Automatic Generation Of Fault Trees Research Articles

An FTA Method for the Unavailability of Supply in Gas Networks Supported by Physical Models

This paper presents a fault tree analysis (FTA) method for the unavailability of supply of gas networks. The method is based on the automatic generation of fault trees, which estimate the probability of disruption of the gas delivery from terminals/storages to the demand nodes. Moreover, it allows probabilistic analyses of the availability of gas supply to individual demand nodes and to the overall gas network. To assess the importance of each network component, the risk achievement worth and the risk reduction worth importance measures are utilized. The developed method can identify weakness in the gas network and can guide expansion planning and maintenance scheduling activities. Furthermore, the developed framework is enriched by steady-state analysis of the gas network operations performed using a physical flow/pressure model. The impact of a component failure on the gas supply interruption at different demand nodes is assessed by the physical model in retrospective to the FTA results. The framework is exemplified with reference to the reduced gas network of Great Britain. The results provide insights to support a robust reliability assessment of the gas network. Moreover, the probability mapping of the most important components in the gas network informs optimal strategies for maintenance scheduling as well for prioritizing improvements of the gas network aimed at significant risk reduction. Remarkably, the failure of components identified as most important according to the FTA importance measures may not have significant effects on the gas supply. A discussion on the range of applicability and limitations of the two methodologies is provided.

Automatic Fault-Tree Generation as a Support for Safety Studies of Railway Interlocking Systems

In the paper, the safety analysis of the state-of-the-art computer based railway interlocking systems is discussed. The fault-tree analysis is selected as a primary method to evaluate the failure behavior of the system. Since the hardware system and the functional tasks of the modern interlocking systems can be very complicated, automatic fault-tree generation is introduced, which provides faster analysis and models without human errors.

Application of the integrated reliability analysis system (IRAS)

Abstract This paper introduces a UNIX-based computer aided reliability assessment system, IRAS, which was developed in the Brite/Euram project BE-4250. It utilises fault propagation models for automatic generation of Fault Trees, Cause–Consequence Diagrams and FMECA. Therefore, it has the following features: a Model Builder which allows the creation of the fault propagation models in a hierarchical manner; a Fault Tree Analysis module that is able to generate Fault Trees on demand and to extract minimal cut sets; an FMECA module that is able to search for and group effects of basic events according to their criticality, severity and probability; a Real Time Fault Location (RTFL) module that enables the fast detection of the most probable cause(s) of system malfunction based on information available from sensors and/or operator. This paper describes the underlying ideas and procedures of IRAS and shows an example application to a Hot Strip Steel Mill.

The measure of effect in an expert system for automatic generation of fault trees : Peiquing Huang and Zhaokun Li. IEEE Trans. Reliab.41, 57 (1992)

The measure of effect in an expert system for automatic generation of fault trees : Peiquing Huang and Zhaokun Li. IEEE Trans. Reliab.41, 57 (1992)

The measure of effect in an expert system for automatic generation of fault trees

The authors introduce the measure of effect, which reflects both the probability of the cause factor and its effect on the event in the context of an expert system for automatic generation of fault trees. Its properties and applications are investigated and several theorems are proven. Using this measure it is possible to delete those events which have a minor effect on the resultant events in real cases and make the fault trees generated in the first stage simpler and more practical. An example illustrates the use of the measure of effect in the inference procedure. >

Generation Of Fault Trees From SimulatedIncipient Fault Case Data

Fault tree analysis is widely used in industry in fault diagnosis. The diagnosis of incipient or 'soft' faults is considerably more difficult than of 'hard' faults, which is the situation considered normally. A detailed fault tree model reflecting signal variations over wide range is required for diagnosing such soft faults. This paper describes the investigation of a machine learning method for the automatic generation of fault trees for incipient faults. Features based on the FFT (Fast Fourier Transform) of the time response simulations are used are used to provide a training set of examples comprising records of fault types, severity and feature list. The algorithm presented, called IFT, is derived from the ID3 algorithm for the induction of decision trees. A significant aspect of this approach is that it does not require any detailed knowledge or analysis of the application system. All that is needed is a 'black-box' model of the system; i.e. knowledge of what faults arise from measurable quantities taking on particular values. The proposed procedure is illustrated using detailed simulation results for a servomechanism typically found in machine tool applications and the results to date indicate the feasibility of the approach.

The Knowledge Engineering Approach ToReliability Analysis

This paper presents the FTAES system, a knowledge based environment designed for the assessment and measurement of reliability, availability, maintainability and safety of industrial systems using fault tree representation. Object oriented structures are used to represent the problem's domain, production rules, algorithms and database structures are the basic elements of the system. FTAES was structured into an open blackboard architecture in order to allow further inclusion of real time diagnostic modules and automatic fault tree generation. Uncertainty, vagueness and fuzzyness are represented and solved with fuzzy logic approaches.