norm and 1-norm constraints). The latter are relaxations of a rank and cardinality *** presented solution is validated using real-life flight test data. Copyright (c) 2024 The Authors.
Subspace-based fault detection methods are widely used for linear time-invariant systems. For linear time-periodic systems, those methods cannot be theoretically used, due to the intrinsic assumptions associated with ...
详细信息
ISBN:
(纸本)9781713872344
Subspace-based fault detection methods are widely used for linear time-invariant systems. For linear time-periodic systems, those methods cannot be theoretically used, due to the intrinsic assumptions associated with those methods in the context of linear time-invariant models. based on the approximation of time-periodic systems as time-invariant ones, those methods can still be applied and adapted to perform change detection for time-periodic systems, through a Gaussian residual built upon the identified modal parameters and their estimated variances. The proposed method is tested and validated on a small numerical model of a rotating wind turbine, with detection and isolation of a blade stiffness reduction leading to rotor anisotropy. Copyright (c) 2023 The Authors.
In this paper, subspace identification for wind turbines and more generally rotating periodic systems are investigated. Previous works have stressed the difficulty of modeling such systems as Linear Time Invariant and...
详细信息
In this paper, subspace identification for wind turbines and more generally rotating periodic systems are investigated. Previous works have stressed the difficulty of modeling such systems as Linear Time Invariant and thus to apply classical Stochastic Subspace identification. Such works plead for periodic or augmented theories. In this paper, the classical SSI can be applied to recover modal information that is related to the eigenstructure of the instrumented system despite the system excitation being modeled as non-stationary. copyright (C) 2022 The Authors.
Subspace-based fault detection methods are widely used for linear time-invariant systems. For linear time-periodic systems, those methods cannot be theoretically used, due to the intrinsic assumptions associated with ...
详细信息
Subspace-based fault detection methods are widely used for linear time-invariant systems. For linear time-periodic systems, those methods cannot be theoretically used, due to the intrinsic assumptions associated with those methods in the context of linear time-invariant models. based on the approximation of time-periodic systems as time-invariant ones, those methods can still be applied and adapted to perform change detection for time-periodic systems, through a Gaussian residual built upon the identified modal parameters and their estimated variances. The proposed method is tested and validated on a small numerical model of a rotating wind turbine, with detection and isolation of a blade stiffness reduction leading to rotor anisotropy.
暂无评论