Decentralized fault diagnosis approach without a global model for fault diagnosis of discrete event systems

Diagnosability property ensures that a predefined set of faults are diagnosable by a centralized diagnoser built using a global model of the system, while co-diagnosability guarantees that these faults are diagnosed in decentralized manner using a set of local diagnosers. A fault must be diagnosed by at least one local diagnoser by using its proper local observation of the system. The aim of using decentralized diagnosis approaches is to overcome the space complexity and weak robustness of centralized diagnosis approaches while at the same time preserving the diagnostic capability of a centralized diagnosis. However, co-diagnosability property is stronger than diagnosability property. If a system is co-diagnosable, then it is diagnosable, while a diagnosable system does not ensure that it is co-diagnosable. Therefore, the challenge of decentralized diagnosis approaches is to perform local diagnosis and to verify that it is equivalent to the centralized one without the need for a global model. In this paper, an approach is proposed to obtain co-diagnosable decentralized diagnosis structure of discrete event systems without the use of a global model. This approach is based on the synchronization of local diagnosis decisions in order to solve the ambiguity between local diagnosers. This synchronization allows obtaining local diagnosis equivalent to the global one without the use of a global model.     

Formal approach to fault diagnosis in distributed discrete event systems with OBDD

In this paper, we study the fault diagnosis problem for distributed discrete event systems. The model assumes that the system is composed of distributed components which are modeled in labeled Petri nets and interact with each other via sets of common resources (places). Further, a component’s own access to a common resource is an observable event. Based on the diagnoser approach proposed by Sampath et al., a distributed fault diagnosis algorithm with communication is presented. The distributed algorithm assumes that the local diagnosis process can exchange messages upon the occurrence of observable events. We prove the distributed diagnosis algorithm is correct in the sense that it recovers the same diagnostic information as the centralized diagnosis algorithm. Furthermore, we introduce the ordered binary decision diagrams (OBDD) in order to manage the state explosion problem in state estimation of the system.     

Active fault detection and isolation of discrete-time linear time-varying systems: a set-membership approach

Active fault detection and isolation (AFDI) is used for detection and isolation of faults that are hidden in the normal operation because of a low excitation signal or due to the regulatory actions of the controller. In this paper, a new AFDI method based on set-membership approaches is proposed. In set-membership approaches, instead of a point-wise estimation of the states, a set-valued estimation of them is computed. If this set becomes empty the given model of the system is not consistent with the measurements. Therefore, the model is falsified. When more than one model of the system remains un-falsified, the AFDI method is used to generate an auxiliary signal that is injected into the system for detection and isolation of faults that remain otherwise hidden or non-isolated using passive FDI (PFDI) methods. Having the set-valued estimation of the states for each model, the proposed AFDI method finds an optimal input signal that guarantees FDI in a finite time horizon. The input signal is updated at each iteration in a decreasing receding horizon manner based on the set-valued estimation of the current states and un-falsified models at the current sample time. The problem is solved by a number of linear and quadratic programming problems, which result in a computationally efficient algorithm. The method is tested on a numerical example as well as on the pitch actuator of a benchmark wind turbine.     

模型未知的离散事件系统故障诊断方法

针对离散事件系统模型难以建立的大型实际系统,无法对其进行有效故障诊断的问题,提出一种基于主动学习的故障诊断方法。首先,为获取到的系统事件日志添加正常/故障标签,并将日志集划分为训练集和测试集,提出一种基于抽象技术的迭代算法提取训练集中日志的故障特征样本。然后,通过故障特征样本构造初始故障识别器,并利用测试集中的日志检验识别器的准确性。仿真结果表明,该故障诊断算法使得模型未知下诊断精度更高。最后,实例说明系统模型未知下故障诊断算法的应用。与现有研究相比,提出的方法可以在系统模型未知下进行故障诊断且算法复杂度为多项式,诊断精度更高,应用范围更加广泛。     

The concept of residuals for fault localization in discrete event systems

In this paper an approach for fault localization in closed-loop Discrete Event Systems is proposed. The presented diagnosis method allows fault localization using a fault-free system model to describe the expected system behavior. Via a systematic comparison of the observed and the expected behavior, a fault can be detected and a set of fault candidates is determined. Inspired by residuals known from diagnosis in continuous systems, different set operations are introduced to generate the fault candidate set. After fault detection and a first fault localization, a procedure is given to render the fault localization more precisely by an analysis of the further observed system behavior. Special emphasis is given to the use of identified models for the fault-free system behavior. The approach is explained using a laboratory manufacturing facility.     

A robust fault detection approach for nonlinear systems

In this paper, we study the robust fault detection problem of nonlinear systems. Based on the Lyapunov method, a robust fault detection approach for a general class of nonlinear systems is proposed. A nonlinear observer is first provided, and a sufficient condition is given to make the observer locally stable. Then, a practical algorithm is presented to facilitate the realization of the proposed observer for robust fault detection. Finally, a numerical example is provided to show the effectiveness of the proposed approach.     

