Fault detection and isolation in nonlinear systems

This paper deals with fault detection and identification in dynamic systems when the system dynamics can be modeled by smooth nonlinear differential equations including affine, bilinear or linear parameter varying (LPV) systems. Two basic approaches will be considered, these apply differential algebraic and differential geometric tools.In the differential algebraic approach the state elimination methods will be used to derive nonlinear parity relations. In the specific case when a reconstruction of the fault signal is needed the dynamic inversion based approach will be investigated. This approach will also be studied from geometric point of view. The geometric approach, as proposed by Isidori and De Persis, is suitable to extend the detection filter and unknown input observer design approaches (well elaborated for LTI systems) to affine nonlinear systems.Beyond the development of the theory of fault detection and identification it is equally important to offer computable methods and to analyze the robustness properties against uncertainties. Both the observer based and the inversion based approaches will be elaborated for LPV systems that may offer computational tools inherited from linear systems and also allow to design for robustness utilizing results from robust filtering and disturbance attenuation.     

Outlier detection based on Gaussian process with application to industrial processes

Due to the extensive usage of data-based techniques in industrial processes, detecting outliers for industrial process data become increasingly indispensable. This paper proposes an outlier detection scheme that can be directly used for either process monitoring or process control. Based on traditional Gaussian process regression, we develop several detection algorithms, of which the mean function, covariance function, likelihood function and inference method are specially devised. Compared with traditional detection methods, the proposed scheme has less postulation and is more suitable for modern industrial processes. The effectiveness of the proposed scheme is verified by experiments on both synthetic and real-life data sets.     

Fault detection and isolation of LPV systems using set-valued observers: An application to a fixed-wing aircraft

A novel fault detection and isolation (FDI) method using set-valued observers (SVOs), for uncertain linear parameter-varying (LPV) systems, is introduced. The proposed method relies on SVO-based model invalidation to discard models incompatible with measured data. When compared to the most common strategies in the literature, the suggested approach: (i) under suitable conditions, guarantees false alarms are avoided; (ii) unlike residual-based architectures, does not require the computation of thresholds to declare faults; (iii) has applicability to a wide class of plants. The performance of the proposed approach is assessed in simulation, using the full nonlinear model of a fixed-wing aircraft longitudinal dynamics.     

Symbolic propagation and sensitivity analysis in Gaussian Bayesian networks with application to damage assessment

In this paper we show how Bayesian network models can be used to perform a sensitivity analysis using symbolic, as opposed to numeric, computations. An example of damage assessment of concrete structures of buildings is used for illustrative purposes. Initially, normal or Gaussian Bayesian network models are described together with an algorithm for numerical propagation of uncertainty in an incremental form. Next, the algorithm is implemented symbolically, in Mathematica code, and applied to answer some queries related to the damage assessment of concrete structures of buildings. Finally, the conditional means and variances of the nodes given the evidence are shown to be rational functions of the parameters, thus, discovering its parametric structure, which can be efficiently used in sensitivity analysis.     

Fault detection and isolation for unstable linear systems

In the design of a residual generator for an unstable plant, the unstable modes are made unobservable from the residual vector. We point out that this cannot be achieved in practice due to the discrepancy between the estimated model and the “true” plant model. Hence, we deduce that it is not possible to design a residual generator for an unstable plant, unless it is stabilized by an adequate controller. As a by-product, we provide expressions which can be used to quantify the effect of modeling uncertainties on the performance of a residual generator. The factorization approach to control theory is used for the developments     

Fault detection and isolation based on fuzzy automata

Fuzzy automata are proposed for fault diagnosis. The output of the monitored system is partitioned into linear segments which in turn are assigned to pattern classes (templates) with the use of membership functions. A sequence of templates is generated and becomes input to fuzzy automata which have transitions that correspond to the templates of the properly functioning system. If the automata reach their final states, i.e. the input sequence is accepted by the automata with a membership degree that exceeds a certain threshold, then normal operation is deduced, otherwise, a failure is diagnosed. Fault diagnosis of a DC motor and detection of abnormalities in the ECG signal are used as case studies.     

