Fault diagnosis of a class of nonlinear uncertain systems with Lipschitz nonlinearities using adaptive estimation

This paper presents a fault detection and isolation (FDI) scheme for a class of Lipschitz nonlinear systems with nonlinear and unstructured modeling uncertainty. This significantly extends previous results by considering a more general class of system nonlinearities which are modeled as functions of the system input and partially measurable state variables. A new FDI method is developed using adaptive estimation techniques. The FDI architecture consists of a fault detection estimator and a bank of fault isolation estimators. The fault detectability and isolability conditions, characterizing the class of faults that are detectable and isolable by the proposed scheme, are rigorously established. The fault isolability condition is derived via the so-called fault mismatch functions, which are defined to characterize the mutual difference between pairs of possible faults. A simulation example of a single-link flexible joint robot is used to illustrate the effectiveness of the proposed scheme.     

Unified model-based fault diagnosis for three industrial application studies

This paper proposes a unified scheme for fault detection and isolation (FDI) that integrates model-based and multivariate statistical methods. For creating suitable models, subspace model identification is utilized together with state-observers to track the measured process operation. To describe and analyze the impact of fault conditions, the scheme utilizes input reconstruction and unknown input estimation to generate multivariate residual-based statistics. In contrast to existing work, the paper presents three industrial application studies involving sensor faults, as well as process and actuator faults which result from measured and unmeasured disturbances.     

Two fault detection and isolation schemes for robot manipulators using soft computing techniques

With growing technology, fault detection and isolation (FDI) have become one of the interesting and important research areas in modern control and signal processing. Accomplishment of specific missions like waste treatment in nuclear reactors or data collection in space and underwater missions make reliability more important for robotics and this demand forces researchers to adapt available FDI studies on nonlinear systems to robot manipulators, mobile robots and mobile manipulators.In this study, two model-based FDI schemes for robot manipulators using soft computing techniques, as an integrator of Neural Network (NN) and Fuzzy Logic (FL), are proposed. Both schemes use M-ANFIS for robot modelling. The first scheme isolates faults by passing residual signals through a neural network. The second scheme isolates faults by modelling faulty robot models for defined faults and combining these models as a generalized observers scheme (GOS) structure. Performances of these schemes are tested on a simulated two-link planar manipulator and simulation results and a comparison according to some important FDI specifications are presented.     

On-line fault detection in uncertain nonlinear systems using diagnostic observers: a survey

The paper discusses the principles of model-based fault detection and isolation (FDI) in nonlinear and time-varying uncertain dynamic systems. Such systems are typical for such complex plants as, for example, in the chemical process industries or in advanced transportation technology. For a model-based fault diagnosis in such situations, robust or even adaptive strategies are needed. In this paper the theory of robust linear observer-based residual generation for FDI is reviewed from a general point of view. The structural equivalence between the parity space approach and observer-based approach is shown in a new simple graphical way by showing that the observer-based FDI concept can easily be transformed into an equivalent extended parity space configuration, without claiming, however, equivalence of the underlying design techniques. The unknown input observer approach known as a most powerful and comprehensive framework for robust residual generation for FDI in uncertain linear systems is extended to classes of nonlinear and time-varying systems. For such plants an adaptive nonlinear unknown input observer scheme is proposed. Finally, appropriate residual evaluation techniques are outlined and suggestions are made to increase the robustness, for instance by using adaptive thresholds.     

Fault tolerant control using a fuzzy predictive approach

This paper proposes the application of fault-tolerant control (FTC) using fuzzy predictive control. The FTC approach is based on two steps, fault detection and isolation (FDI) and fault accommodation. The fault detection is performed by a model-based approach using fuzzy modeling and fault isolation uses a fuzzy decision making approach. The information obtained on the FDI step is used to select the model to be used in fault accommodation, in a model predictive control (MPC) scheme. The fault accommodation is performed with one fuzzy model for each identified fault. The FTC scheme is used to accommodate the faults of two systems a container gantry crane and three tank benchmark system. The fuzzy FTC scheme proposed in this paper was able to detect, isolate and accommodate correctly the considered faults of both systems.     

Fault detection and isolation in stochastic non-linear state-space models using particle filters

A novel model-based algorithm for fault detection and isolation (FDI) in stochastic non-linear systems is proposed. The algorithm monitors changes in the process behavior and identifies a corresponding fault using a bank of particle filters running in parallel. The particle filters are used to generate a sequence of hidden states which are then used in a log-likelihood ratio to detect and isolate the faults. The approach is demonstrated through an implementation on two highly nonlinear case studies—a multi-unit chemical reactor system and a polyethylene reactor system. The effectiveness and the robustness of the proposed algorithm are illustrated by comparing the results with FDI techniques that use EKF and UKF state estimators instead of particle filters.     

