Robust Model-based Fault Diagnosis Research Articles

Design Methodology for Robust Model-Based Fault Diagnosis Schemes and its Application to an Aircraft Hydraulic Power Package

In a system’s design phase, where knowledge about the actual behavior of the system is shallow, the design of an efficient and robust system diagnostics is a complex task. In order to simplify this process, this paper presents a modelbased methodology for the design of fault diagnosis schemes. The methodology analyzes the structure of available behavioral models of the system and proposes minimal sets of sensors to fulfill diagnostic requirements. In order to choose an optimal set of sensors, the method evaluates the sets in terms of costs and diagnostic robustness. The proposed fault detection, isolation and identification schemes rely on the robust evaluation of model-based residuals using Monte-Carlo methods and highest density regions to account for measurement and parameter uncertainty. To show the design capabilities, the presented method is applied to an aircraft hydraulic power package and the resulting schemes are tested on a real test rig.

Robust model-based fault diagnosis of mechanical drive train in V47/660 kW wind turbine

In this study, a robust fault diagnosis scheme for V47/660 kW wind turbine is proposed. A comprehensive mathematical model for mechanical drive train and gearbox dynamic of V47/660 kW wind turbine operating in Manjil wind farm, Gilan province, Iran, is developed based on which the model-based Fault Detection and Isolation (FDI) scheme is designed. The proposed FDI scheme detects various critical and common sensor faults, actuator faults and components faults. A mixed Unknown Input-Proportional Integral Observer (UI-PIO) method and the parameter estimation method are used for fault detection and isolation. The robustness of the residuals to disturbances is also addressed. Simulation results using experimental data are presented to demonstrate the effectiveness of the proposed fault diagnosis system.

Model-based approach for fault diagnosis using set-membership formulation

This paper describes a robust model-based fault diagnosis approach that enables to enhance the sensitivity analysis of the residuals. A residual is a fault indicator generated from an analytical redundancy relation which is derived from the structural and causal properties of the signed bond graph model. The proposed approach is implemented in two stages. The first stage consists in computing the residuals using available input and measurements while the second level leads to moving horizon residuals enclosures according to an interval consistency technique. These enclosures are determined by solving a constraint satisfaction problem which requires to know the derivatives of measured outputs as well as their boundaries. A numerical differentiator is then proposed to estimate these derivatives while providing their intervals. Finally, an inclusion test is performed in order to detect a fault upon occurrence. The proposed approach is well suited to deal with different kinds of faults and its performances are demonstrated through experimental data of an omni-directional robot.

A model-based fault diagnosis in a nonlinear bioreactor using an inverse problem approach and evolutionary algorithms

Nonlinear bioreactors are considered essential technology in chemical and biochemical industries. This paper presents a proposal of a robust model based fault diagnosis in a nonlinear bioreactor, formulated as the solution of an inverse problem. The optimization problem is solved by using four different evolutionary strategies: Particle Swarm Optimization (PSO), Differential Evolution (DE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Particle Swarm Optimization with Memory (PSO-M), with DE resulting the best according to the evaluated quantitative indicators. The results obtained with this approach indicate advantages in comparison to other methods of fault diagnosis (FDI) present in literature.

Robust Model-based Fault Diagnosis of Thruster Faults in Spacecraft

The research work presented in this paper addresses the design and validation of a robust model-based fault diagnosis system to detect and isolate any thruster fault occurring in the thruster-based propulsion system of the chaser spacecraft during rendezvous on a circular orbit around the Mars planet. The REFERENCE mission is the ESA Mars Sample Return (MSR) mission. Key features of the proposed method are the use of a Hi/H∞ filter for robust fault detection and a bank of unknown input observers jointly used with a dot product of vectors strategy for fault isolation. Robust performance over a large range of uncertainties (a total of 36 uncertainties is considered) is formally demonstrated through the μ3 formalism.

Robust model-based fault diagnosis for air handling units

Fault detection and diagnosis (FDD) for heating, ventilation and air conditioning (HVAC) equipment significantly impact energy consumption in both residential and commercial buildings. In most modern building management systems (BMS), HVAC historical data logs in large quantity and high resolution are recorded and available for further online or offline analysis, including automated FDD. In this paper, a model-based fault diagnosis method is developed by applying support vector machine (SVM) techniques to model parameters recursively calculated by an online estimator. The estimator presumes an autoregressive time series model with exogenous variables (ARX). A real-world air handling unit (AHU) dataset containing process variables measured at regular intervals is pre-processed by the online estimation algorithm. Each data vector in the original dataset (measured), together with a small number of appropriately selected lags, is converted to a parameter vector representing the state at the same instant. The set of parameter vectors is sub-divided into classes by SVM, enabling fault classification. Validation via experimental data demonstrates that the proposed hybrid approach produce superior performance measured by F-measure scores compared to alternative methods. Robustness to model uncertainty is also established.

Model-based Robust Fault Diagnosis for Satellite Control Systems Using Learning and Sliding Mode Approaches

In this paper, our recent work on robust model-based fault diagnosis (FD) for several satellite control systems using learning and sliding mode approaches are summarized. Firstly, a variety of nonlinear mathematical models for these satellite control systems are described and analyzed for the purpose of fault diagnosis. These satellite control systems are classified into two classes of nonlinear dynamical systems. Then, several fault diagnostic observers using sliding mode and learning approaches are presented. Sliding mode with time-varying switching gains, second order sliding mode, and high order sliding mode differentiators are respectively used in the proposed diagnostic observers to deal with modeling uncertainties. Neural model-based and iterative learning algorithms-based online learning estimators are respectively used in the diagnostic observers for the purpose of isolating and estimating faults. Finally, conclusions and future work on the health monitoring and fault diagnosis for satellite control systems are provided.

