Fault diagnostics of dynamic system operation using a fault tree based method

For conventional systems, their availability can be considerably improved by reducing the time taken to restore the system to the working state when faults occur. Fault identification can be a significant proportion of the time taken in the repair process. Having diagnosed the problem the restoration of the system back to its fully functioning condition can then take place. This paper expands the capability of previous approaches to fault detection and identification using fault trees for application to dynamically changing systems. The technique has two phases. The first phase is modelling and preparation carried out offline. This gathers information on the effects that sub-system failure will have on the system performance. Causes of the sub-system failures are developed in the form of fault trees. The second phase is application. Sensors are installed on the system to provide information about current system performance from which the potential causes can be deduced. A simple system example is used to demonstrate the features of the method. To illustrate the potential for the method to deal with additional system complexity and redundancy, a section from an aircraft fuel system is used. A discussion of the results is provided.     

Bayesian belief networks for system fault diagnostics

Fault diagnostic methods aim to recognize when faults exist on a system and to identify the failures that have caused the fault. The symptoms of the fault are obtained from readings from sensors located on the system. When the observed readings do not match those expected then a fault can exist. Using the detailed information provided by the sensors, a list of the failures (singly or in combinations) that could cause the symptoms can be deduced. In the last two decades, fault diagnosis has received growing attention due to the complexity of modern systems and the consequent need for more sophisticated techniques to identify the failures when they occur. Detecting the causes of a fault quickly and efficiently means reducing the costs associated with the system unavailability and, in certain cases, avoiding the risks of unsafe operating conditions. Bayesian belief networks (BBNs) are probabilistic models that were developed in artificial intelligence applications but are now applied in many fields. They are ideal for modelling the causal relations between faults and symptoms used in the detection process. The probabilities of events within the BBN can be updated following observations (evidence) about the system state. In this paper we investigate how BBNs can be applied to diagnose faults on a system. Initially Fault trees (FTs) are constructed to indicate how the component failures can combine to cause unexpected deviations in the variables monitored by the sensors. Converting FTs into BNs enables the creation of a model that represents the system with a single network, which is constituted by sub‐networks. The posterior probabilities of the components' failures give a measure of those components that have caused the symptoms observed. The method gives a procedure that can be generalized for any system where the causality structure can be developed relating the system component states to the sensor readings. The technique is demonstrated with a simple example system. Copyright © 2008 John Wiley & Sons, Ltd.     

Mode automata and their compilation into fault trees

In this article, we advocate the use of mode automata as a high level representation language for reliability studies. Mode automata are states/transitions based representations with the additional notion of flow. They can be seen as a generalization of both finite capacity Petri nets and block diagrams. They can be assembled into hierarchies by means of composition operations.The contribution of this article is twofold. First, we introduce mode automata and we discuss their relationship with other formalisms. Second, we propose an algorithm to compile mode automata into Boolean equations (fault trees). Such a compilation is of interest for two reasons. First, assessment tools for Boolean models are much more efficient than those for states/transitions models. Second, the automated generation of fault trees from higher level representations makes easier their maintenance through the life cycle of systems under study.     

Analytical propagation of uncertainties through fault trees

A method is presented which enables one to propagate uncertainties described by uniform probability density functions through fault trees. The approach is analytical. It is based on calculating the expected value and the variance of the top event probability. These two parameters are then equated with the corresponding ones of a beta-distribution. An example calculation comparing the analytically calculated beta-pdf (probability density function) with the top event pdf obtained using the Monte-Carlo method shows excellent agreement at a much lower expense of computing time.     

Hybrid Petri net and digraph approach for deadlock prevention in automated manufacturing systems

This paper proposes a hybrid approach to deadlock prevention in automated manufacturing systems that combines Petri nets (PNs) and digraphs, so taking advantage of the strong points of both techniques. The approach uses digraphs to make the detection of deadlock conditions easier and then translates the obtained information in empty siphons of the PN modelling the same system. The proposed methodology allows the implementation of new PN‐based deadlock prevention control policies. A case study and the simulation results show the benefits of the new control strategies.     

