Optimal observation strategies for model-based fault detection in distributed systems

In model oriented diagnostics of real-world systems, the problems of structural identification and parameter estimation are of crucial importance. They require a properly designed schedule of measurements in such a way as to obtain possibly the most informative observational data. The aim of this work is to develop a novel approach to fault detection in distributed systems based on the maximization of the power of parametric hypothesis test, which verifies the nominal state of the considered system. The optimal locations of sensors are determined using the performance index operating on the Fisher Information Matrix. A general scheme is then proposed and tested on a computer example regarding an advection-diffusion problem.     

Immune model-based fault diagnosis

In this paper, a novel approach to immune model-based fault diagnosis methodology for nonlinear systems is presented. The diagnosis scheme consists of forward/inverse immune model identification, filtered residual generation, the fault alarm concentration (FAC), and the artificial immune regulation (AIR). A two-link manipulator simulation was employed to validate the effectiveness and robustness of the diagnosis approach. The simulation results show that it can detect and isolate actuator faults, sensor faults, and system component faults efficiently.     

Fuzzy model-based fault detection and diagnosis for a pilot heat exchanger

This article addresses the design and real-time implementation of a fuzzy model-based fault detection and diagnosis (FDD) system for a pilot co-current heat exchanger. The design method is based on a three-step procedure which involves the identification of data-driven fuzzy rule-based models, the design of a fuzzy residual generator and the evaluation of the residuals for fault diagnosis using statistical tests. The fuzzy FDD mechanism has been implemented and validated on the real co-current heat exchanger, and has been proven to be efficient in detecting and isolating process, sensor and actuator faults.     

Combination of model-based observer and support vector machines for fault detection of wind turbines

Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach is data-based and is therefore robust to process knowledge. It is based on structural risk minimization which enhances generalization even with small training data set and it allows for process nonlinearity by using flexible kernels. In this work, a radial basis function is used as the kernel. Different parts of the process are investigated including actuators and sensors faults. With duplicated sensors, sensor faults in blade pitch positions,generator and rotor speeds can be detected. Faults of type stuck measurements can be detected in 2 sampling periods. The detection time of offset/scaled measurements depends on the severity of the fault and on the process dynamics when the fault occurs. The converter torque actuator fault can be detected within 2 sampling periods. Faults in the actuators of the pitch systems represents a higher difficulty for fault detection which is due to the fact that such faults only affect the transitory state(which is very fast) but not the final stationary state. Therefore, two methods are considered and compared for fault detection and isolation of this fault: support vector machines and a Kalman-like observer. Advantages and disadvantages of each method are discussed. On one hand, support vector machines training of transitory states would require a big amount of data in different situations, but the fault detection and isolation results are robust to variations in the input/operating point. On the other hand, the observer is model-based, and therefore does not require training, and it allows identification of the fault level, which is interesting for fault reconfiguration. But the observability of the system is ensured under specific conditions, related to the dynamics of the inputs and outputs. The whole fault detection and isolation scheme is evaluated using a wind     

A quest for appropriate software fault models: Case studies on fault detection effectiveness of model-based test generation techniques

Model-based test generation (MBTG) is becoming an area of active research. These techniques differ in terms of (1) modeling notations used, and (2) the adequacy criteria used for test generation. This paper (1) reviews different classes of MBTG techniques at a conceptual level, and (2) reports results of three case studies comparing various techniques in terms of their fault detection effectiveness. Our results indicate that MBTG technique which employs mutation and explicitly generates state verification sequences has better fault detection effectiveness than those based on boundary values, and predicate coverage criteria for transitions. Instead of a default adequacy criteria, certain techniques allow the user to specify test objectives in addition to the model. Our experience indicates that the task of defining appropriate test objectives is not intuitive. Furthermore, notations provided to describe such test objectives may have inadequate expressive power. We posit the need for a suitable fault modeling notation which also treats domain invariants as first class entities.     

Principles and design of model-based robot controllers

Model-based control algorithms for industrial manipulators require the on-line evaluation of robot dynamics and are particularly sensitive to modelling errors. The development of a unifying framework for the analysis and design of model-based robot control strategies is the theme of this paper. In this framework, the practical problems associated with real-time implementation are highlighted and methods to improve the robustness of the closed-loop system are suggested.     