Development of a robust model-based fault diagnosis technique for Reusable Launch Vehicles. A case study

This paper deals with the design of robust Fault Detection and Isolation (FDI) filters for atmospheric re-entry vehicles subjected to actuator faults. The FDI technique is based on carefully chosen linear models of the controlled vehicle about the available on-board reference trajectories. The modelling process allows for both Lateral/directional and longitudinal motions. Design trade-offs are formulated and managed as H∞/H_ specifications. Nonlinear simulations show the effectiveness of the proposed scheme despite atmospheric disturbances and measurement noises.

Passive robust fault detection using fuzzy parity equations

In this paper, a new approach for robust fault detection based on fuzzy parity equations is presented. The problem of robustness is handled using a passive approach based on generating a fuzzy adaptive threshold. The fuzzy adaptive threshold will be generated using fuzzy parity equations. Fuzzy parity equations are a generalisation of crisp parity equations but in this case the parameters in the used model are modelled using fuzzy parameters. Then, the residuals produced by fuzzy parity equations will also be fuzzy numbers. The extension from crisp parity equations to fuzzy parity equations is based on the extension principle of Zadeh [Inform. Contr. 8 (1965) 338; Inform. Sci. 89 (1975) 43]. Two possible crisp parity equations can be considered, according to Gertler [Fault Detection and Diagnosis in Engineering Systems, Marcel Dekker, New York, 1998] the moving average (MA) parity equation and autoregressive-moving average (ARMA) parity equations. When generalising these crisp parity equations to the fuzzy case, different problems appear when MA or ARMA parity equation approach is used. The underlying approach of fuzzy parity equations is the approach based on interval parity equations. Interval parity equations is an extension of crisp parity equations in the case of substituting crisp parameters in model equations by intervals. These intervals reflect the uncertainty on model parameters. When applying the extension principle of Zadeh [Inform. Contr. 8 (1965) 338; Inform. Sci. 89 (1975) 43] to extend crisp parity equations to fuzzy parity equations, a set of interval parity equations must be evaluated at different α-cuts. Interval parity equations have been extensively studied and applied in the literature, using two approaches: the simulation approach [Robust model-based fault diagnosis: the state of the art, in: Proceedings of the IFAC Symposium (SAFEPROCESS’94), 1994] and the prediction approach [IEEE Expert Intell. Syst. Appl. (1997)]. These two approaches can be deduced directly from crisp parity equations, in particular, from ARMA and MA approaches. Properties of interval parity equations (MA and ARMA) will be presented and compared. Finally, the proposed fuzzy parity equations will be tested on an example.

Editorial

International Journal of Robust and Nonlinear ControlVolume 10, Issue 14 p. 1153-1154 EditorialFree Access Editorial Henrik Niemann, Henrik Niemann Guest Editor Department of Automation, Technical University of Denmark, DK-2800 Lyngby, DenmarkSearch for more papers by this author Henrik Niemann, Henrik Niemann Guest Editor Department of Automation, Technical University of Denmark, DK-2800 Lyngby, DenmarkSearch for more papers by this author First published: 21 November 2000 https://doi.org/10.1002/1099-1239(20001215)10:14<1153::AID-RNC577>3.0.CO;2-ECitations: 3AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. REFERENCES 1 Patton R. Fault tolerant control: the 1997 situation. In Proceedings of the IFAC Symposium SAFEPROCESS'97, Hull, England, 1997; 1033– 1055. 2 Beard RV. Failure accommondation in linear systems through self-reorganization. Ph.D. Thesis, Department of Aeronautics and Astronautics, MIT, Cambridge, MA, 1971. 3 Jones HL. Failure detection in linear systems. Ph.D. Thesis, Department of Aeronautics and Astronautics, MIT, Cambridge, MA, 1973. 4 Basseville M, Nikiforov IV. Detection of Abrupt Changes — Theory and Application. Prentice Hall: Englewood Cliffs, NJ. 1993. 5 Chen J, Patton R. Robust Model-Based Fault Diagnosis for Dynamic Systems. Kluwer: Dordrecht, 1998. 6 Gertler J. Fault Detection and Diagnosis in Engineering Systems. Marcel Dekker: New York, 1998. 7 Mangoubi RS. Robust Estimation and Failure Detection: A Concise Treatment. Springer: Berlin, 1998. 8 Patton R, Frank P, and Clark R. Fault Diagnosis in Dynamic Systems — Theory and Applications. Prentice Hall: Englewood Cliffs, 1989. 9 Willsky AS. A survey of design methods for failure detection in dynamic systems. Automatica 1976; 12: 601– 611. 10 Massoumnia M, Verghese GC, Willsky AS. Failure detection and identification. IEEE Transactions on Automatic Control 1989; 34: 316– 321. Citing Literature Volume10, Issue14Special Issue: Fault Detection and Identification15 December 2000Pages 1153-1154 ReferencesRelatedInformation

On the Application of the Generalised Structured Singular Value to Robust FDI System Design

The generalised structured singular value has proven to be very useful for solving the model validation problem within the robust control framework, This paper considers its application to the design of robust model-based fault diagnosis systems, Three different fields of use are addressed, starting with the determination of model uncertainty, which is of crucial importance for all model-based fault diagnoses, Second, frequency domain model validation is directly used for robust fault detection and isolation, Fault diagnosis for an aircraft actuator is presented as an illustrative application example, Finally, a new idea towards a compact formulation of the FDI optimisation problem, based on the generalised structured singular value, is introduced.