Fault detection,isolation and identification for hybrid systems with unknown mode changes and fault patterns

This article presents a solution to the problem of multiple fault detection, isolation and identification for hybrid systems without information on mode change and fault patterns. Multiple faults of different patterns are considered in a complex hybrid system and these faults can happen either in a detectable mode or in a non-detectable mode. A method for multiple fault isolation is introduced for situation of lacking information on fault pattern and mode change. The nature of faults in a monitored system can be classified as abrupt faults and incipient faults. Under abrupt fault assumption, i.e. constant values for fault parameters, fault identification is inappropriate to handle cases related to incipient fault. Without information on fault nature, it is difficult to achieve fault estimation. Situation is further complicated when mode change is unknown after fault occurrence. In this work, fault pattern is represented by a binary vector to reduce computational complexity of fault identification. Mode change is parameterized as a discontinuous function. Based on these new representations, a multiple hybrid differential evolution algorithm is developed to identify fault pattern vector, abrupt fault parameter/incipient fault dynamic coefficient, and mode change indexes. Simulation and experiment results are reported to validate the proposed method.     

Automatic change detection and quantification of dermatological diseases with an application to psoriasis images

Change monitoring in skin lesion analysis has proven to be a useful adjunct in their assessment. This article presents a comparative study of the available change detection techniques applied to change visualization and quantification in bi-temporal psoriasis images. The chosen methods are evaluated on a time series of psoriasis images and results are compared with dermatologists’ scores.     

Fault detection and isolation using viability theory and interval observers

This paper proposes the use of interval observers and viability theory in fault detection and isolation (FDI). Viability theory develops mathematical and algorithmic methods for investigating the viability constraints characterisation of dynamic evolutions of complex systems under uncertainty. These methods can be used for checking the consistency between observed and predicted behaviour by using simple sets that approximate the exact set of possible behaviour (in the parameter or state space). In this paper, FDI is based on checking for an inconsistency between the measured and predicted behaviours using viability theory concepts and sets. Finally, an example is provided in order to show the usefulness of the proposed approach.     

Fault detection and isolation of faults in a multivariate process with Bayesian network

The main objective of this paper is to present a new method of detection and isolation with a Bayesian network. For that, a combination of two original works is made. The first one is the work of Li et al. [1] who proposed a causal decomposition of the T² statistic. The second one is a previous work on the detection of fault with Bayesian networks [2], notably on the modeling of multivariate control charts in a Bayesian network. Thus, in the context of multivariate processes, we propose an original network structure allowing to decide if a fault has appeared in the process. This structure permits the isolation of the variables implicated in the fault. A particular interest of the method is the fact that the detection and the isolation can be made with a unique tool: a Bayesian network.     

Fault detection and isolation in manufacturing systems with an identified discrete event model

In this article a generic method for fault detection and isolation (FDI) in manufacturing systems considered as discrete event systems (DES) is presented. The method uses an identified model of the closed-loop of plant and controller built on the basis of observed fault-free system behaviour. An identification algorithm known from literature is used to determine the fault detection model in form of a non-deterministic automaton. New results of how to parameterise this algorithm are reported. To assess the fault detection capability of an identified automaton, probabilistic measures are proposed. For fault isolation, the concept of residuals adapted for DES is used by defining appropriate set operations representing generic fault symptoms. The method is applied to a case study system.     

UIO design for singular delayed LPV systems with application to actuator fault detection and isolation

In this paper, the unknown input observer (UIO) design for singular delayed linear parameter varying (LPV) systems is considered regarding its application to actuator fault detection and isolation. The design procedure assumes that the LPV system is represented in the polytopic framework. Existence and convergence conditions for the UIO are established. The design procedure is formulated by means of linear matrix inequalities (LMIs). Actuator fault detection and isolation is based on using the UIO approach for designing a residual generator that is completely decoupled from unknown inputs and exclusively sensitive to faults. Fault isolation is addressed considering two different strategies: dedicated and generalised bank of observers’ schemes. The applicability of these two schemes for the fault isolation is discussed. An open flow canal system is considered as a case study to illustrate the performance and usefulness of the proposed fault detection and isolation method in different fault scenarios.     

Fault detection and isolation for linear parameter-varying systems with time-delays: a geometric approach

Fault detection and isolation(FDI) problems for linear parameter-varying(LPV) systems with state time-delays are studied in this paper. By defining the concept of unobservability subspace and designing its calculation algorithm, the geometric approach is introduced to the time-delay LPV systems. Utilizing Wirtinger-based integral inequality, we obtain a sufficient condition to solve the so-called H∞-based residual generation problem for the LPV systems. In this paper, we consider two cases: the ...     