Efficient algorithms to assess component and gate importance in fault tree analysis

One of the principal activities of risk assessment is either the ranking or the categorization of structures, systems and components with respect to their risk-significance or their safety-significance. Several measures, so-called importance factors, of such a significance have been proposed for the case where the support model is a fault tree. In this article, we show how binary decision diagrams can be use to assess efficiently a number of classical importance factors. This work completes the preliminary results obtained recently by Andrews and Sinnamon, and the authors. It deals also with the concept of joint reliability importance.     

故障树分析法在某型飞机火控系统故障诊断中的应用

故障树分析法是系统安全、可靠性分析研究中常用的一种方法。基于故障树分析法与专家系统相结合的某型飞机火控系统故障诊断仪,以机载火控系统不工作为顶事件,建立了故障树,并对故障树作了定性分析,本系统不但具有故障诊断能力,还具有较强的自学习的功能。结果表明,故障树分析法是机载火控系统故障诊断的一种有效方法。     

Model-based system monitoring and diagnosis of failures using statecharts and fault trees

Models such as statecharts and fault trees become increasingly more available in electronic form as they progressively find more useful applications in the development of safety critical systems. As these models typically reduce in their utility after system certification, however, useful knowledge about the behaviour of the system remains unused in the operational phase of the system lifecycle. In this paper, we show that this knowledge could be exploited in the context of an on-line hazard-directed monitoring scheme in which a suitable specification derived from design models and safety analyses forms a reference monitoring model. As a practical application of this approach, we propose a generic safety monitor that can operate on statecharts and fault trees to support the on-line detection, diagnosis and control of hazardous failures in real-time. We discuss the structuring of the monitoring model, the monitoring algorithms and report on a case study performed on a model aircraft fuel system.     

破碎设备网络化在线状态监测与故障诊断系统

应用虚拟仪器技术进行破碎设备在线监测与故障诊断,并用网络技术实现数据的网络传输和远程监测.利用NI公司提供的G web Server实现系统监测显示面板的网页发布,利用Datasocket技术快速实现监测数据的网络传输.LabVIEW和网络技术的结合可以方便客户获取检测图像和检测结果.本系统在对破碎设备主要性能参数（如破碎机水平轴扭矩,振动,速度,间隙,温度,压力,流量等）的在线检测和远程故障诊断上均取得了较好的效果.     

Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches

Safety analysis in gas process facilities is necessary to prevent unwanted events that may cause catastrophic accidents. Accident scenario analysis with probability updating is the key to dynamic safety analysis. Although conventional failure assessment techniques such as fault tree (FT) have been used effectively for this purpose, they suffer severe limitations of static structure and uncertainty handling, which are of great significance in process safety analysis. Bayesian network (BN) is an alternative technique with ample potential for application in safety analysis. BNs have a strong similarity to FTs in many respects; however, the distinct advantages making them more suitable than FTs are their ability in explicitly representing the dependencies of events, updating probabilities, and coping with uncertainties. The objective of this paper is to demonstrate the application of BNs in safety analysis of process systems. The first part of the paper shows those modeling aspects that are common between FT and BN, giving preference to BN due to its ability to update probabilities. The second part is devoted to various modeling features of BN, helping to incorporate multi-state variables, dependent failures, functional uncertainty, and expert opinion which are frequently encountered in safety analysis, but cannot be considered by FT. The paper concludes that BN is a superior technique in safety analysis because of its flexible structure, allowing it to fit a wide variety of accident scenarios.     

Approximate estimation of system reliability via fault trees

In this article, we show how fault tree analysis, carried out by means of binary decision diagrams (BDD), is able to approximate reliability of systems made of independent repairable components with a good accuracy and a good efficiency. We consider four algorithms: the Murchland lower bound, the Barlow-Proschan lower bound, the Vesely full approximation and the Vesely asymptotic approximation. For each of these algorithms, we consider an implementation based on the classical minimal cut sets/rare events approach and another one relying on the BDD technology. We present numerical results obtained with both approaches on various examples.     

