A non-homogeneous Markov model for phased-mission reliability analysis

Smotherman, Mark; Zemoudeh, K.

doi:10.1109/24.46486

Cited by 113 publications

(51 citation statements)

References 12 publications

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“…This is due to the fact that at the phase change times, the system must occupy a state that allows both of the involved phases to function. The phases of the mission will be statistically dependent and an approach for solution has been presented by Smotherman and Zemoudeh [4] for repairable components. Of the many considered solutions to phased mission problems, simulation techniques typically offer the greatest generality in representation, but are also often the most expensive in computational requirements.…”

Section: Introductionmentioning

confidence: 99%

Phased mission modelling using fault tree analysis

Band

Andrews

2004

Proceedings of the Institution of Mechanical Engineers, Part E:

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Abstract:Many types of system operate for missions which are made up of several phases. For the complete mission to be a success, the system must operate successfully during each of the phases. Examples of such systems include an aircraft flight, and also many military operations for both aircraft and ships. An aircraft mission could be considered as the following phases: taxiing to the runway, takeoff, climbing to the correct altitude, cruising, descending, landing and taxiing back to the terminal. Component failures can occur at any point during the mission but their condition may only be critical for one particular phase. As such it may be that the transition from one phase to another is the critical event leading to mission failure, the component failures resulting in the system failure may have occurred during some previous phase. This paper describes a means of analysing the reliability of non-repairable systems which undergo phased missions. Fault Tree Analysis has been used as the method to assess the system performance. The results of the analysis are the system failure modes in each phase (minimal cut sets), the failure probability in each phase and the total mission unreliability. To increase the efficiency of the analysis the fault trees constructed to represent the system failure logic are analysed using a modularisation method. Binary Decision Diagrams (BDD's) are then employed to quantify the likelihood of failure in each phase.

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Section: Introductionmentioning

confidence: 99%

Phased mission modelling using fault tree analysis

Band

Andrews

2004

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…A number of methods have been utilised to analyse phased missions, such as Fault Tree Analysis (FTA) [5], Markov analysis [6], Binary Decision Diagrams (BDD) [7,8] and Cause Consequence Analysis (CCA) [9]. The suitability of each of these analysis methods for use in a prognostics capability is shown in Table 1.…”

Section: B Suitable Methods Of Analysismentioning

confidence: 99%

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

Prescott

Andrews

2009

2009 8th International Conference on Reliability, Maintainability and Safety

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“…The categorization of a mission to be non-repairable or repairable influences the reliability modelling techniques that can be used. Fault tree approaches are appropriate for non-repairable phased missions [1][2][3], and Markov methods when some degree of repair is possible [4][5][6]. This paper focuses on nonrepairable missions.…”

Section: Introductionmentioning

confidence: 99%

A ternary decision diagram method to calculate the component contributions to the failure of systems undergoing phased missions

Andrews

2008

Proceedings of the Institution of Mechanical Engineers, Part O:

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The way that many systems are utilized can be expressed in terms of missions which are split into a sequence of contiguous phases. Mission success is only achieved if each of the phases is successful, and each phase is required to achieve a different objective and use different elements of the system. The reliability analysis of a phased mission system will produce the probability of failure during each of the phases, together with the overall mission failure likelihood. In the event that the system performance does not meet with the acceptance requirement, weaknesses in the design are identified and improvements made to rectify the deficiencies. In conventional system assessments, importance measures can be predicted which provide a numerical indicator of the significance of the role that each component plays in the system failure. Through the development of appropriate importance measures, this paper provides ways of identifying the contribution made by each component failure to each phase failure and the overall mission failure. In addition, a means is given to update the system performance prediction as phases of the mission are successfully completed. The causes of phase failure are expressed as fault trees. The binary decision diagram (BDD) concept is extended to produce ternary decision diagrams (TDDs) to facilitate fast calculation of the importance measures.

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A non-homogeneous Markov model for phased-mission reliability analysis

Cited by 113 publications

References 12 publications

Phased mission modelling using fault tree analysis

Phased mission modelling using fault tree analysis

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

A ternary decision diagram method to calculate the component contributions to the failure of systems undergoing phased missions

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