Fault detection and diagnosis of networked control system

Consider the plant of Multiple-input Networked Control System (NCS) with long reduced delay. The effect of the disturbances to the NCS is discussed and the iterative method is used to compensate the delay when design a reduced-order state observer with a γ-stability margin (0 相似文献   

Fault detection and isolation in transient states using principal component analysis

This paper proposes the use of principal component analysis (PCA) for process monitoring and fault detection and isolation in processes with several operation modes and long transient states and start-ups. The principal aspects of the PCA approach and the necessary transformations for dealing with this type of processes are presented. In this paper a classical PCA model is used for each steady state of the process and a modification of a batch PCA approach is applied to the transient states of the continuous process. So, in this last case, the PCA model is performed over a three way matrix arranged with the values of the measured variables of several past transitions with a nominal behaviour. This approach presents some problems, such as the unfolding, alignment and imputation. The methods proposed to deal with these problems are explained in detail and compared in order to design a fault detection and isolation method. Two examples are considered to perform the tasks explained. In both cases good results are obtained.     

Fault isolation for diagnosis: Nuisance rejection and multiple hypotheses testing

Fault detection, fault isolation and fault diagnosis are addressed within a statistical framework. The corresponding inference problems are stated. Several statistical tools for solving these inference problems are described. Particular emphasis is put on dealing with nuisance parameters and deciding between multiple hypotheses. How to use these tools for solving problems is discussed. An example illustrates some of the proposed methods.     

Data‐based design of robust fault detection and isolation residuals via LASSO optimization and Bayesian filtering

In this paper, a data‐based approach for the design of structured residual subsets for the robust isolation of sensor faults is proposed. Linear regression models are employed to estimate faulty signals and to build a set of primary residuals. L₁‐regularized least squares estimation is used to identify model parameters and to enforce sparsity of the solutions by increasing the regularization weight. In this way, it is possible to generate a set of residuals generators with different fault sensitivity. Then, a residual selection procedure based on fault sensitivity maximization is proposed to extract a minimum size subset of structured residuals that allows for isolation of the faulty sensor. To overcome modelling uncertainty, a robust recursive Bayesian Filter has been employed to process, online, the distance of the residuals from nominal fault directions, providing a fault probability for each sensor. The proposed method has been validated by designing and testing a fault isolation scheme for six aircraft sensors using multi‐flight experimental data of a P92 Tecnam aircraft.     

Cheap diagnosis using structural modelling and fuzzy-logic-based detection

Practical fault diagnosis can be based on simple, yet efficient, analysis of redundant information about the state of a plant, and diagnostic algorithms can be made without detailed and expensive modelling efforts. This paper shows how it is possible, using structural analysis, to find redundancy relations for linear or non-linear dynamic behaviour, and combine this with fuzzy output observer design to provide an effective diagnostic approach. An adaptive neuro-fuzzy inference method is used. A fuzzy adaptive threshold is employed to cope with practical uncertainty. The methods are demonstrated using measurements on a ship propulsion system subject to simulated faults.     

基于概率密度函数的时滞依赖故障检测与诊断

针对包含时滞的随机分布系统,提出了一种基于概率密度函数（PDF）的时滞依赖故障检测与诊断方法。建立了基于PDF信息的故障检测残差,利用故障检测与诊断理论,设计了基于线性矩阵不等式的故障检测观测器和故障估计器,并且通过了稳定性分析,实现了对该系统的故障检测以及估计。通过数值仿真,证明了该方法的有效性。     

Relaxed fault detection and isolation: An application to a nonlinear case study

Given a number of possibly concurrent faults (and disturbances) that may affect a nonlinear dynamic system, it may not be possible to solve the standard fault detection and isolation (FDI) problem, i.e., to detect and isolate each single fault from all other, possibly concurrent faults and disturbances, due to the violation of the available necessary conditions of geometric nature. Motivated by a robotic application where this negative situation structurally occurs, we propose some relaxed formulations of the FDI problem and show how necessary and sufficient conditions for their solution can be derived from those available for standard FDI. The design of a hybrid residual generator follows directly from the fulfillment of the corresponding solvability conditions. In the considered nonlinear case study, a robotic system affected by possible actuator and/or force sensor faults, we detail the application of these results and present experimental tests for validation.