The safe dispatch of aircraft with known faults

Time-limited dispatch (TLD) allows the dispatch of aircraft with faults present in their control systems for limited time periods. In order for TLD to be applied to an aircraft system it is first necessary to demonstrate that the relevant safety and certification requirements are being met by modelling the system in question. To do this existing modelling techniques use variations of fault tree analysis and Markov analysis with various simplifying assumptions, made to assist in the analytical process. Monte Carlo simulation is presented here as an alternative method of analysis, which can deal well with the potential difficulties that may present themselves when modelling TLD, such as the complex architectures of aircraft systems and dependencies that are introduced when applying TLD. In this paper a simple example system is introduced and the application of TLD to it is modelled using the existing variation of Markov analysis and a Monte Carlo simulation technique. The results obtained using the different techniques are seen to differ and a number of reasons are suggested for this difference

The safe dispatch of aircraft with known faults

Time-limited dispatch (TLD) allows the dispatch of aircraft with faults present in their control systems for limited time periods. In order for TLD to be applied to an aircraft system it is first necessary to demonstrate that the relevant safety and certification requirements are being met by modelling the system in question. To do this existing modelling techniques use variations of fault tree analysis and Markov analysis with various simplifying assumptions, made to assist in the analytical process. Monte Carlo simulation is presented here as an alternative method of analysis, which can deal well with the potential difficulties that may present themselves when modelling TLD, such as the complex architectures of aircraft systems and dependencies that are introduced when applying TLD. In this paper a simple example system is introduced and the application of TLD to it is modelled using the existing variation of Markov analysis and a Monte Carlo simulation technique. The results obtained using the different techniques are seen to differ and a number of reasons are suggested for this difference

A comparison of modelling approaches for the time-limited dispatch (TLD) of aircraft

The time-limited dispatch (TLD) of aircraft allows operators efficiently to meet certification requirements. In order to show that these requirements are met it is necessary to model the aircraft systems to which TLD is being applied. Currently, variations of fault tree analysis and Markov analysis are commonly used. However, in order to apply either of these methods, a number of assumptions are made to assist in the analysis. Monte Carlo simulation (MCS) is presented here as an alternative method of demonstrating the required level of system reliability. A simple system is analysed using a time-weighted average approach, a reduced fault state Markov approach, and an MCS approach. MCS is seen to offer benefits when modelling the application of TLD to a simple system that could also be seen in the modelling of the application of TLD to real aircraft systems

Aircraft safety modeling for time-limited dispatch

This paper offers an alternative method of modeling the Time-Limited Dispatch (TLD) of aircraft. Existing methods involve the use of fault tree analysis and Markov analysis with various simplifying assumptions. Monte Carlo simulation (MCS) is the suggested alternative, which overcomes the problems associated with the other techniques, such as dependencies between basic events (fault tree analysis) or huge number of system states (Markov analysis). The results obtained from the analysis of a simple example are compared for the existing modeling approaches and MCS. MCS is seen to have potential advantages, especially when modeling TLD for large, full scale systems

A reliability-based approach to mission planning in multi-platform phased missions

Many systems perform phased missions consisting of several distinct, sequential phases. Mission success depends on the successful completion of all mission phases. Increasingly, for example in military theatre, platforms operating phased missions are required to collaborate in order to achieve an overall mission objective, with specific platform phases containing specific tasks that contribute to that objective. Particularly, but not exclusively, in the case of autonomous vehicles, the calculation of phase and mission failure probabilities can be used to assist in making decisions on the future course of a mission. This paper describes how this decision making process can be implemented

A comparison of modelling approaches for the time-limited dispatch (TLD) of aircraft

The time-limited dispatch (TLD) of aircraft allows operators to efficiently meet certification requirements. In order to display that these requirements are met it is necessary to model the aircraft systems to which TLD is being applied. Currently variations of fault tree analysis and Markov analysis are commonly used. However, in order to apply either of these methods a number of assumptions are made in order to assist in the analysis. Monte Carlo simulation (MCS) is presented here as an alternative method of demonstrating the required level of system reliability. A simple system is analysed using a time-weighted average approach, a reduced fault state Markov approach and a MCS approach. MCS is seen to offer benefits when modelling the application of TLD to a simple system that could also be seen in the modelling of the application of TLD to real aircraft systems

A system reliability approach to decision making in autonomous multi-platform systems operating phased missions

This paper presents a decision making strategy for autonomous multi-platform systems, wherein a number of platforms perform phased missions in order to achieve an overall mission objective. Phased missions are defined for both single and multi-platform systems and a decision making strategy is outlined for such systems. The requirements for a tool performing such a strategy are discussed and methods and techniques, traditionally used for system reliability assessment, are identified to fulfill these requirements. Two examples are presented in order to demonstrate how a decision making tool would be employed in practice. Finally, a brief discussion of the efficient implementation of such a strategy is presented

