Fault Detection And Isolation Filter Design Research Articles

Fault Detection and Isolation for a Class of Nonlinear Systems Based on Gerschgorin Theorem and Optimization Approach

This article is concerned with the fault detection and isolation (FDI) problem for a class of nonlinear systems described by the T–S fuzzy models. Based on the concept of minimum unobservability subspace and geometric property of factor space, a set of FDI filters where each residual is only affected by one fault and completely decoupled from other faults is designed. Furthermore, in the decoupling space, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }/H_{-}$ </tex-math></inline-formula> performance indexes are provided to enhance the sensitivity of residual to faults and robustness to disturbances. In particular, to solve the nonconvex filter design problem caused by introducing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{-}$ </tex-math></inline-formula> index, the Gerschgorin theorem is first used to linearize the corresponding filter design conditions in the outer region of a ball. Then, the FDI filter design problem is converted into a convex optimization one, which is solved via the linear matrix inequality (LMI) control Toolbox, and the advantages and effectiveness of the proposed FDI method are verified through two simulation examples.

Fault Detection and Isolation for Affine Fuzzy Systems With Sensor Faults

This paper investigates the fault detection and isolation (FDI) problem for a class of nonlinear systems with sensor outage faults. The considered nonlinear systems are described as affine fuzzy models, and the system outputs are chosen as the premise variables of fuzzy models. Different from the existing results, the influence of sensor faults on premise variables is considered. As a result, the well-known parallel distributed compensation scheme cannot be used for FDI filters design. By using the structural information encoded in the fuzzy rules, the affine fuzzy system is represented by multiple operating-regime-based models in fault-free case and faulty cases. In the multiple-model scheme, a bank of piecewise FDI filters are constructed, each of them is based on the affine fuzzy model that describes the system in the presence of a specified fault. The fault-dependent residual signals generated from the filters are used for detecting and isolating the specified fault. The FDI filter design conditions are obtained in the formulation of linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness and merits of the proposed method.

A new approach to deal with sensor errors in structural controls with MR damper

As commonly known, sensor errors and faulty signals may potentially lead structures in vibration to catastrophic failures. This paper presents a new approach to deal with sensor errors/faults in vibration control of structures by using the Fault detection and isolation (FDI) technique. To demonstrate the effectiveness of the approach, a space truss structure with semi-active devices such as Magneto-Rheological (MR) damper is used as an example. To address the problem, a Linear Matrix Inequality (LMI) based fixed-order H FDI filter is introduced and designed. Modeling errors are treated as uncertainties in the FDI filter design to verify the robustness of the proposed FDI filter. Furthermore, an innovative Fuzzy Fault Tolerant Controller (FFTC) has been developed for this space truss structure model to preserve the pre-specified performance in the presence of sensor errors or faults. Simulation results have demonstrated that the proposed FDI filter is capable of detecting and isolating sensor errors/faults and actuator faults e.g., accelerometers and MR dampers, and the proposed FFTC can maintain the structural vibration suppression in faulty conditions.

Fault Tolerant Control for Civil Structures Based on LMI Approach

The control system may lose the performance to suppress the structural vibration due to the faults in sensors or actuators. This paper designs the filter to perform the fault detection and isolation (FDI) and then reforms the control strategy to achieve the fault tolerant control (FTC). The dynamic equation of the structure with active mass damper (AMD) is first formulated. Then, an estimated system is built to transform the FDI filter design problem to the static gain optimization problem. The gain is designed to minimize the gap between the estimated system and the practical system, which can be calculated by linear matrix inequality (LMI) approach. The FDI filter is finally used to isolate the sensor faults and reform the FTC strategy. The efficiency of FDI and FTC is validated by the numerical simulation of a three-story structure with AMD system with the consideration of sensor faults. The results show that the proposed FDI filter can detect the sensor faults and FTC controller can effectively tolerate the faults and suppress the structural vibration.

Fault detection and fault tolerant control of a smart base isolation system withmagneto-rheological damper

Fault detection and isolation (FDI) in real-time systems can provide early warnings forfaulty sensors and actuator signals to prevent events that lead to catastrophic failures. Themain objective of this paper is to develop FDI and fault tolerant control techniques for baseisolation systems with magneto-rheological (MR) dampers. Thus, this paper presents afixed-order FDI filter design procedure based on linear matrix inequalities (LMI). Thenecessary and sufficient conditions for the existence of a solution for detecting and isolatingfaults using the formulation is provided in the proposed filter design. Furthermore, an FDI-filter-basedfuzzy fault tolerant controller (FFTC) for a base isolation structure model was designed topreserve the pre-specified performance of the system in the presence of various unknownfaults. Simulation and experimental results demonstrated that the designed filter cansuccessfully detect and isolate faults from displacement sensors and accelerometers whilemaintaining excellent performance of the base isolation technology under faulty conditions.

Development of an FDI Filter for the Hopper RLV during Re-entry

This paper presents the design process and evaluation of a fault detection and isolation (FDI) filter for the provision of health monitoring capabilities on the Hopper reusable launch vehicle (RLV) during a period of the re-entry. A high fidelity nonlinear model of the Hopper RLV is employed, with NDI control providing a robust vehicle response during unfaulted vehicle operation. Two fault scenarios are considered; faults in the rudder actuator and sideslip sensor. A robust FDI filter design procedure is developed based on H-infinity FDI theory. Scheduling of designed LTI filters is employed to overcome the inherent variation in the vehicle's dynamical characteristics during re-entry. A key feature of the developed design process is that FDI filters are designed in open-loop, despite the vehicle dynamics being unstable. Monte-Carlo analysis performed using nonlinear simulations demonstrates the robustness and effectiveness of the proposed FDI approach.

Linear Paramater-Varying Detection Filter Design for a Boeing 747-100/200 Aircraft

We present a fault detection and isolation (FDI) filter design using a linear parameter-varying (LPV) model of the longitudinal dynamics of a Boeing 747 series 100/200. The LPV FDI filter design is based on an extension of the fundamental problem of residual generation concepts elaborated for linear, time-invariant systems. Typically, the FDI filters are designed for open-loop models, and applied in closed loop. An application is shown of an LPV FDI filter for actuator failure detection and isolation in the closed-loop longitudinal LPV system to the full nonlinear Boeing 747 aircraft simulation, which represents the “true” system.