An analytic solution for a fault tree with circular logics in which the systems are linearly interrelated

A circular logic or a logical loop is defined as the infinite circulation of supporting relations due to their mutual dependencies among the systems in the fault tree analysis. While many methods to break the circular logic have been developed and used in the fault tree quantification codes, the general solution for a circular logic is not generally known as yet. This paper presents an analytic solution for circular logics in which the systems are linearly interrelated with each other. To formulate the analytic solution, the relations among systems in the fault tree structure are described by the Boolean equations. The solution is, then, obtained from the successive substitutions of the Boolean equations, which is equivalent to the attaching processes of interrelated system's fault tree to a given fault tree. The solution for three interrelated systems and their independent fault tree structures are given as an example.     

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。     

Monte Carlo simulation for model-based fault diagnosis in dynamic systems

Fault diagnosis requires the accurate estimation of the dynamic state of the system in real time. This can be pursued starting from a model of the system dynamics and on measurements related to the state of the system. In real applications, the nonlinearity of the model and non-Gaussianity of the noise typically affecting the measurement challenge the classical approximate approaches, e.g. the extended-Kalman, Gaussian-sum and grid-based filters, which often turn out to be inaccurate and/or too computationally expensive for real-time applications. On the contrary, Monte Carlo estimation methods, also called particle filters, can be very effective. Based on sequential importance sampling and on a Bayesian formulation of the estimation problem, these methods recursively approximate the relevant probability distributions of the system state by random measures composed of particles (sampled values of the unknown state variables) and associated weights.The present paper aims at demonstrating the power of particle filtering for fault diagnosis. This is done by applying an estimation procedure called sampling importance resampling (SIR) to a case study of literature.     

A practical method for accurate quantification of large fault trees

This paper describes a practical method to accurately quantify top event probability and importance measures from incomplete minimal cut sets (MCS) of a large fault tree. The MCS-based fault tree method is extensively used in probabilistic safety assessments. Several sources of uncertainties exist in MCS-based fault tree analysis. The paper is focused on quantification of the following two sources of uncertainties: (1) the truncation neglecting low-probability cut sets and (2) the approximation in quantifying MCSs. The method proposed in this paper is based on a Monte Carlo simulation technique to estimate probability of the discarded MCSs and the sum of disjoint products (SDP) approach complemented by the correction factor approach (CFA). The method provides capability to accurately quantify the two uncertainties and estimate the top event probability and importance measures of large coherent fault trees. The proposed fault tree quantification method has been implemented in the CUTREE code package and is tested on the two example fault trees.     

DSD: a generic software package for model-based fault diagnosis in dynamic systems

A user-friendly, interactive software package is described that can be used for fault diagnosis in dynamic systems. The methodology is based on the representation of system evolution in time as probability of transitions between sets of magnitude intervals in the state/parameter space. The software is developed in C++ for Windows NT platform. The display capabilities of the software and its implementation are illustrated using a second order system.     

Component-based modeling of systems for automated fault tree generation

One of the challenges in the field of automated fault tree construction is to find an efficient modeling approach that can support modeling of different types of systems without ignoring any necessary details. In this paper, we are going to represent a new system of modeling approach for computer-aided fault tree generation. In this method, every system model is composed of some components and different types of flows propagating through them. Each component has a function table that describes its input-output relations. For the components having different operational states, there is also a state transition table. Each component can communicate with other components in the system only through its inputs and outputs. A trace-back algorithm is proposed that can be applied to the system model to generate the required fault trees. The system modeling approach and the fault tree construction algorithm are applied to a fire sprinkler system and the results are presented.