Process fault diagnosis with diagnostic rules based on structural decomposition

A method for formulating diagnostic rules from knowledge of system structures and component functions is presented. Based on this deep knowledge about a process, diagnosis can be performed hierarchically and structural decomposition is used in the presented technique to narrow the diagnostic focus. In the development of diagnostic rules, the structural and functional knowledge about a process is represented by several Boolean matrices, which describe the relations between subsystems, the relations between measured variables and the relations between malfunctions and measurements. Diagnosis is carried out by searching for the source subsystem where a fault occurred, and locating the fault in this source subsystem. The method is demonstrated in the development of diagnostic rules for a pilot scale mixing process and a simulated CSTR (continuously stirred tank reactor) system, where the diagnosis systems are developed using the ExTran expert system shell.     

Monitoring of complex systems of interacting dynamic systems

Increases in functionality, power and intelligence of modern engineered systems led to complex systems with a large number of interconnected dynamic subsystems. In such machines, faults in one subsystem can cascade and affect the behavior of numerous other subsystems. This complicates the traditional fault monitoring procedures because of the need to train models of the faults that the monitoring system needs to detect and recognize. Unavoidable design defects, quality variations and different usage patterns make it infeasible to foresee all possible faults, resulting in limited diagnostic coverage that can only deal with previously anticipated and modeled failures. This leads to missed detections and costly blind swapping of acceptable components because of one??s inability to accurately isolate the source of previously unseen anomalies. To circumvent these difficulties, a new paradigm for diagnostic systems is proposed and discussed in this paper. Its feasibility is demonstrated through application examples in automotive engine diagnostics.     

Interacting behavioral Petri nets analysis for distributed causal model-based diagnosis

This paper deals with the problem of spatially distributed causal model-based diagnosis on interacting behavioral petri nets (BPNs). The system to be diagnosed comprises different interacting subsystems (each modeled as a BPN) and the diagnostic system is defined as a multi-agent system where each agent is designed to diagnose a particular subsystem on the basis of its local model, the local received observation and the information exchanged with the neighboring agents. The interactions between subsystems are captured by tokens that may pass from one net model to another via bordered places. The diagnostic reasoning scheme is accomplished locally within each agent by exploiting classical analysis techniques of Petri nets like reachability graph and invariant analysis. Once local diagnoses are obtained, agents begin to communicate to ensure that such diagnoses are consistent and recover completely the results that would be obtained by a centralized agent having a global view about the whole system. The paper concludes with an empirical comparison, in terms of the running time, of two implementations of Petri net analysis techniques used as a distributed diagnostic reasoning schemes.     

Distributed sensor fault diagnosis in a class of interconnected nonlinear uncertain systems

In this paper, a distributed sensor fault detection and isolation (FDI) method is developed for a class of interconnected nonlinear uncertain systems. In the distributed FDI architecture, a FDI component is designed for each subsystem in the interconnected system. For each subsystem, its corresponding local FDI component is designed by utilizing local measurements and certain communicated information from neighboring FDI components associated with subsystems that are directly interconnected to the particular subsystem under consideration. Under certain assumptions, adaptive thresholds for distributed sensor fault detection and isolation in each subsystem are derived, ensuring robustness with respect to interactions among subsystems and system modeling uncertainty. Moreover, the fault detectability condition is rigorously investigated, characterizing the class of sensor faults in each subsystem that is detectable by the proposed distributed FDI method. Additionally, the stability and learning capability of the distributed adaptive fault isolation estimators is established. A simulation example of interconnected inverted pendulums mounted on carts is used to illustrate the effectiveness of the distributed FDI method.     

基于概率Petri网的故障诊断模型研究

为解决不确定条件下的故障诊断问题,在传统Petri网基础上,引入概率理论,提出了概率Petri网的概念;概率Petri网将事件发生的概率及其逻辑推理过程引入Petri网的设计及矩阵运算中,以反映事件转换过程中发生的可能性。针对概率Petri网特点,故障诊断模型的设计建立在根据简化样本集获取的诊断规则基础上,以避免复杂系统建模时出现的组合爆炸问题,诊断规则的获取可以有效推广故障诊断范围,使其不仅仅局限于样本集;同时,针对诊断规则的形式,定义并提出将诊断规则转换为标准基本规则序列,便于模型的程序化设计。通过旋转机械故障诊断的示例证明了这种方法的可行性与有效性。     