基于机器学习的蜂窝网络故障管理框架及方法综述

网络故障管理旨在检测、识别和纠正网络中发生的错误状况,为用户获得可靠稳定的网络服务提供保障,近年来,如何利用机器学习方法进行蜂窝网络故障管理引起了广泛关注。首先介绍了蜂窝网络故障管理的研究背景,明确网络故障管理的流程和功能;接着介绍现有蜂窝网络故障管理框架;随后对现有机器学习在蜂窝网络故障管理中的方法研究进行评述,从故障管理周期入手,分别对实现故障检测、故障诊断以及故障预测的机器学习方法展开介绍、总结和对比分析,为相关领域的研究提供参考。     

基于Petri网诊断器的离散事件系统模式故障的在线诊断

本文研究基于Petri网诊断器的离散事件系统模式故障的在线诊断问题.先构建一种用于模式故障在线诊断的自动机,给出了基于这种自动机的在线诊断方法.然后将自动机转换为Petri网并进一步构造了可用于S型模式故障或T型模式故障在线诊断的Petri网诊断器,提出了基于Petri网诊断器的模式故障在线诊断算法.通过分析算法的复杂性,得到了该算法具有多项式空间复杂性的结论.     

采样数据系统故障检测的H∞方法

提出一种对采样数据系统进行故障检测的H_∞方法. 首先构造离散时间故障检测系统, 分析残差产生器的动态特性. 然后从故障检测的角度引入算子, 定量考察连续时间未知输入信号和故障信号对离散时间残差信号的影响. 最后优化残差产生器的参数, 使得故障检测系统对未知输入具有较强的鲁棒性, 同时对故障具有较高的灵敏度.     

Active fault detection and isolation in linear time-invariant systems: A geometric approach

In this work, a novel approach on active fault detection and isolation for linear time-invariant systems, named forced diagnosability, is proposed. This approach computes a continuous state feedback law to render a fault diagnosable, even when it cannot be diagnosed by using passive diagnosis methods. To do that, this work derives novel geometric relationships between unobservability and $(A,B)$-invariant subspaces that, under certain conditions, guarantee the existence of such state feedback law. The objective of the state feedback law is to force all the faults, except the one required to be diagnosed, named $L_d$, to reside in an unobservability subspace. This effectively decouples the effect of $L_d$ on the system output, from the effect of the other faults, allowing the design of a residual generator to detect and isolate the desired fault. The proposed state feedback law continuously forces diagnosability, and it can be computed in polynomial time. This avoids testing faults only at fixed time intervals and solving complex optimization problems required in other active diagnosis approaches. A numerical example is presented to illustrate the efficiency of the proposed approach.     

Analysis of mobile agents in network fault management

Network domains have become more and more advanced in terms of their size, complexity and the level of heterogeneity. Comprehensive fault management is the most significant challenge in network management. Fault management can help increase the availability of the network by quickly identifying the faults and then, proactively, start the recovery process. Current centralized configured network management systems suffer from problems such as insufficient scalability, availability and flexibility as networks become more distributed. Mobile agents (MAs), with integral intelligence, can present a reasonable new technology that will help to achieve distributed management, several researchers have embraced these approaches. In this paper, we introduce a general analytical model for network management client/server (CS) and MA paradigms. We express how to build up an analytical framework, which can be used to quantitatively assess the performances of the MA and CS paradigms under different scenarios. We present some numerical and experimental results that demonstrate the applicability of our proposed framework, which will be based on a combination of MA and CS schemes called Adaptive Intelligent Mobile Agent.     

分布式随机离散事件系统的模式故障预测EI北大核心CSCD

本文以随机自动机为模型,研究分布式随机离散事件系统的模式故障预测问题.首先根据分布式随机离散事件系统的分布特性对模式故障协同可预测性概念进行形式化,并通过构造模式故障识别器来识别系统中发生的模式故障.然后,构造一个模式故障协同预测器,提出一种基于协同预测器的具有多项式复杂度的算法,得到关于分布式随机离散事件系统模式故障协同可预测性的充分必要条件,实现对分布式随机离散事件系统的模式故障预测.     

Probabilistic approaches to fault detection in networked discrete event systems

In this paper, we consider distributed systems that can be modeled as finite state machines with known behavior under fault-free conditions, and we study the detection of a general class of faults that manifest themselves as permanent changes in the next-state transition functionality of the system. This scenario could arise in a variety of situations encountered in communication networks, including faults occurred due to design or implementation errors during the execution of communication protocols. In our approach, fault diagnosis is performed by an external observer/diagnoser that functions as a finite state machine and which has access to the input sequence applied to the system but has only limited access to the system state or output. In particular, we assume that the observer/diagnoser is only able to obtain partial information regarding the state of the given system at intermittent time intervals that are determined by certain synchronizing conditions between the system and the observer/diagnoser. By adopting a probabilistic framework, we analyze ways to optimally choose these synchronizing conditions and develop adaptive strategies that achieve a low probability of aliasing, i.e., a low probability that the external observer/diagnoser incorrectly declares the system as fault-free. An application of these ideas in the context of protocol testing/classification is provided as an example.     