New methods to determine the importance measures of initiating and enabling events in fault tree analysis

Components' importance measures play a very important role in system reliability analysis. They are used to identify the weakest parts of the system for design improvement, failure diagnosis and maintenance. This paper deals with the problem of determining the importance measures of basic events in case of unreliability analysis of binary coherent and non-coherent fault trees. This type of analysis is typical of catastrophic top events, characterised by unacceptable consequences. Since the unreliability of systems with repairable components cannot be exactly calculated via fault tree, the Expected Number of Failures - which is obtained by integrating the unconditional failure frequency - is considered as it represents a good upper bound. In these cases it is important to classify events as initiators or enablers since their roles in the system are different, their sequence of occurrence is different and consequently they must be treated differently. New equations based on system failure frequency are described in this paper for determining the exact importance measures of initiating and enabling events. Simple examples are provided to clarify the application of the proposed calculation methods. Compared with the exact methods available in the literature, those proposed in this paper are easier to apply by hand and are simpler to implement in a fault tree analyser.     

统计过程控制在无人机故障预报系统中的应用

无人机故障预报是无人机故障检测的重要组成部分之一,它能有效地减小无人机的故障发生率。利用统计过程控制(SPC)理论,从数理统计的角度对某无人机的测试数据进行分析,并根据常见的五种故障数据,设计了一套无人机故障预报系统,同时给出了系统的工作原理和具体的实现过程。本系统有助于技术人员发现无人机的潜在故障并判断检测设备是否虚警,具有良好的使用性能和价值。     

Plasma diagnostics in plasma processing for nanotechnology and nanolevel chemistry

The author reviews the role of various plasma diagnostics in plasma processing for nanotechnology, and points out some essential methods of spectroscopic methods to diagnose plasmas for nanoprocessing. Two experimental examples are discussed between the characteristics of nanomaterials and plasma parameters. One is measurement of rotation temperature in processing of carbon nanotube. The other is that of vibrational temperature in surface nitriding of titanium by nitrogen plasma processing. We summarize what to measure and how to measure them from the technical viewpoint of plasma diagnostics.     

Plasma diagnostics in plasma processing for nanotechnology and nanolevel chemistry

The author reviews the role of various plasma diagnostics in plasma processing for nanotechnology, and points out some essential methods of spectroscopic methods to diagnose plasmas for nanoprocessing. Two experimental examples are discussed between the characteristics of nanomaterials and plasma parameters. One is measurement of rotation temperature in processing of carbon nanotube. The other is that of vibrational temperature in surface nitriding of titanium by nitrogen plasma processing. We summarize what to measure and how to measure them from the technical viewpoint of plasma diagnostics.     

Uncertainty analyses in fault trees and Bayesian networks using FORM/SORM methods

Efficient computational methods based on first and second order approximations (FORM/SORM) of the tails of a random variable, which is defined as a function of a set of basic variables, are given. Interesting examples of these types of functions are the probabilities of occurrence of the root events of fault trees or nodes in a Bayesian network. The method allows estimation of extreme percentiles and high confidence one-sided probability intervals of the target variables. Several examples of applications to real cases are used to illustrate the whole process.     

Attractors in cutting and their future use in diagnostics

It is shown that measurement of cutting tool vibration trajectories in three-dimensional space makes it possible to diagnose cutting, including wear, more effectively. Various aspects of using attractors in diagnostic systems are considered. Translated from Izmeritel’naya Tekhnika, No. 2. pp. 37–41, February, 2009.     

Cold plasma chemistry and diagnostics

This paper gives an overview of the main chemical processes which take place in cold plasmas generated in glow discharges, both in the gas phase and at the surfaces in contact with the plasma. Illustrative examples are provided where the relevance of different processes is evinced. Most common plasma characterization techniques and their recent improvements are also presented briefly.     

在线处理分子筛纯化系统阀门故障

介绍分子筛纯化系统可以在线处理的阀门故障和处理措施,结合再生气出口阀不停车更换的实际操作,总结不停车处理阀门故障的操作经验。     

某低温系统实时故障诊断专家系统的研究与应用

介绍了某低温系统在线故障诊断专家系统,叙述了诊断系统的基本结构及故障监测、预测和故障处理。系统使用delphi开发工具,利用各运行参数之间的逻辑关系作为推理依据,运用经验知识和理论知识相结合作为故障诊断的理论依据,对整个低温系统进行状态监测和故障诊断,指导操作人员排除故障。