Distributed fault diagnosis for continuous-time nonlinear systems: The input–output case

In this paper, some new results on distributed fault diagnosis of continuous-time nonlinear systems with partial state measurements are proposed. By exploiting an overlapping decomposition framework, the dynamics of a nonlinear uncertain large-scale dynamical system is described as the interconnections of several subsystems. Each subsystem is monitored by a Local Fault Diagnoser: a set of local estimators, based on the nominal local dynamic model and on an adaptive approximation of the interconnection and of the fault function, allows to derive a local fault decision. A consensus-based protocol is used in order to improve the detectability and the isolability of faults affecting variables shared among different subsystems because of the overlapping decomposition. A sufficient condition ensuring the convergence of the estimation errors is derived. Finally, possibly non-conservative time-varying threshold functions guaranteeing no false-positive alarms and theoretical results dealing with detectability and isolability sufficient conditions are presented.     

基于滑模观测器和广义观测器的故障估计方法

针对受未知干扰影响的一类非线性系统,提出一种基于滑模观测器和广义观测器的执行器故障和传感器故障估计方法.首先通过线性变换将原系统解耦为两个降阶的子系统,其中一个子系统受执行器故障和干扰的影响,另一个含有传感器故障和干扰,进一步将后一个子系统转化为广义系统.对两类子系统分别设计滑模观测器和广义观测器,给出估计误差一致最终有界的条件,得到系统状态和未知干扰的估计值.然后,利用等效输出控制原理重构执行器故障,引入干扰补偿保证重构算法的鲁棒性,再根据广义观测器的结果获得传感器故障的估计值.最后,通过计算机仿真验证了本文方法的有效性.     

Fault diagnosis for high order systems based on model decomposition

Fault detection observer and fault estimation filter are the main tools for the model based fault diagnosis approach. The dimension of the observer gain normally depends on the system order and the system output dimension. The fault estimation filter traditionally has the same order as the monitored system. For high order systems, these methods have the potential problems such as parameter optimization and the real time implementation on-board for applications. In this paper, the system dynamical model is first decomposed into two subsystems. The first subsystem has a low order which is the same as the fault dimension. The other subsystem is not affected by the fault directly. With the new model structure, a fault detection approach is proposed where only the residual of the first subsystem is designed to be sensitive to the faults. The residual of the second subsystem is totally decoupled from the faults. Moreover, a lower order fault estimation filter (with the same dimension of the fault) design algorithm is investigated. In addition, the design of a static fault estimation matrix is presented for further improving the fault estimation precision. The effectiveness of the proposed method is demonstrated by a simulation example.     

Distributed fault diagnosis in a class of interconnected nonlinear uncertain systems

In this article, a distributed fault detection and isolation (FDI) method is developed for a class of interconnected nonlinear uncertain systems. In the distributed FDI architecture, a FDI component is designed for each subsystem in the interconnected system. For each subsystem, its corresponding local FDI component is designed by utilising local measurements and certain communicated information from neighbouring FDI components associated with subsystems that are directly interconnected to the particular subsystem under consideration. Under certain assumptions, adaptive thresholds for distributed FDI in each subsystem are derived, ensuring robustness with respect to interactions among subsystems and system modelling uncertainty. Moreover, the fault detectability and isolability conditions are rigorously investigated, characterising the class of faults in each subsystem that are detectable and isolable by the proposed distributed FDI method. Additionally, the stability and learning capability of the local adaptive fault isolation estimators designed for each subsystem is established. A simulation example of interconnected inverted pendulums mounted on carts is used to illustrate the effectiveness of the method.     

分布式多Agent专家系统设计与实现

为实现测试与诊断的相对分离，设计了基于多Agent的专家系统——SMAS。采用COM／CORBA分布式组件技术，实现通用建模子系统、知识获取子系统、诊断运行子系统等。故障诊断功能由分级分布的智能Agent并行或分级完成。Agent的组织和分布模式由系统提供的建模子系统完成建模，对不同的UUT系统具有通用性。分布式的结点Agent既可自主驱动运行，也可由其它Agent触发驱动，因而系统既可用于综合测试数据的分析诊断，也可用于与ATE的在线、交互式测试、诊断和分析。     

Distributed Fault Diagnosis With Overlapping Decompositions: An Adaptive Approximation Approach

