Aircraft fuel rig system fault diagnostics based on the application of digraphs

The issue of fault diagnostics is a dominant factor concerning current engineering systems. Information regarding possible failures is required in order to minimize disruption caused to functionality. A method proposed in this paper utilizes digraphs to model the information flow within an application system. Digraphs are composed from a set of nodes representing system process variables or component failure modes. The nodes are connected by signed edges thus illustrating the influence, be it positive or negative, one node has on another. System fault diagnostics is conducted through a procedure of back-tracing in the digraph from a known deviating variable. A computational method has been developed to conduct this process. Comparisons are made between retrieved transmitter readings and those expected while the system is in a known operating mode. Any noted deviations are assumed to indicate the presence of a failure. The current paper looks in detail at the application of the digraph diagnostic method to an industrially based test stand of an aircraft fuel system. This research includes transient system effects; the rate of change of a parameter is taken into consideration as a means of monitoring the system dynamically. The validity of the results achieved, through performing fault diagnostics based on the use of a digraph model, is evaluated. Finally, the effectiveness and scalability issues associated with the application of the method are addressed

Enhanced diagnosis of faults using the digraph approach applied to a dynamic aircraft fuel system

Malfunctions within commercial aircraft can considerably increase both the financial cost of downtime and the disruption caused to passenger travel. For this reason prompt detection, diagnosis, and rectification of faults is imperative to the successful operation of such a system. In this paper the fault diagnostic problem is tackled based on the application of the digraph procedure. Digraphs model the information flow, and hence fault propagation, through a system. A computational method has been successfully developed to conduct the fault diagnostics process and produce a list of the fault combinations determined. The scope of the method has been demonstrated by consideration of two modes of operation to the application of a commercial aircraft fuel system, namely that of a Boeing 777. In addition the paper highlights the contribution of the development of a reduction method to enhance the likelihood of identifying the possible failure causes in three ways from different viewpoints. The three methods provide the option of determining the component at fault, the most probable failure mode cause, and also evidence for a particular component fault

Integrated system fault diagnostics utilising digraph and fault tree-based approaches

With the growing intolerance to failures within systems, the issue of fault diagnosis has become ever prevalent. Information concerning these possible failures can help to minimise the disruption to the functionality of the system by allowing quick rectification. Traditional approaches to fault diagnosis within engineering systems have focused on sequential testing procedures and real time mechanisms. Both methods have been predominantly limited to single fault causes. Latest approaches also consider the issue of multiple faults in reflection to the characteristics of modern day systems designed for high reliability. In addition, a diagnostic capability is required in real time and for changeable system functionality. This paper focuses on two approaches which have been developed to cater for the demands of diagnosis within current engineering systems, namely application of the fault tree analysis technique and the method of digraphs. Both use a comparative approach to consider differences between actual system behaviour and that expected. The procedural guidelines are discussed for each method, with an experimental aircraft fuel system used to test and demonstrate the features of the techniques. The effectiveness of the approaches are compared and their future potential highlighted

Comparison of digraph and fault tree based approaches for system fault diagnostics

The issue of fault diagnosis has become ever prevalent in engineering systems. Information concerning possible failures within a system can help to minimise the disruption to the functionality of the system by allowing quick rectification. Traditional approaches to fault diagnosis within engineering systems have focused on sequential testing procedures and real time mechanisms. Both methods have been predominantly limited to single fault causes. Latest approaches also consider the issue of multiple faults in reflection to the characteristics of modern day systems designed for high reliability. The bases of these approaches are the fault tree analysis technique and the method of digraphs. Both use a comparative approach to consider differences between actual system behaviour and that expected. This paper focuses on reviewing the developments with these methods to diagnose faults within an aircraft fuel system and to compare their effectiveness and future potential

Fault management for data systems

Issues related to automating the process of fault management (fault diagnosis and response) for data management systems are considered. Substantial benefits are to be gained by successful automation of this process, particularly for large, complex systems. The use of graph-based models to develop a computer assisted fault management system is advocated. The general problem is described and the motivation behind choosing graph-based models over other approaches for developing fault diagnosis computer programs is outlined. Some existing work in the area of graph-based fault diagnosis is reviewed, and a new fault management method which was developed from existing methods is offered. Our method is applied to an automatic telescope system intended as a prototype for future lunar telescope programs. Finally, an application of our method to general data management systems is described

Robot graphic simulation testbed

The objective of this research was twofold. First, the basic capabilities of ROBOSIM (graphical simulation system) were improved and extended by taking advantage of advanced graphic workstation technology and artificial intelligence programming techniques. Second, the scope of the graphic simulation testbed was extended to include general problems of Space Station automation. Hardware support for 3-D graphics and high processing performance make high resolution solid modeling, collision detection, and simulation of structural dynamics computationally feasible. The Space Station is a complex system with many interacting subsystems. Design and testing of automation concepts demand modeling of the affected processes, their interactions, and that of the proposed control systems. The automation testbed was designed to facilitate studies in Space Station automation concepts

