Darren Prescott

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 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.

A track ballast maintenance and inspection model for a rail network

The geometry of railway track governs the quality of the ride for passengers and, should it deteriorate to an extreme state, can become a safety concern with potential derailment. The geometry is dependent upon a number of different factors, among which is the condition of the ballast. Several options exist to control the condition of the ballast, including manual intervention, tamping and stoneblowing. Ballast condition is monitored implicitly by measuring the geometry of the railway lines using a specially equipped measurement train. By analysing the data collected by this train, the deterioration process of the railway geometry can be understood. Using this understanding, mathematical models can then be constructed, which take account of the possible maintenance and renewal options to predict the track state. This model enables decisions to be made on the best or optimal strategy for maintenance and renewal of the ballast. This article describes a model of the track maintenance process for a railway network. Owing to their flexibility, the model is formulated using a Petri net and combines the deterioration, maintenance and inspection processes for a railway network containing a number of regions. There are a limited number of maintenance machines in the network and the maintenance in each region is organised independently. The model also takes account of the fact that the maintenance of track sections with severe levels of ballast deterioration must take priority over all other maintenance in the network and allows for opportunistic maintenance to be analysed.

Modelling maintenance in railway infrastructure management

Conventional railway track, of the type seen throughout the majority of the UK rail network, is made up of rails that are fixed to sleepers (ties), which, in turn, are supported by ballast. The ballast comprises crushed, hard stone and its main purpose is to distribute loads from the sleepers as rail traffic passes along the track. Over time, the stones in the ballast deteriorate, leading the track to settle and the geometry of the rails to change. Changes in geometry must be addressed in order that the track remains in a safe condition. Track inspections are carried out by measurement trains, which use sensors to precisely measure the track geometry. Network operators aim to carry out maintenance before the track geometry degrades to such an extent that speed restrictions or line closures are required. However, despite the fact that it restores the track geometry, the maintenance also worsens the general condition of the ballast, meaning that the rate of track geometry deterioration tends to increase as the amount of maintenance performed to the ballast increases. This paper considers the degradation, inspection and maintenance of a single one eighth of a mile section of railway track. A Markov model of such a section is produced. Track degradation data from the UK rail network has been analysed to produce degradation distributions which are used to define transition rates within the Markov model. The model considers the changing deterioration rate of the track section following maintenance and is used to analyse the effects of changing the level of track geometry degradation at which maintenance is requested for the section. The results are also used to show the effects of unrevealed levels of degradation. A model such as the one presented can be used to form an integral part of an asset management strategy and maintenance decision making process for railway track.

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.

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 systems reliability approach to decision making in autonomous multi-platform systems operating a phased mission

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.

Investigating railway track asset management using a Markov analysis

A railway track asset management strategy must ensure that the condition of the track stays at an acceptable level, ensuring passenger comfort and safety. The determination of an efficient and effective strategy is a complex problem that requires consideration of the interrelated processes of deterioration, inspection, maintenance and renewal. Railway traffic causes the track’s geometry to deteriorate through time. When the track’s geometry has been assessed and seen to have deteriorated, maintenance is scheduled to restore the track’s geometry using a tamping machine. Tamping involves lifting the track to the required position before inserting vibrating tines into the ballast either side of the sleepers and using them to pack the ballast below. This process causes damage to the ballast, causing a relative acceleration in future track geometry deterioration and decreasing the time between subsequent maintenance interventions. This paper describes a Markov model that can be used to investigate the asset management strategy applied to a railway track section. The model predicts the way that the track section’s condition changes with time for a given asset management strategy, which is defined through the specification of a number of model parameters. The model is applied to a track section with a specified maintenance strategy and is used to investigate the track’s performance for that strategy. Parameters corresponding to asset management decisions that relate to inspection, maintenance and renewal are then changed in order to illustrate the effects of the decisions on the track’s life cycle. The Markov model provides a simple yet powerful means of investigating the effects of an asset management strategy on a railway track section.

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.

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.