The development of an adaptive threshold for model-based fault detection of a nonlinear electro-hydraulic system

This paper presents a practical approach to combine model-based fault detection with an adaptive threshold. The suitability of the proposed technique is illustrated through its application to the condition monitoring of a nonlinear electro-hydraulic plant. The paper begins by outlining the difficulties associated with modelling the plant and the steps taken to identify the uncertain factors that influence the accuracy of the resulting model. A linearised model is applied in this study. The reason for this is because of the availability of many well-developed model-based approaches and model parameter estimation techniques for linear systems. The errors due to the linearisation and stochastic factors are studied both experimentally and theoretically and are compensated for by using an adaptive threshold. The combined linearised model-based approach and adaptive threshold is not only easy for on-line implementation but also takes into account the unknown influences such as model errors, measurement noise, temperature fluctuation and hence leads to a reliable fault detection scheme. The performance of the proposed fault detection scheme is demonstrated in detecting several different fault types associated with the control components, actuator and sensor.     

Practical design considerations for successful industrial application of model-based fault detection techniques to aircraft systems

This paper discusses some key factors which may arise for successful application of model-based Fault Detection (FD) techniques to aircraft systems. The paper reports on the results and the lessons learned during flight V&V (Validation & Verification) activities, implementation in the A380 Flight Control Computer (FCC) and A380 flight tests at Airbus (Toulouse, France). The paper does not focus on new theoretical materials, but rather on a number of practical design considerations to provide viable technological solutions and mechanization schemes. The selected case studies are taken from past and on-going research actions between Airbus and the University of Bordeaux (France). One of the presented solutions has received final certification on new generation Airbus A350 aircraft and is flying (first commercial flight: January 15, 2015).     

A model-based approach to robot fault diagnosis

This paper presents a model-based approach to online robotic fault diagnosis: First Priority Diagnostic Engine (FPDE). The first principle of FPDE is that a robot is assumed to work well as long as its key variables are within an acceptable range. FPDE consists of four modules: the bounds generator, interval filter, component-based fault reasoner (core of FPDE) and fault reaction. The bounds generator calculates bounds of robot parameters based on interval computation and manufacturing standards. The interval filter provides characteristic values in each predetermined interval to denote corresponding faults. The core of FPDE carries out a two-stage diagnostic process: first it detects whether a robot is faulty by checking the relevant parameters of its end-effector, if a fault is detected it then narrows down the fault at the component level. FPDE can identify single and multiple faults by the introduction of characteristic values. Fault reaction provides an interface to invoke emergency operation or tolerant control, even possibly system reconfiguration. The paper ends with a presentation of simulation results and discussion of a case study.     

Statistical background model-based target detection

This paper proposes a statistical background modeling framework to deal with the issue of target detection, where the global and local information is utilized to achieve more accurate detection of moving objects. Specifically, for the target detection problem under illumination change conditions, a novel self-adaptive Gaussian mixture model mixed with the global information is utilized to construct a statistical background model to detect moving objects; for the target detection problem under dynamic background conditions, the self-tuning spectral clustering method is first utilized to cluster background images, and then the kernel density estimation method mixed with the local information is utilized to construct a statistical background model to detect moving objects. Experimental results demonstrate that the proposed framework can improve the detection performance under illumination change conditions or dynamic background conditions.     

基于模型的异常检测的发展

在过去几年中,许多技术用于检测异常、误用、网络攻击和其他网络安全缺陷。文中讨论一种基于模型的技术方案。该技术并不是全新的,其已经成功用于校验通信协议的标准模式。然而在很多情况下,网络系统会忽略这些标准和提议。为了解决这个问题,可以在通信协议中结合使用基于模型技术和异常检测技术。发现类似网络攻击的信号或恶意行为时,就对这些异常加以研究,可以显著提高防御成功率。首先使用网络协议中的理论和方法原理作为状态机,然后在网络安全领域检测应用情况,最后提出一些实验研究中应遵循的核心方向,尽可能实现一些突破性成果。     