Cascade filter structure for sensor/actuator fault detection and isolation of satellite attitude control system

This paper presents a new scheme for fault detection and isolation in a satellite system. The purpose of this paper is to develop detection, isolation and identification algorithms based on a cascade filter for both total and partial faults in a satellite attitude control system (ACS). The cascade filter consists of a decentralized Kalman filter (DKF) and a bank of interacting multiple model (IMM) filters. The cascade filter is utilized for detection and diagnosis of anticipated sensor and actuator faults in a satellite ACS. Other fault detection and isolation (FDI) schemes are compared with the proposed FDI scheme. The FDI procedure using a cascade filter was developed in three stages. In the first stage, two local filters and a master filter detect sensor faults. In the second stage, the FDI scheme checks sensor residuals to isolate sensor faults, and 11 Extended Kalman filters with actuator fault models detect wherever actuator faults occur. In the third stage of the FDI scheme, four filters identify the fault type, which is either a total or partial fault. An important feature of the proposed FDI scheme is that it can decrease fault isolation time and accomplish not only fault detection and isolation but also fault type identification using a scalar penalty in the conditional density function.     

Fault detection and identification based on combining logic and model in a wall-climbing robot

A combined logic- and model-based approach to fault detection and identification （FDI） in a suction foot control system of a wall-climbing robot is presented in this paper. For the control system, some fault models are derived by kinematics analysis. Moreover, the logic relations of the system states are known in advance. First, a fault tree is used to analyze the system by evaluating the basic events （elementary causes）, which can lead to a root event （a particular fault）. Then, a multiple-model adaptive estimation algorithm is used to detect and identify the model-known faults. Finally, based on the system states of the robot and the results of the estimation, the model-unknown faults are also identified using logical reasoning. Experiments show that the proposed approach based on the combination of logical reasoning and model estimating is efficient in the FDI of the robot.     

Fault diagnosis of an industrial gas turbine prototype using a system identification approach

In this work, a model-based procedure exploiting analytical redundancy for the detection and isolation of faults on a gas turbine simulated process is presented. The main point of the paper consists of exploiting an identification scheme in connection with dynamic observer or filter design procedures for diagnostic purposes. Thus, black-box modelling and output estimation approaches to fault diagnosis are in particular advantageous in terms of solution complexity and performance achieved. Moreover, the suggested scheme is especially useful when robust solutions are considered for minimising the effects of modelling errors and noise, while maximising fault sensitivity. In order to experimentally verify the robustness of the solution obtained, the proposed FDI strategy has been applied to the simulation data of a single-shaft industrial gas turbine plant in the presence of measurement and modelling errors. Hence, extensive simulations of the test-bed process and Monte Carlo analysis are the tools for assessing experimentally the capabilities of the developed FDI scheme, when compared also with different data-driven diagnosis methods.     

基于键合图的混合动态系统故障诊断的方法

本文首先概述了控制系统故障诊断的概念,分析了国内外对于这一领域的研究水平和发展状况,并且分基于和不依赖于控制系统数学模型两大类介绍了一些主要的故障诊断方法。此外,本文针对当前应用较多的一种动态系统——混合系统,提出了一种基于键合图模型的故障诊断方法。介绍了混合键合图基本原理,通过混合键合图对混合动态系统建模,为进一步进行MBD故障检测和追踪直至故障隔离和鉴别提供基础模型。     

A novel approach to fault diagnosis for time-delay systems

In this paper, a novel fault detection and identification (FDI) scheme for time-delay systems is presented. Different from the existing FDI design methods, the proposed approach utilizes fault tracking approximator (FTA) and iterative learning algorithm to obtain estimates of the fault functions. Performance of the FTA is rigorously analyzed by investigating its stability and fault tracking sensitivity properties in the presence of slowly developing or abrupt faults for state delayed dynamic systems. A novel feature of the FTA is that it can simultaneously detect and identify the shape and magnitude of the faults. Additionally, an extension to a class of nonlinear time-delay systems is made by using nonlinear control theories. Finally, the applicability and effectiveness of the proposed FDI scheme is illustrated by a practical industrial process.     

Design and Performance Evaluation of Residual Generators for the FDI of an Aircraft

In this work, several procedures for the fault detection and isolation （FDI） on general aviation aircraft sensors are presented. In order to provide a comprehensive wide-pectrum treatment, both linear and nonlinear, model-based and data-driven methodologies are considered. The main contributions of the paper are related to the development of both FDI polynomial method （PM） and FDI scheme based on the nonLinear geometric approach （NLGA）. As to the PM, the obtained results highlight a good trade-off between solution complexity and resulting performances. Moreover, the proposed PM is especially useful when robust solutions are required for minimising the effects of modelling errors and noise, while maximising fault sensitivity. As to the NLGA, the proposed work is the first development and robust application of the NLGA to an aircraft model in flight conditions characterised by tight-oupled longitudinal and lateral dynamics. In order to verify the robustness of the residual generators related to the previous FDI techniques, the simulation results adopt a typical aircraft reference trajectory embedding several steady-tate flight conditions, such as straight flight phases and coordinated turns. Moreover, the simulations are performed in the presence of both measurement and modelling errors. Finally, extensive simulations are used for assessing the overall capabilities of the developed FDI schemes and a comparison with neural networks （NN） and unknown input Kalman filter （UIKF） diagnosis methods is performed.     