Multiplatform phased mission reliability modelling for mission planning

Autonomous systems are being increasingly used in many areas. A significant example is unmanned aerial vehicles (UAVs), regularly being called upon to perform tasks in the military theatre. Autonomous systems can work alone or be called upon to work collaboratively towards common mission objectives. In this case it will be necessary to ensure that the decisions enable the progression of the platform objectives and also the overall mission objectives. The motivation behind the work presented in this paper is the need to be able to predict the failure probability of missions performed by a number of autonomous systems working together. Such mission prognoses can assist the mission planning process in autonomous systems when conditions change, with reconfiguration taking place if the probability of mission failure becomes unacceptably high. In a multiplatform phased mission a number of platforms perform their own phased mission that contributes to an overall mission objective. Presented in this paper is a methodology for calculating the phase failure probabilities of a multiplatform phased mission. These probabilities are then used to find the total mission failure probability. Prior to the mission the failure probabilities are used to decide if the original mission structure is acceptable. Once underway, failure probabilities, updated as circumstances change, are used to decide whether a mission should continue. Circumstances can change owing to failures on a platform, changing environmental conditions (weather), or the occurrence of unforeseen external events (emerging threats). This diagnostics information should be used to ensure that the updated failure probabilities calculated take into account the most up-to-date system information possible. Since the speed of decision making and the accuracy of the information used are essential, binary decision diagrams (BDDs) are utilized to form the basis of a fast, accurate quantification process

A reliability analysis method using binary decision diagrams in phased mission planning

The use of autonomous systems is becoming increasingly common in many fields. A significant example of this is the ambition to deploy unmanned aerial vehicles (UAVs) for both civil and military applications. In order for autonomous systems such as these to operate effectively, they must be capable of making decisions regarding the appropriate future course of their mission responding to changes in circumstance in as short a time as possible. The systems will typically perform phased missions and, owing to the uncertain nature of the environments in which the systems operate, the mission objectives may be subject to change at short notice. The ability to evaluate the different possible mission configurations is crucial in making the right decision about the mission tasks that should be performed in order to give the highest possible probability of mission success. Because binary decision diagrams (BDDs) may be quickly and accurately quantified to give measures of the system reliability it is anticipated that they are the most appropriate analysis tools to form the basis of a reliability-based prognostics methodology. The current paper presents a new BDD-based approach for phased mission analysis, which seeks to take advantage of the proven fast analysis characteristics of the BDD and enhance it in ways that are suited to the demands of a decision-making capability for autonomous systems. The BDD approach presented allows BDDs representing the failure causes in the different phases of a mission to be constructed quickly by treating component failures in different phases of the mission as separate variables. This allows flexibility when building mission phase failure BDDs because a global variable ordering scheme is not required. An alternative representation of component states in time intervals allows the dependencies to be efficiently dealt with during the quantification process. Nodes in the BDD can represent components with any number of failure modes or factors external to the system that could affect its behaviour, such as the weather. Path simplification rules and quantification rules are developed that allow the calculation of phase failure probabilities for this new BDD approach. The proposed method provides a phased mission analysis technique that allows the rapid construction of reliability models for phased missions and, with the use of BDDs, rapid quantification

Modeling and specification of time-limited dispatch categories for commercial aircraft

Time-limited dispatch allows the degraded redundancy dispatch of aircraft. Aircraft can be dispatched with certain control system faults and fault combinations for specified periods of time if the failure rates from those configurations meet certification requirements. The various system faults and fault combinations are assigned to dispatch categories according to these failure rates. This gives the dispatch criteria for the system. The overall failure rate of the system can then be calculated according to the dispatch criteria. Dispatch criteria are allocated to a small example system, and the system is subsequently modeled using a reduced-state Markov approach currently recommended in SAE ARP5107. An alternative method of setting dispatch criteria and modeling systems, using Monte Carlo simulation, is proposed in this paper, and this technique is also applied to the example system. Dispatch criteria applied to the different models are seen to differ, as are the system failure rates calculated using the different models. A method for setting the dispatch criteria for a system using a Monte Carlo simulation approach is introduced. The method is applied to a simple system, giving auditable results that exhibit the expected behavior for such a system. Because restrictive assumptions in the mathematics are unnecessary with Monte Carlo simulation, it is expected to give more accurate results in comparison to Markov approaches. Also, the results of the reduced-state Markov model appear to be largely dependent on failure rates, which are very difficult to determine