A hierarchical coloured Petri net model of fleet maintenance with cannibalisation

Cannibalisation refers to a maintenance action where an unserviceable part in an inoperative platform is replaced by a serviceable part of the same type from another platform. It helps a fleet meet operational requirements when spares are in short supply but leads to more maintenance tasks to be carried out. In practice, cannibalisation may be performed in an unrestricted manner, or through the use of cannibalisation birds. A cannibalisation bird is a platform which is selected as the primary source of cannibalisation, while any inoperative platform can be a cannibalisation source under the unrestricted policy. In order to aid fleet managers in making cannibalisation-related decisions, this paper presents a hierarchical coloured Petri net (HCPN) model of a fleet operation and maintenance process which considers mission-oriented operation, multiple level maintenance, multiple cannibalisation policies (no cannibalisation, unrestricted cannibalisation and cannibalisation bird), maintenance scheduling and spare inventory management. The model is applied to an example fleet to compare the effects of different cannibalisation policies on fleet performance using a number of performance measures related to reliability and maintenance and to optimise the number of cannibalisation birds used and the length of time that a platform is taken as a cannibalisation bird for the fleet

Using a Novel Hierarchical Coloured Petri Net to Model and Optimise Fleet Spare Inventory, Cannibalisation and Preventive Maintenance

Spare part availability is crucial to restoring inoperative platforms to the working state. Platforms failing during operation undergo corrective maintenance to replace failed components with spares. To reduce the frequency of this unplanned, corrective maintenance, platforms are inspected periodically and degraded components preventively replaced. Maintenance delays occur when spares are unavailable but cannibalisation can reduce these delays by allowing working components to be removed from inoperative platforms and used to restore other inoperative platforms. Fleets can be deployed across multiple bases that are served by one or more depots. Failed components that cannot be repaired at a base are sent to a depot for repair, along with associated requests for spares, which are satisfied by depot inventories.The management of fleet corrective and preventive maintenance, cannibalisation, spare inventories, provision of spares to bases and depots, and response of the depot to spare requests is a complex problem for fleet maintenance managers and critical to ensuring acceptable fleet performance. This paper presents a novel hierarchical coloured Petri net (HCPN) model of a fleet spare inventory system, which accounts for these issues alongside fleet deployment and mission-oriented operation. The application of the model is demonstrated using case studies of two example fleets

A coloured Petri net framework for modelling aircraft fleet maintenance

The aircraft fleet maintenance organisation is responsible for keeping aircraft in a safe, efficient operating condition. Through optimising the use of maintenance resources and the implementation of maintenance activities, fleet maintenance management aims to maximise fleet performance by, for example, ensuring there is minimal deviation from the planned operational schedule,that the number of unexpected failures is minimised or that maintenance cost is kept at a minimum. To obtain overall fleet performance, the performance of individual aircraft must first be known. The calculation of aircraft performance requires an accurate model of the fleet operation and maintenance processes. This paper aims to introduce a framework that can be used to build aircraft fleet maintenance models. A variety of CPN (coloured Petri nets) models are established to represent fleet maintenance activities and maintenance management, as well as the factors that have a significant impact on fleet maintenance including fleet operation, aircraft failure logic and component failure processes. Such CPN models provide an ideal structured framework for Monte Carlo simulation analysis, within which calculations can be performed in order to determine numerous fleet reliability and maintenance performance measures

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 UAVs (unmanned aerial vehicles) 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, due 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. Since Binary Decision Diagrams (BDD) 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. This paper presents a new Binary Decision Diagram 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 which 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 since 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

A stochastic model for railway track asset management

The determination of the strategy to ensure that the geometry for railway track is kept within acceptable limits, in a cost effective manner, is a complex process. It requires the simultaneous consideration of the activities which govern inspection, maintenance and renewal. In addition to this the geometry degradation process is dependent upon the maintenance history. The condition where the track geometry is shown to have deteriorated to a level where intervention is required can be improved using a tamping machine. Tamping is carried out by a special train which measures the geometry of the rails, predicts the correction needed, lifts the rails to the required position, inserts tines into the ballast either side of the sleepers and packs the ballast such that the correct rail position is attained. Whilst improving the geometry this process has the disadvantage that it also breaks the ballast which accelerates the track geometry degradation and reduces the time between interventions. This paper describes a modelling process to predict the state of the track geometry given any specified asset management strategy. It is based on the Petri net method and in addition to predicting the track condition over time it can also compute the expected whole life costs. By varying the parameters which govern the inspection, maintenance and renewal of the ballast as the most cost effective means to achieve the required level of performance can be predicted

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

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 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

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