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.     

Fault-tolerant model predictive control of a direct methanol-fuel cell system with actuator faults

This paper investigates fault tolerant model predictive control (MPC) of a direct methanol fuel cell (DMFC) system with several faults in the methanol feeding pump. An active FTMPC strategy with a hierarchal structural design is developed. The focus here is on fault detection and isolation (FDI) and the implementation of fault-tolerant strategies within the control algorithm. To this end, a model-based FDI scheme with virtual sensors is first developed by means of the real-time diagnosis of fault occurrence during operation. Thereby, several faults in the methanol pump are characterized and the information integrated into the MPC algorithm in each fault case. Strategies are presented to reconfigure the active fault-tolerant MPC to keep the DMFC system stable in case of a feeding failure. Moreover, economic, stability and lifetime characteristics are also integrated into the active fault-tolerant MPC. The proposed FDI and FTMPC scheme is tested experimentally in a DMFC test rig with a 5-cell DMFC stack to demonstrate the effectiveness and robustness of the designed approach. Several fault scenarios with the FTMPC are shown. Particularly in the case of fuel cells, fault tolerance is necessary to meet the goals of long-lasting system stability and efficiency.     

LPV model-based robust diagnosis of flight actuator faults

A linear parameter-varying (LPV) model-based synthesis, tuning and assessment methodology is developed and applied for the design of a robust fault detection and diagnosis (FDD) system for several types of flight actuator faults such as jamming, runaway, oscillatory failure, or loss of efficiency. The robust fault detection is achieved by using a synthesis approach based on an accurate approximation of the nonlinear actuator–control surface dynamics via an LPV model and an optimal tuning of the free parameters of the FDD system using multi-objective optimization techniques. Real-time signal processing is employed for identification of different fault types. The assessment of the FDD system robustness has been performed using both standard Monte-Carlo methods as well as advanced worst-case search based optimization-driven robustness analysis. A supplementary industrial validation performed on the AIRBUS actuator test bench for the monitoring of jamming, confirmed the satisfactory performance of the FDD system in a true industrial setting.     

Isolation of parametric faults in continuous-time multivariable systems: a sampled data-based approach

A novel approach is proposed towards on-line and real-time detection and isolation of parametric faults in a multivariable linear continuous-time (CT) system. The problem of fault detection and isolation (FDI) is formulated in terms of a CT state space model. Since parameters in a CT model usually have simple relationships with physical parameters of the system, isolating parametric faults in the CT model can lead to the isolation of undesired changes in the physical parameters. Isolating parametric faults is very challenging, because even in a linear time-invariant system, the fault model can be time-varying and random. To obtain a constant fault model, many existing parametric FDI schemes have to make unrealistical assumptions. Our proposed FDI approach can generate an optimal primary residual vector (PRV), in which the fault model is constant without making any assumptions. To isolate faults, the PRV is transformed into a set of structured residual vectors (SRVs), where one SRV is made insensitive to a specified subset of faults, but most sensitive to other faults. The proposed approach is successfully applied to detection and isolation of undesired changes in the physical parameters of a simulated continuously stirred tank process.     

A pulsed plasma thruster fault detection and isolation strategy for formation flying of satellites

The main objective of this paper is to develop a dynamic neural network-based fault detection and isolation (FDI) scheme for pulsed plasma thrusters (PPTs) that are employed in the attitude control subsystem (ACS) of satellites tasked to perform formation flying (FF) missions. A hierarchical methodology is proposed that consists of three fault detection and isolation (FDI) approaches, namely (i) a “low-level” FDI scheme, (ii) a “high-level” FDI scheme, and (iii) an “integrated” FDI scheme. Based on the data from the electrical circuit of the PPTs, the proposed “low-level” FDI scheme can detect and isolate faults in the PPT actuators with a good level of accuracy, however the precision level is poor and below expectations with the misclassification rates as expressed by False Healthy and False Faulty parameters being too high. The proposed “high-level” FDI scheme utilizes data from the relative attitudes of the FF mission. This scheme has good detection capabilities, however its isolation capabilities are not adequate. Finally, the proposed “integrated” FDI scheme takes advantage of the strengths of each of the above two schemes while reducing their individual weaknesses. The results demonstrate a high level of accuracy (99.79%) and precision (99.94%) with a misclassification rates that are quite negligible (less than 1%). Furthermore, the proposed “integrated” FDI scheme provides additional and interesting information related to the effects of faults in the thrust production levels that would not have been available from simply using the low or the high level schemes alone.     

Delta算子描述的离散系统故障检测滤波器

研究基于Delta算子模型的离散时间系统故障检测问题.推导了Delta算子系统的故障可检测和可分离条件,给出了Delta算子模型的故障检测滤波器设计方法.研究表明,在采样周期很小时,Delta算子故障检测趋近于连续模型的相应结果,可统一处理连续系统和离散系统故障检测的相关问题.仿真实例验证了该方法的有效性.