This technical note deals with the problem of designing a distributed fault detection methodology for distributed (and possibly large-scale) nonlinear dynamical systems that are modelled as the interconnection of several subsystems. The subsystems are allowed to overlap, thus sharing some state components. For each subsystem, a local fault detector is designed, based on the measured local state of the subsystem as well as the transmitted variables of neighboring states that define the subsystem interconnections. The local detection decision is made on the basis of the knowledge of the local subsystem dynamic model and of an adaptive approximation of the interconnection with neighboring subsystems. The use of a specially-designed consensus-based estimator is proposed in order to improve the detectability of faults affecting variables shared among different subsystems. Simulation results provide an evidence of the effectiveness of the proposed distributed fault detection scheme.     

Decentralized fault tolerant control of a class of nonlinear interconnected systems

In this paper, a novel decentralized fault tolerant controller (DFTC) is proposed for interconnected nonlinear continuous-time systems by using local subsystem state vector alone in contrast with traditional distributed fault tolerant controllers or fault accommodation schemes where the measured or the estimated state vector of the overall system is needed. The proposed decentralized controller uses local state and input vectors and minimizes the fault effects on all the subsystems. The DFTC in each subsystem includes a traditional controller term and a neural network based online approximator term which is used to deal with the unknown parts of the system dynamics, such as fault and interconnection terms. The stability of the overall system with the proposed DFTC is investigated by using Lyapunov approach and the boundedness of all signals is guaranteed in the presence of a fault. Therefore, the proposed controller enables the system to continue its normal operation after the occurrence of a fault, as long as it does not cause failure or break down of a component. Although the decentralized fault tolerant controller is designed mainly for large-scale systems where continuous transmissions between subsystems is not possible, it can also be applied to small-scale systems where sensor measurements are available for use in all subsystems. Finally the proposed methods are verified and compared in simulation environment.     

A multi-agent architecture for diagnosing simultaneous faults along water canals

Water is intensively used in mankind activities, in particular in agriculture. Water is commonly conveyed for agriculture purposes through water canal networks which are large-scale spatially distributed systems crossing extensive regions. In the presence of leaks, unauthorized water withdrawals, water depth sensor faults or gate faults, the quality of service can be severely compromised. A system able to diagnose which type of fault is present at a given time is of vital importance to access the current state of the water canal and proceed to restore its nominal condition. This paper proposes a multi-agent architecture to simultaneously detect, isolate and estimate lateral outflows (e.g., leaks or water withdrawals) and hardware faults (e.g., a gate obstruction or a downstream water depth sensor fault) in water canal networks. First, the main canal network is broken down into several subsystems composed of a single canal pool with the corresponding gate. Then, an agent is assigned to each subsystem aiming at its fault diagnosis. The approach is based on the generation and evaluation of residuals obtained from the comparison of model-based output signals with real data. Application to an experimental water canal bears out the proposed architecture as a valuable tool for monitoring and supervising general water canals.     

Variance results for identification of cascade systems

The objective of this contribution is to analyze statistical properties of estimated models of cascade systems. Models of such systems are important in for example cascade control applications. The aim is to present and analyze some fundamental limitations in the quality of an identified model of a cascade system under the condition that the true subsystems have certain common dynamics. The model quality is analyzed by studying the asymptotic (large data) covariance matrix of the Prediction Error Method parameter estimate. The analysis will focus on cascade systems with three subsystems. The main result is that if the true transfer functions of the first and second subsystem are identical, the output signal information from the second and third subsystems will not affect the asymptotic variance of the estimated model of the first subsystem. This result implies that for a cascade system with two subsystems, where the dynamics of the first subsystem is a factor of the dynamics of the second one, the output signal information from the second subsystem will not improve the asymptotic quality of the estimate of the first subsystem. The results are illustrated by some simple FIR examples.     

An Efficient Algorithm for Finding Minimal Overconstrained Subsystems for Model-Based Diagnosis

In model-based diagnosis, diagnostic system construction is based on a model of the technical system to be diagnosed. To handle large differential algebraic imemodels and to achieve fault isolation, a common strategy is to pick out small overconstrained parts of the model and to test these separately against measured signals. In this paper, a new algorithm for computing all minimal overconstrained subsystems in a model is proposed. For complexity comparison, previous algorithms are recalled. It is shown that the time complexity under certain conditions is much better for the new algorithm. This is illustrated using a truck engine model.     

