Synthesis and analysis of temporal fault trees with PANDORA: The time of Priority AND gates

Classical Fault Tree Analysis (FTA) can determine the effects of combinations of failure events on a system but cannot capture the significance of the temporal order of events, which may be critical. In this paper, we propose an extension based on formal definition and use of Priority AND gates that enables representation of event sequences and analysis of temporal relationships in FTA. In addition, we show how this type of temporal analysis can be used in conjunction with a recently proposed method for automated fault tree synthesis to allow accurate failure analyses of system models to be carried out efficiently. The approach is demonstrated on a generic system with a shared backup component. The paper tentatively concludes that this type of temporal FTA can provide a more precise and ultimately more correct insight into the failure behaviour of a system.     

Bond graph model-based system mode identification and mode-dependent fault thresholds for hybrid systems

Hybrid system models exploit the modelling abstraction that fast state transitions take place instantaneously so that they encompass discrete events and the continuous time behaviour for the while of a system mode. If a system is in a certain mode, e.g. two rigid bodies stick together, then residuals of analytical redundancy relations (ARRs) within certain small bounds indicate that the system is healthy. An unobserved mode change, however, invalidates the current model for the dynamic behaviour. As a result, ARR residuals may exceed current thresholds indicating faults in system components that have not happened. The paper shows that ARR residuals derived from a bond graph cannot only serve as fault indicators but may also be used for bond graph model-based system mode identification. ARR residuals are numerically computed in an off-line simulation by coupling a bond graph of the faulty system to a non-faulty system bond graph through residual sinks. In real-time simulation, the faulty system model is to be replaced by measurements from the real system. As parameter values are uncertain, it is important to determine adaptive ARR thresholds that, given uncertain parameters, allow to decide whether the dynamic behaviour in a current system mode is the one of the healthy system so that false alarms or overlooking of true faults can be avoided. The paper shows how incremental bond graphs can be used to determine adaptive mode-dependent ARR thresholds for switched linear time-invariant systems with uncertain parameters in order to support robust fault detection. Bond graph-based hybrid system mode identification as well as the determination of adaptive fault thresholds is illustrated by application to a power electronic system easy to survey. Some simulation results have been analytically validated.     

基于故障树和混沌粒子群算法的锻压机床故障诊断方法

锻压机床由于生产效率高和材料利用率高的特点,被广泛应用于各领域.然而,锻压机床发生故障时,其故障种类繁多、故障数据量大,所以对锻压机床故障源的快速、准确诊断较困难.针对该问题,文章提出一种将故障树分析法和混沌粒子群算法相融合的方法,对锻压机床的故障源进行故障诊断.该方法是先通过故障树分析法对锻压机床的故障进行分析从而得到故障模式及其故障概率,然后由得到的故障模式和已知的故障维修经验分析归纳出故障模式的学习样本,再根据得到的故障概率运用混沌粒子群算法的遍历性快速、准确地诊断出锻压机床发生故障的精确位置.文章提出的方法以锻压机床的伺服系统为例进行了故障诊断实验,将该实验结果与遗传算法、粒子群算法进行对比.实验结果表明,文章的算法在锻压机床伺服系统的故障诊断中准确度更高、速度更快.     

Reliability analysis of an Attitude Determination and Control System (ADCS) through the RAMSAS method

Reliability analysis of modern large-scale systems is a challenging task which could benefit from the jointly exploitation of recent model-based approaches and simulation techniques to flexibly evaluate the system reliability performances and compare different design choices. In this context, RAMSAS, a model-based method which supports the reliability analysis of systems through simulation by combining the benefits of popular OMG modeling languages with wide adopted simulation and analysis environments, has been recently proposed. This paper shows the effectiveness of RAMSAS through a real case study concerning the reliability analysis of an Attitude Determination and Control System (ADCS) of a satellite.     

Risk assesment of a new high-speed railway

A quantitative risk assessment has been carried out for theconceptual stage of the proposed Channel Tunnel Rail Link (CTRL).The assessment is one of the first for a railway system. Itincludes an integrated computer model. The whole risk modelfor the CTRL from base event data, through fault and event trees,to individual, collective, and societal risks is processed usingLotus 1-2-3 spreadsheets. Variations to the design and operationalassumptions and to the input data can be introduced into themodel, and the effect on risk calculated rapidly. This versatilityis particularly useful because of the early stage of the project.It can be used to assist designers in consideration of possibledesign changes.     

An algorithmic approach for maintenance management based on advanced state space systems and harmonic regressions

Point mechanisms are special track elements which failures results in delays and increased operating costs. In some cases such failures cause fatalities. A new robust algorithm for fault detection of point mechanisms is developed. It detects faults by comparing what can be considered the ‘normal’ or ‘expected’ shape of some signal with respect to the actual shape observed as new data become available. The expected shape is computed as a forecast of a combination of models. The proposed system deals with complicated features of the data in the case study, the main ones being the irregular sampling interval of the data and the time varying nature of the periodic behaviour. The system models are set up in a continuous-time framework and the system has been tested on a large dataset taken from a point mechanism operating on a commercial line.     