Aircraft fuel system diagnostics using digraphs

Faults within any system will decrease its functionality, with the worst case scenario being complete system failure. When faults do occur it is imperative they can be diagnosed and ultimately rectified as quickly as possible, minimising the effects of such a failure. In the case of a commercial aircraft system efficient diagnosis can optimise the time to return the aircraft to service, thus allowing less disruption to passenger travel. For a military air vehicle diagnosis of the status of the system can mean that missions can be altered or aborted given the faults detected. With the increasing complexity of modern day systems, designed for reliability, it is usually several items that are required to fail before catastrophic or complete system failure is experienced, thus diagnosis of multiple faults is important. In addition, for the most effective diagnosis, detection needs to happen in real time. A method of finding faults or combinations of faults as they occur is the subject of this paper. The approach uses sensor readings to assess the state of the system. The method of digraphs is used to diagnose the faults by considering deviations in the sensor readings from the expected system state. Digraphs allow a means to represent the propagation of inputs through a system, reflecting the relationships and interactions between the components. The primary research has shown the applicability of using the digraph based approach for fault diagnosis on a simulated test stand of an aircraft fuel system. The analysis has assumed steady state conditions although guidelines have been provided for use for dynamic behaviour. The technique has shown potential for extension for diagnosis to the real aircraft fuel system

Space station advanced automation

In the development of a safe, productive and maintainable space station, Automation and Robotics (A and R) has been identified as an enabling technology which will allow efficient operation at a reasonable cost. The Space Station Freedom's (SSF) systems are very complex, and interdependent. The usage of Advanced Automation (AA) will help restructure, and integrate system status so that station and ground personnel can operate more efficiently. To use AA technology for the augmentation of system management functions requires a development model which consists of well defined phases of: evaluation, development, integration, and maintenance. The evaluation phase will consider system management functions against traditional solutions, implementation techniques and requirements; the end result of this phase should be a well developed concept along with a feasibility analysis. In the development phase the AA system will be developed in accordance with a traditional Life Cycle Model (LCM) modified for Knowledge Based System (KBS) applications. A way by which both knowledge bases and reasoning techniques can be reused to control costs is explained. During the integration phase the KBS software must be integrated with conventional software, and verified and validated. The Verification and Validation (V and V) techniques applicable to these KBS are based on the ideas of consistency, minimal competency, and graph theory. The maintenance phase will be aided by having well designed and documented KBS software

A sensor selection method for fault diagnostics

In the modern world, systems are becoming increasingly complex, consisting of large numbers of components and their failures. In order to monitor system performance and to detect faults and diagnose failures, sensors can be used. However, using sensors can increase the cost and weight of the system. Therefore, sensors need to be selected based on the information that they provide. In this paper, a sensor selection process is introduced based on a novel sensor performance metric. In this process, sensors are selected based on their ability to detect faults and diagnose failures of components in the system, as well as the severity of failure effects on system performance. A Bayesian Belief Network (BBN) is used to model the outputs of the sensors. Sensor reading evidence is introduced in the BBN to enable the component failures to be identified. A simple system example is used to illustrate the proposed approach

Integrating Exception Handling in Machine Development

In modern batch plants, alarm floods overwhelming the operator is a common problem of ever increasing severity. As plant hardware increase in complexity and performance, measures such as downtime and meantime between failures, increase in importance and the need for a well-functioning exception handling routine therefore grows critical. In order to minimize downtime and hence boost production efficiency, it is important that faulty or unexpected behaviour is noted early and diagnosed accurately. If it is, the machine or its operator can deal with it fast and accurately, perhaps even while production continues. If it is not, however, it basically means two bad things, the first being unnecessary production stops and the second a flooded alarm list. The alarm list is the screen on which all exceptions are listed and which is meant to tell the process operator what is wrong and what he should do about it. If no care is taken about it, the list will be flooded since one key exception will cause several others, leaving the operator with the quite unpleasant task of identifying what went wrong and how he should fix it. This master's thesis, carried out at Tetra Pak in Lund, Sweden, presents a well-structured exception handling method and a way of linking it to a workflow. It uses advantages of reusability, modularisation and linking to almost any structural model such as a Function Means Tree (used in WCE) or a UML-model. The linking has several advantages minimizing the work and improving evolutionary possibilities. Examples are being made for illustrational purposes but no implementational efforts or issues are addressed. A well-defined structure for information flow is also suggested to provide modular thinking that, for example, prepares the possibility to collect data for statistical analysis and such