A geometric approach to nonlinear fault detection and isolation

We present a differential geometric approach to the problem of fault detection and isolation for nonlinear systems. A necessary condition for the problem to be solvable is derived in terms of an unobservability distribution, which is computable by means of suitable algorithms. The existence and regularity of such a distribution implies the existence of changes of coordinates in the state and in the output space which induce an “observable” quotient subsystem unaffected by all fault signals but one. For this subsystem, a fault detection filter is designed     

An actuator fault reconstruction scheme for linear systems

This paper presents an actuator fault reconstruction scheme that can be classified into an algebraic decomposition approach in which the original system is decomposed into two subsystems: a fault-free subsystem and a fault-dependent one. From the result of such algebraic decomposition, the fault estimate is obtained as a set of combinational functions of the system input, the measurement output, the estimate of certain linear functionals involving the state of the fault-free subsystem and the first-order derivative of the measurement output. Here a functional observer and robust exact differentiators are adopted to provide the estimate of the linear functionals of the state of the fault-free subsystem and the derivative of the measurement output, respectively. The proposed method is verified through two numerical examples of seventh-order aircraft and fourth-order crane models.     

A Unifying Approach to the Design of a Secure Database Operating System

Database management systems (DBMS's) today are usually built as subsystems on top of an operating system (OS). This design approach can lead to problems of unreliability and inefficient performance as well as forcing a duplication of functions between the DBMS and OS. A new design approach is proposed which eliminates much of this duplication by integrating the duplicated functions into independent subsystems used by both the DBMS and OS. Specifically, an I/O and file support subsystem and a security subsystem are defined. Both subsystems make use of a logical information model which models the stored information in secondary storage. The new database operating system organization and the logical information model are presented in detail. Design of the security subsystem is based on the access control model, and is extended with Boolean predicates to produce an access control model capable of enforcing content-dependent security policies. The access matrix is implemented using a combination of access lists and capabilities. Authorization models and multiple user processes are discussed in relation to the new system organization. The outline of a formal specification and proof of correctness of the security subsystem is also discussed.     

Performance Modeling and Evaluation of a Two-Dimensional Disk Array System

This paper presents a performance model of a two-dimensional disk array (TIDA) system, which is composed of several major subsystems including disk cache, intelligent disk array controller, SCSI-like I/O bus, and two-dimensional array of disk devices. Accessing conflict in these subsystems and fork/join synchronization of physical disk requests are considered in the model. The representation for the complex behavior, including the interactions among subsystems, of a whole disk array system distinguishes the model from others that model only individual subsystems. To assist evaluating the architectural alternatives of TIDA, we employ a subsystem access time modeling methodology, in which we model for each subsystem the mean subsystem access time per request (SATPR). Fed with a given set of representative workload parameters, the performance model is used to conduct performance evaluation and the SATPRs of the subsystems are utilized to identify the bottleneck subsystem for performance improvement. The results show that (1) the values of some key design parameters, such as data block size and I/O bus bandwidth that yield the best system throughput are dependent not only on the subsystem performance but also on the interaction among subsystems; (2) an I/O bus bandwidth of 5 Mbytes/s per disk device is large enough for data transfers from/to disk devices equipped with a cache of 1 Mbytes; and (3) the activity of fork/join synchronization of physical disk requests may cause performance degradation, which can be improved by using large I/O bus bandwidth and/or placing a cache in each disk device.     

事件触发双模分布式预测控制

本文针对有界扰动作用下的线性离散大系统,提出了事件触发双模分布式预测控制设计方法.利用输入状态稳定性(input-to-state stability,ISS)理论建立了仅与子系统自身信息相关的事件触发条件.只有子系统满足相应的事件触发条件,才进行状态信息的传输和分布式预测控制优化问题的求解,并与邻域子系统交互最优解作用下的关联信息.当子系统进入不变集时,采用状态反馈控制律进行镇定,并与进入不变集的邻域子系统不再交互信息.分析了算法的递推可行性和系统的闭环稳定性,给出了扰动的上界.最后,通过车辆控制系统对算法进行仿真验证,结果表明,本文提出的方法能够有效降低优化问题的求解次数和关联信息的交互次数,节约计算资源和通信资源.