Dissipative analysis for discrete‐time systems via fault‐tolerant control against actuator failures

This article investigates the problem of robust dissipative fault‐tolerant control for discrete‐time systems with actuator failures. Based on the Lyapunov technique and linear matrix inequality (LMI) approach, a set of delay‐dependent sufficient conditions is developed for achieving the required result. A design scheme for the state‐feedback reliable dissipative controller is established in terms LMIs which can guarantee the asymptotic stability and dissipativity of the resulting closed‐loop system with actuator failures. In addition, the proposed controller not only stabilize the fault‐free system but also to guarantee an acceptable performance of the faulty system. Also as special cases, robust H_∞ control, passivity control, and mixed H_∞ and passivity control with the prescribed performances under given constraints can be obtained for the considered systems. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed fault‐tolerant control technique. © 2016 Wiley Periodicals, Inc. Complexity 21: 579–592, 2016     

Diagnosis for uncertain,dynamic and hybrid domains using Bayesian networks and arithmetic circuits

System failures, for example in electrical power systems, can have catastrophic impact on human life and high-cost missions. Due to an electrical fire in Swissair flight 111 on September 2, 1998, all 229 passengers and crew on board sadly lost their lives. A battery failure most likely took place on the Mars Global Surveyor, which unfortunately last communicated with Earth and thus ended its mission on November 2, 2006. Fault diagnosis techniques that seek to hinder similar accidents in the future are being developed in this article. We present comprehensive fault diagnosis methods for dynamic and hybrid domains with uncertainty, and validate them using electrical power system data. Our approach relies on the use of Bayesian networks, which model the electrical power system, compiled to arithmetic circuits. We handle in an integrated way varying fault dynamics (both persistent and intermittent faults), fault progression (both abrupt and drift faults), and fault behavior cardinality (both discrete and continuous behaviors). Our work has resulted in a software system for fault diagnosis, ProDiagnose, that has been the top performer in three of the four international diagnostics competitions in which it participated. In this paper we comprehensively present our methods as well as novel and extensive experimental results on data from a NASA electrical power system.     

利用组合群验数学模型分析汽车故障

利用组合群验的理论和方法建立数学模型,依据汽车可靠度对汽车系统定量分析,为确定故障部位,汽车维修,故障分析提供方法.     

系统多故障状态模糊决策的研究

复杂系统发生故障时，往往出现两个或两个以上故障现象，并且故障原因与故障现象之间常存在交叉关系，传统的故障树分析法无法对系统多故障进行诊断。本将模糊决策的基本原理和故障树定量分析中常用的重要度分析法相结合，提出了一种对多故障状态的分析方法，并对液化气储配站烃泵灌装系统多故障状态进行了分析，取得了良好的应用结果。     

复杂可修复系统故障数据分析处理中的两个问题

马良河等．复杂可修复系统故障数据分析处理中的两个问题．数理统计与管理，１９９８，１７（３），３７～４０．本文给出了利用部件的故障数据对复杂可修复系统的可靠性状态进行分析和估计时应首先解决的两个问题：应选取合适的故障数据及应分析系统部件的固有属性对故障数据的影响。进而给出了这两个问题的解决方法及对系统性能的分析方法。最后针对飞机发动机的一组故障数据给出了计算实例。所用工具主要是假设检验和方差分析的理论     

基于半马尔科夫过程的逻辑门可靠性分析

动态故障树分析方法是在静态故障树的基础上拓展而来的自上而下的图形化演绎技术,可以很好地对具有复杂失效行为和交互作用的系统进行建模,进而分析系统的可靠性。本文从动态故障树逻辑门的可靠性建模与分析入手,结合半马尔科夫过程原理,将动态逻辑门转化为半马尔科夫链。其次给出在半马尔科夫链中动态逻辑门输出事件的发生概率和系统可靠性的计算公式。提出各种逻辑门到半马尔科夫链的通用转化模型,通过更改通用模型中的相关参数,将逻辑门转化为半马尔科夫链。最后,基于半马尔科夫过程求解动态逻辑门输出事件的发生概率,以动态优先与门、顺序相关门和备件门为例,并给出系统可靠性的计算公式。     