A spiking neural network-based approach to bearing fault diagnosis

Fault diagnosis, with the aim of accurately identifying the presence of various faults as early as possible so at to provide effective information for maintenance planning, has been extensively concerned in advanced manufacturing systems. With the increase of the amount of condition monitoring data, fault diagnosis methods have gradually shifted from the model-based paradigm to data-driven paradigm. Intelligent fault diagnosis approaches which can automatically mine useful information from a huge amount of raw data are becoming promising ways to identify faults of manufacturing systems in the context of massive data. In this paper, the Spiking Neural Network (SNN), as the third generation neural network, is tailored as an intelligent fault diagnosis tool for bearings in rotating machinery. Compared to the perceptron and the back propagation neural network (BPNN) which are respectively the first and second generations of neural networks. SNN, which introduces the concept of time into its operating model can more closely mimic natural neural networks and possesses high bionic characteristics. In the proposed SNN-based approach to bearing fault diagnosis, features extracted from raw vibration signals through the local mean decomposition (LMD) are encoded into spikes to train an SNN with the improved tempotron learning rule. The performance of the proposed method is examined by the CWRU and MFPT datasets, and the experimental results show that the method can achieve a promising accuracy in bearing fault diagnosis.     

基于马尔科夫预测的多传感器故障检测与诊断机制

随着无线传感器网络应用规模的不断扩大,各类应用中传感器故障检测与诊断成为系统正常作业、安全可靠性保障的关键技术。针对多传感器系统与节点工作过程定义3种状态,基于故障检测信息建立状态转移矩阵,通过马尔科夫模型预测传感器故障信息,为故障检测与诊断提供决策依据。另外,拓展数据包信息字段包括故障类型、节点定位等,故障处理后节点转移至正常状态后将故障处理和诊断特征等信息存储到网关或者汇聚节点,为改善故障检测精度和诊断效率以及系统资源利用率提供依据。实验结果表明：所提故障检测与诊断算法与传统算法相比,具有更高的故障检测精度,更短的故障诊断时延、能够准确判断故障类型等性能。     

Parity Relation Based Fault Estimation for Nonlinear Systems： An LMI Approach

This paper proposes a parity relation based fault estimation for a class of nonlinear systems which can be modelled by Takagi-Sugeno （TS） fuzzy models. The design of a parity relation based residual generator is formulated in terms of a family of linear matrix inequalities （LMIs）. A numerical example is provided to illustrate the effectiveness of the proposed design techniques.     

嵌入式设备故障检测和诊断系统设计

本文分析嵌入式设备的特点,并在此基础上提出充分利用嵌入式设备提供的接口资源,实现故障检测和诊断的方法,将设备故障定位到电路板级,为板级电路的故障检测和诊断奠定基础。此种方法在复杂工程环境下,无需拆卸嵌入式设备即可判断整机性能,达到故障检测和诊断的基本要求。在这一方法基础上,本文以某车载GPS导航系统为被测对象,详细介绍了故障检测和诊断系统设计。     

Sensor selection for fault diagnosis in uncertain systems

ABSTRACT

Finding the cheapest, or smallest, set of sensors such that a specified level of diagnosis performance is maintained is important to decrease cost while controlling performance. Algorithms have been developed to find sets of sensors that make faults detectable and isolable under ideal circumstances. However, due to model uncertainties and measurement noise, different sets of sensors result in different achievable diagnosability performance in practice. In this paper, the sensor selection problem is formulated to ensure that the set of sensors fulfils required performance specifications when model uncertainties and measurement noise are taken into consideration. However, the algorithms for finding the guaranteed global optimal solution are intractable without exhaustive search. To overcome this problem, a greedy stochastic search algorithm is proposed to solve the sensor selection problem. A case study demonstrates the effectiveness of the greedy stochastic search in finding sets close to the global optimum in short computational time.     

An active approach for detection of incipient faults

The methodology of auxiliary signal design for robust fault detection based on a multi-model (MM) formulation of normal and faulty systems is used to study the problem of incipient fault detection. The fault is modelled as a drift in a system parameter, and an auxiliary signal is to be designed to enhance the detection of variations in this parameter. It is shown that it is possible to consider the model of the system with a drifted parameter as a second model and use the MM framework for designing the auxiliary signal by considering the limiting case as the parameter variation goes to zero. The result can be applied very effectively to many early detection problems where small parameter variations should be detected. Two different approaches for computing the test signal are given and compared on several computational examples.