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.     

Automotive engine FDI by application of an automated model-based and data-driven design methodology

Fault detection and isolation (FDI) in automotive diesel engines is important in order to achieve and guarantee low exhaust emissions, high vehicle uptime, and efficient repair and maintenance. This paper illustrates how a set of general methods for model-based sequential residual generation and data-driven statistical residual evaluation can be combined into an automated design methodology. The automated design methodology is then utilized to create a complete FDI-system for an automotive diesel engine. The performance of the obtained FDI-system is evaluated using measurements from road drives and engine test-bed experiments. The overall performance of the FDI-system is good in relation to the required design effort. In particular no specific tuning of the FDI-system, or any adaption of the design methodology, was needed. It is illustrated how estimations of the statistical powers of the fault detection tests in the FDI-system can be used to further increase the performance, specifically in terms of fault isolability.     

Combining FDI and AI approaches within causal-model-based diagnosis.

This paper presents a model-based diagnostic method designed in the context of process supervision. It has been inspired by both artificial intelligence and control theory. AI contributes tools for qualitative modeling, including causal modeling, whose aim is to split a complex process into elementary submodels. Control theory, within the framework of fault detection and isolation (FDI), provides numerical models for generating and testing residuals, and for taking into account inaccuracies in the model, unknown disturbances and noise. Consistency-based reasoning provides a logical foundation for diagnostic reasoning and clarifies fundamental assumptions, such as single fault and exoneration. The diagnostic method presented in the paper benefits from the advantages of all these approaches. Causal modeling enables the method to focus on sufficient relations for fault isolation, which avoids combinatorial explosion. Moreover, it allows the model to be modified easily without changing any aspect of the diagnostic algorithm. The numerical submodels that are used to detect inconsistency benefit from the precise quantitative analysis of the FDI approach. The FDI models are studied in order to link this method with DX component-oriented reasoning. The recursive on-line use of this algorithm is explained and the concept of local exoneration is introduced.     

Enhancement of robustness in observer-based fault detection†

A prerequisite for the feasibility of the technique of observer-based fault detection and isolation (FDI) in dynamic systems is a satisfactory robustness with respect to modelling uncertainties. This paper surveys the most relevant methods to increase the robustness in both the stage of residual generation and residual evaluation. Among these methods are the generalized observer scheme, the robust parity space check, the unknown input and H _∞ observer scheme, the decorrelation filter, and the concept of adaptive threshold selection. It is pointed out that the unknown input observer concept, which provides perfect decoupling from the modelling errors or its optimal approximation with the aid of H _∞ techniques, constitutes a general framework of robust residual generation that generalizes and unifies numerous other approaches, among them the parity space and detection filter approach. It is further shown that this FDI method can even be applied to a certain class of nonlinear systems.     

局部近邻标准化偏最小二乘的多模态间歇过程故障检测

本文针对多模态间歇过程数据多中心和模态方差差异明显的问题,提出了一种基于局部近邻标准化偏最小二乘方法.首先,采用统计模量方法处理间歇过程数据,再利用局部近邻标准化方法将统计模量后的训练数据进行高斯化处理,建立偏最小二乘监控模型,确定控制限;然后,同样对统计模量后的测试数据进行局部近邻标准化处理,再计算测试数据的高斯偏最小二乘监控指标,进行过程监视及故障检测.最后,通过数值实例和青霉素发酵过程验证方法有效性.实验结果表明所提方法解决了故障样本近邻集跨模态问题,对多模态数据具有更好的故障检测能力.     

The detection mechanism for false data injection attack via the ellipsoidal set-membership approach

The false data injection (FDI) attack detection problem in cyber-physical systems (CPSs) is investigated in this paper. A novel attack detection algorithm is proposed based on the ellipsoidal set-membership approach. In comparison to the existing FDI attack detection methods, the developed attack detection approach in this paper neither requires predefined thresholds nor specific statistical characteristics of the attacks. In order to guarantee that the estimation ellipsoid contains normal states despite the unknown but bounded (UBB) process and measurement noises, the one-step ellipsoidal set-membership estimation method is put forward. In addition, a convex optimization algorithm is introduced to calculate the gain matrix of the observer recursively. Moreover, with the help of the state estimation ellipsoid, the residual ellipsoid can be obtained for attack detection. Whether a detector can detect the FDI attack depends on the relationship between the residual value and residual ellipsoidal set. Finally, the effectiveness of the proposed method is demonstrated by a numerical simulation example.     

Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/Set-membership approach

This paper addresses the problem of fault detection and isolation of wind turbines using a mixed Bayesian/Set-membership approach. Modeling errors are assumed to be unknown but bounded, following the set-membership approach. On the other hand, measurement noise is also assumed to be bounded, but following a statistical distribution inside the bounds. To avoid false alarms, the fault detection problem is formulated in a set-membership context. Regarding fault isolation, a new fault isolation scheme that is inspired on the Bayesian fault isolation framework is developed. Faults are isolated by matching the fault detection test results, enhanced by a complementary consistency index that measures the certainty of not being in a fault situation, with the structural information about the faults stored in the theoretical fault signature matrix. The main difference with respect to the classical Bayesian approach is that only models of fault-free behavior are used. Finally, the proposed FDI method is assessed against the wind turbine FDI benchmark proposed in the literature, where a set of realistic fault scenarios in wind turbines are proposed.     

Fault detection and isolation for networked control systems with finite frequency specifications

The fault detection and isolation (FDI) problem with finite frequency specifications is addressed in this paper under the framework of geometric approach for networked control systems subject to communication constraints and packet losses. The considered communication constraint is that only one of the transmission nodes is allowed to gain access to the shared channel. Also, those transmission nodes are scheduled to transmit data according to a specified stochastic protocol. Then by virtues of the common unobservable subspace and the finite frequency stochastic H _? index, a novel FDI scheme is developed in which a set of FDI filters that perform the FDI task with only partially available measurements are designed such that each residual is only sensitive to one fault in certain frequency domain and decoupled from the others. Further, less conservative conditions including some previous existing results have been presented to construct the FDI filters. Finally, an example is given to illustrate the effectiveness of the proposed method.Copyright © 2012 John Wiley & Sons, Ltd.     

Decentralized fault detection and isolation of Markovian jump interconnected systems with unknown interconnections

This paper investigates the decentralized fault detection and isolation (FDI) problem for Markovian jump interconnected systems with unknown interconnections. Different from the existing decentralized FDI approaches, the requirement for access to operation modes of all subsystems, which is unreasonable and hard to meet in realistic applications, is removed. By utilizing local measurements and neighboring mode information, a decentralized FDI filter is constructed to generate a residual for each subsystem of Markovian jump interconnected system. Then, a new design method is developed such that the resulting augmented system is stochastically stable and the generated residual is sensitive to local fault. In addition, the proposed method can achieve fault detection and isolation simultaneously. Finally, two examples are given to illustrate the effectiveness and merits of the new results. Copyright © 2017 John Wiley & Sons, Ltd.     

Actuator and sensor fault detection and isolation for nonlinear systems subject to uncertainty

This work addresses the problem of simultaneous actuator and sensor fault detection and isolation (FDI) for control affine nonlinear uncertain systems in the absence of measurement noise. The FDI is achieved by using a bank of filters, which utilize a subset of the measurements along with prescribed values of the control actuators to estimate states and compute expected process behavior. Residuals are next defined as the difference between the observed and expected behavior. Detectability conditions are developed, which, upon satisfaction, ensure that each residual remains sensitive to a subset of fault scenarios in the presence of uncertainty. To this end, first the ability of observers in providing bounded estimation error for a generalized class of nonlinear uncertain systems is rigorously established. These bounds allow determining thresholds that account for the impact of uncertainty on each residual. Finally, the ability of the proposed framework to achieve FDI by ensuring a unique residual breaching pattern for each fault scenario is established. The efficacy of the FDI framework subject to uncertainty and measurement noise is illustrated using a chemical reactor example.     

Limnimeter and rain gauge FDI in sewer networks using an interval parity equations based detection approach and an enhanced isolation scheme

In this paper, a methodology for limnimeter and rain-gauge fault detection and isolation (FDI) in sewer networks is presented. The proposed model based FDI approach uses interval parity equations for fault detection in order to enhance robustness against modelling errors and noise. They both are assumed unknown but bounded, following the so-called interval (or set-membership) approach. On the other hand, fault isolation relies on an algorithm that reasons using several fault signature matrices that store additional information to the typical binary one used in standard FDI approaches. More precisely, the considered fault signature matrices contain information about residual fault sign/sensitivity and time/order of activation. The paper also proposes an identification procedure to obtain the interval models used in fault detection that delivers the nominal model plus parameter uncertainty is proposed. To exemplify the proposed FDI methodology, a case study based on the Barcelona sewer network is used.     