基于模型的软件接口故障注入测试平台技术

针对软件接口类型多、接口故障注入自动控制复杂的困难以及目前缺少有效的软件接口故障注入工具现状,对软件故障注入中的交联接口故障注入技术展开研究,提出了一种基于模型的软件接口故障注入测试平台技术。通过交联接口测试可视化建模和接口故障注入算子设计,自动生成XML 格式的测试用例,并且通过基于XSLT的脚本自动化生成与执行技术实现测试的控制与执行。故障注入测试主机与接口故障注入器网络连接,接口故障注入器将接口故障数据转换成以太网、串口或CAN网络数据注入到被测软件中,并返回被测件输出数据到测试主机实现测试数据收集与判断。基于模型的软件接口故障注入测试平台具有较强的通用性与易用性,提高了接口故障注入测试的效率,降低了接口测试的成本。     

Adaptive neural model-based fault tolerant control for multi-variable processes

An adaptive neural network model-based fault tolerant control approach for unknown non-linear multi-variable dynamic systems is proposed. A multi-layer Perceptron network is used as the process model and is adapted on-line using the extended Kalman filter to learn changes in process dynamics. In this way, the adaptive model will learn the post-fault dynamics caused by actuator or component faults. Then, the inversion of the neural model is used as a controller to maintain the system stability and control performance after fault occurrence. The convergence of the model inversion control is proved using Lyapunov method. The proposed method is applied to the simulation of a two-input two-output continuous-stirred tank reactor to demonstrate the effectiveness of the approach. Several actuator and component faults are simulated on the continuously stirred tank reactor process when the system is under the proposed fault tolerant control. The results have shown a fast recovery of tracking performance and the maintained stability.     

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.     

复杂系统的图模型多智能体协同故障诊断

为解决复杂、不确定系统的故障诊断实时推理问题,提出了基于图模型-多连片贝叶斯网络架构下多智能体协同推理的故障诊断方法。该方法将一个复杂贝叶斯网分割成若干有重叠的贝叶斯子网,使监控网络的单个智能体被抽象为一个拥有局部知识的贝叶斯网,利用成熟的贝叶斯网推理算法可完成智能体的自主推理。随后,通过重叠的子网接口进行多智能体间消息的传播,实现了多智能体协同故障诊断推理。实验结果表明了基于图模型多智能体的协同故障诊断方法的正确性和有效性。     

A model-based fault diagnosis scheme for wheeled mobile robots

In this paper, a fault diagnosis scheme for wheeled mobile robots is presented. In the fault detection module, a nonlinear observer is designed based on the mobile robot dynamic model. A fault is detected when at least one of the residuals exceeds its corresponding threshold. After the fault is detected, the fault isolation module is activated to isolate three types of fault: right wheel fault, left wheel fault, and other changing dynamic parameter faults. Three simulation examples are performed to show the effect of each fault to the tracking performance of mobile robot in a real situation. The simulation results demonstrate the effectiveness of our proposed approach for fault detection and isolation in wheeled mobile robots.     

基于危险模型的三级模块式入侵检测系统

利用危险理论和数据融合技术,提出一种基于危险模型的三级模块式入侵检测系统,并在第三级模块中提出了一种自适应决策模板算法,实现了检测模板的在线自动修正。系统的优点在于：对于利用现有知识难以给出检测结果的情况,系统将根据是否有危险信号做出判断,不但可减少误报还能改善对未知攻击的识别能力;利用自适应决策模板算法,系统的检测模板能够在线调整,不需要定期更新,使系统能适应行为经常改变的环境,也因此提高了检测未知攻击的能力。基于KDD-CUP-99数据库的实验验证了系统的有效性。     

Robustness of model-based fault diagnosis: Decisions with information-gap models of uncertainty

Fault diagnosis is analysed here as a decision between alternative hypotheses, based on uncertain evidence. W e consider severe lack of information, and perceive the uncertainty as an information gap between what is known, and what needs to be known for a perfect decision. This uncertainty is quantified with info-gap models of uncertainty, which require less information than probabilistic models. Previous work with convex set-models is extended to linear info-gap models which are not necessarily convex, as well as to more general info-gap models with arbitrary expansion properties. We define a decision algorithm based on info-gap models and prove three theorems, one establishing the connection with the earlier work on convex models, the other two showing that the algorithm is maximally robust for linear info-gap models as well as for general infogap models of uncertainty. An illustrative example is presented which shows how these results can be used for optimizing the design of a model-based fault diagnosis algorithm.