On the shape of the fringe of various types of random trees

We analyze a fringe tree parameter w in a variety of settings, utilizing a variety of methods from the analysis of algorithms and data structures. Given a tree t and one of its leaves a, the w(t, a) parameter denotes the number of internal nodes in the subtree rooted at a's father. The closely related w?(t, a) parameter denotes the number of leaves, excluding a, in the subtree rooted at a's father. We define the cumulative w parameter as W(t) = Σ_aw(t, a), i.e. as the sum of w(t, a) over all leaves a of t. The w parameter not only plays an important rôle in the analysis of the Lempel–Ziv '77 data compression algorithm, but it is captivating from a combinatorial viewpoint too. In this report, we determine the asymptotic behavior of the w and W parameters on a variety of types of trees. In particular, we analyze simply generated trees, recursive trees, binary search trees, digital search trees, tries and Patricia tries. The final section of this report briefly summarizes and improves the previously known results about the w? parameter's behavior on tries and suffix trees, originally published in one author's thesis (see Analysis of the multiplicity matching parameter in suffix trees. Ph.D. Thesis, Purdue University, West Lafayette, IN, U.S.A., May 2005; Discrete Math. Theoret. Comput. Sci. 2005; AD :307–322; IEEE Trans. Inform. Theory 2007; 53 :1799–1813). This survey of new results about the w parameter is very instructive since a variety of different combinatorial methods are used in tandem to carry out the analysis. Copyright © 2008 John Wiley & Sons, Ltd.     

具有Markov跳变参数的广义系统鲁棒故障检测

针对一类广义不确定时滞Markov跳变系统,应用鲁棒控制理论分析重构的增广系统,获取了系统的故障检测滤波器.利用构造的随机Lyapunov-Krasovskii函数和线性矩阵不等式,证明并给出了故障检测滤波器有解的充分条件和优化设计方法,同时使得残差体现其对扰动的抑制作用和对故障的灵敏性.所设计的滤波器使系统具有随机稳定性,抑制干扰能力强,满足所给的范数指标.仿真算例对结果进行了验证,在故障信息发生时,故障检测滤波器可以很灵敏地检测出故障,并能有效地抑制未知扰动对残差的影响在给定的范围内.     

USING NETWORK ANALYSIS TO CHARACTERIZE FOREST STRUCTURE

Abstract Network analysis quantifies different structural properties of systems of interrelated parts using a single analytical framework. Many ecological phenomena have network‐like properties, such as the trophic relationships of food webs, geographic structure of metapopulations, and species interactions in communities. Therefore, our ability to understand and manage such systems may benefit from the use of network‐analysis techniques. But network analysis has not been applied extensively to ecological problems, and its suitability for ecological studies is uncertain. Here, we investigate the ability of network analysis to detect spatial patterns of species association in a tropical forest. We use three common graph‐theoretic measures of network structure to quantify the effect of understory tree size on the spatial association of understory species with trees in the canopy: the node degree distribution (NDD), characteristic path length (CPL), and clustering coefficient (CC). We compute the NDD, CPL, and CC for each of seven size classes of understory trees. For significance testing, we compare the observed values to frequency distributions of each statistic computed from randomized data. We find that the ability of network analysis to distinguish observed patterns from those representing randomized data strongly depends on which aspects of structure are investigated. Analysis of NDD finds no significant difference between random and observed networks. However, analysis of CPL and CC detected nonrandom patterns in three and one of the seven size classes, respectively. Network analysis is a very flexible approach that holds promise for ecological studies, but more research is needed to better understand its advantages and limitations.     

Publisher's Announcement

The main purpose of this paper is to emphasize the particularities of models needed for model-based fault detection and isolation (FDI) in contrast to the models used for control. Of special interest is the question of complexity of the model, which is of great importance for the practical implementation. This, of course, depends basically on the given situation such as the kind of plant, the measurements, the kind and number of faults to be detected and the demands for fault isolation and robustness. However, the paper shows that diagnostic models, in contrast to the wide-spread opinion that those have always to be more complex than the functional models for control, may be even less complex, because they are restricted to only those parts of the system in which the faults occur. The issue of model complexity is discussed in terms of different model-based FDI approaches analytical, knowledge-based and data-based. The ideas are illustrated in a case study, where several types of model-based FDI techniques are compared with the same plant, the amira three tank system.     

Fault diagnosability utilizing quasi-static and structural modelling

This paper presents a diagnosis model-based method to analyse fault discriminability and assess diagnosability. The technique is based on the state space representation of quasi-static models. Fault diagnosability characterises the faults that can be discriminated using the available sensors in a system. The method can be used to select the minimum set of sensors that guarantee discriminability of an anticipated set of faults. The approach is applied on a two-tanks system benchmark and is compared to a diagnosability analysis method based on structural analysis.     

Mathematical modeling of a steam generator for sensor fault detection

A dynamic model for a nuclear power plant steam generator (vertical, preheated, U-tube recirculation-type) is formulated as a sixth-order nonlinear system. The model integrates nodal mass and energy balances for the primary water, the U-tube metal and the secondary water and steam. The downcomer flow is determined by a static balance of momentum. The mathematical system is solved using transient input data from the Philippsburg 2 (FRG) nuclear power plant. The results of the calculation are compared with actual measured values. The proposed model provides a low-cost tool for the automatic control and simulation of the steam generating process. The “parity-space” algorithm is used to demonstrate the applicability of the mathematical model for sensor fault detection and identification purposes. This technique provides a powerful means of generating temporal analytic redundancy between sensor signals. It demonstrates good detection rates of sensor errors using relatively few steps of scanning time and allows the reconfiguration of faulty signals.