Sensor fault detection and isolation for aircraft control systems by kinematic relations

This paper presents a new approach to Fault Detection and Isolation (FDI) for sensors of aircraft. In the most general case, fault detection of these sensors on modern aircraft is performed by a logic that selects one of, or combines, the three redundant measurements. Such a method is compliant with current airworthiness regulations. However, in the framework of the global aircraft optimization for future and upcoming aircraft, it could be required, e.g., to extend the availability of sensor measurements. Introducing a form of analytical redundancy of these measurements can increase the fault detection performance and result in a weight saving of the aircraft. This can be achieved by exploiting the knowledge of the kinematic relations between the measured variables. These relations are exactly known giving the advantage that no model-mismatches need to be accounted for. Furthermore these relations are valid over the whole flight envelope and general for any type of aircraft. Two example applications will be presented, showing the applicability of the method for the FDI of air data sensors and measurements of the inertial reference unit.     

基于局部熵双子空间的多模态过程故障检测

为了提高非高斯工业过程的检测性能, 提出局部熵双子空间(LEDS)的多模态过程故障检测方法. 运用局部 概率密度估计构建数据的局部熵矩阵, 消除数据的多模态特性. 用Kolmogorov-Smirnov (KS)检验局部熵数据中变 量的正态分布特性, 对高斯分布和非高斯分布的数据分别建立基于PCA的高斯子空间和ICA的非高斯子空间故障 检测模型. 利用Bayesian决策将检测结果转化成发生故障概率的形式, 将检测结果组合成最终的统计信息, 进行故 障检测. 将该方法应用于数值例子和田纳西–伊斯曼多模态过程, 仿真结果表明, 该方法在误报率较低的情况下, 故 障检测率最高, 优于PCA、局部熵PCA(LEPCA)和局部熵ICA(LEICA)方法.     

两级故障检测在SINS-Galileo-北斗组合中的应用研究

针对联邦滤波器子系统同时存在硬故障和软故障问题,提出一种适用于联邦滤波结构的两级故障检测方法。首先,构造联邦结构残差2χ检验法对系统硬故障进行检测,再用第k-m步未发生故障时的全局最优估计信息构造滑动残差检验函数,对未检测出的软故障进行时间积累,进而检测软故障,同时,联邦滤波信息分配系数根据软故障检测函数进行自适应调节。通过SINS-Galileo-北斗组合导航系统仿真对比分析了基于局部滤波残差2χ检验法和本文提出的故障检测方法,结果表明:该故障检测方法对系统硬故障和软故障具有较高的故障检测灵敏度,能够提高组合导航系统的可靠性。     

Fault Detection and Isolation of Distributed Time-Delay Systems

This paper investigates the development of fault detection and isolation (FDI) filters for distributed time-delay systems. The notion of a finite unobservability subspace is introduced for distributed time-delay systems and an algorithm for its construction is presented. A bank of residual generators is designed based on our developed geometric framework so that each residual is affected by one fault and is decoupled from the others while the $H_{infty} $ norm of the transfer function between the disturbance and the residual signals are kept at less than a prespecified value. Simulation results for a combustion system in a rocket motor chamber demonstrate the effectiveness and capabilities of our proposed FDI algorithm.      

基于随机丢包网络的网络存储系统故障检测方法

针对网络随机丢包的特性,研究网络存储系统在带有随机丢包的网络中故障检测失误率高的问题,提出了一种在随机丢包网络中的网络存储故障检测方法。该方法将残差发生、残差评估和误报率引入故障检测中。首先,在系统框架中实现残差发生;然后,充分利用随机丢包的随机特性获得残差评价;最后,通过切比雪夫不等式对所设计的阈值进行性能评价,即误报率的计算,给出了相应的诊断算法。仿真结果表明,该方法对故障具有较高的检测灵敏度,并且也证明了该方法的有效性。     

Subspace identification for FDI in systems with non-uniformly sampled multirate data

This paper proposes a novel subspace approach towards direct identification of a residual model for fault detection and isolation (FDI) in a system with non-uniformly sampled multirate (NUSM) data without any knowledge of the system. From the identified residual model, an optimal primary residual vector (PRV) is generated for fault detection. Furthermore, by transforming the PRV into a set of structured residual vectors, fault isolation is performed. The proposed algorithms have been applied to an experimental pilot plant with NUSM data for sensor FDI, where different types of faults are successfully detected and isolated, fully validating the practicality and utility of the developed theory.     

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.     

A geometric approach to fault detection and isolation for bilinearsystems

In this paper, a geometric approach to the synthesis of a residual generator for fault detection and isolation (FDI) in bilinear systems is considered. A necessary and sufficient condition to solve the so-called fundamental problem of residual generation is obtained. The proposed approach resorts to extensions of the notions of (C, A)-invariant and unobservability subspaces, and it yields a constructive design method