Failures of railway point systems (RPS) often lead to service delays or hazardous situations. A condition monitoring system can be used by railway infrastructure operators to detect the early signs of the deteriorated condition of RPS and prevent failures. This paper presents a methodology for early detection of the changes in the measurements of current drawn by the motor of the point operating equipment (POE) of RPS, which can be used to warn about a possible failure in the system. The proposed methodology uses the One Class Support Vector Machines (OCSVM) classification method with the similarity measure of Edit distance with Real Penalties (ERP). The technique has been developed taking into account specific features of the data of in-field RPS and therefore is able to detect the changes in the measurements of current of the POE with greater accuracy compared to the commonly used threshold-based technique. The data from in-field RPS, which relate to incipient failures of RPS, were used after the deficiencies in the data labelling were removed using expert knowledge. In addition, the possible improvements in the proposed methodology were identified in order for it to be used as an automatic online condition monitoring system.

Switches and crossings (S&C) are critical elements on railway networks. Any failure of S&C usually leads to train delays and cancelations having a negative impact on the quality of service delivered, railway safety and also operating costs. S&C is a multi-component system and to enable proactive prevention of S&C unit faults, with their undesirable consequences, the ability to predict problems at component level is needed. This paper describes the derivation of lifetime distributions of individual S&C components based on field data collected. Maintenance was identified as a potential contributor towards increased frequency of reported S&C faults. The outcomes of the analysis and lifetime distributions for the elements of the switch units provide a means of predicting the expected number of maintenance activities, and associated costs for these units over any specified period of time. They provide an essential input to the optimisation of maintenance in developing an asset management strategy.

A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. AbstractManagement of a large portfolio of infrastructure assets is a complex and demanding task for transport agencies. Although extensive research has been conducted on probabilistic models for asset management, in particular bridges, focus has been almost exclusively on deterioration modelling. The model being presented in this study tries to reunite a disjointed system by combining deterioration, inspection and maintenance models. A Petri-Net (PN) modelling approach is employed and the resulting model consists of a number of different modules each with its own source of data, calibration methodology and functionality. The modules interconnect providing a robust framework. The interaction between the modules can be used to provide meaningful outputs useful to railway bridge portfolio managers.

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Delivering the railway infrastructure whose functionality is sustainable and uncompromised in terms of safety and availability under ever decreasing budget constraints is a great challenge. The successful accomplishment of this task relies on the effective management of individual assets within a wider whole system perspective. This is a highly complex decision making task where mathematical models are required to enable well informed choices.In this paper a novel modelling framework is proposed for performing the whole system lifecycle cost analysis. The framework is based on two models: railway network performance and costs. Using the former model investigations of the effects of decisions can be carried out for the individual asset and the whole system. A Petri Net modelling technique is used to construct the model which is then analysed by means of Monte Carlo simulations. The infrastructure performance model is then integrated with the cost model to perform the lifecycle cost analysis.A superstructure example is presented to demonstrate the application of the approach. The results show that taking into account interdependencies among the intervention activities greatly influences, not only the performance of the infrastructure, but also its lifecycle costs and thus should be included in the cost analysis. Thus, the proposed models enable more detailed and accurate economic forecast.

The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. AbstractA high demand of oil products on daily basis requires oil processing plants to work with maximum efficiency. Oil, water and gas separation in a three-phase separator is one of the first operations that are performed after crude oil is extracted from an oil well. Failure of the components of the separator introduces the potential hazard of flammable materials being released into the environment. This can escalate to a fire or explosion. Such failures can also cause downtime for the oil processing plant since the separation process is essential to oil production. Fault detection and diagnostics techniques used in the oil and gas industry are typically threshold based alarm techniques. Observing the sensor readings solely allows only a late detection of faults on the separator which is a big deficiency of such a technique, since it causes the oil and gas processing plants to shut down.A fault detection and diagnostics methodology for three-phase separators based on Bayesian Belief Networks (BBN) is presented in this paper. The BBN models the propagation of oil, water and gas through the different sections of the separator and the interactions between component failure modes and process variables, such as level or flow monitored by sensors installed on the separator. The paper will report on the results of the study, when the BBNs are used to detect single and multiple failures, using sensor readings from a simulation model. The results indicated that the fault detection and diagnostics model was able to detect inconsistencies in sensor readings and link them to corresponding failure modes when single or multiple failures were present in the separator.

Stochastic Petri-Nets (PNs) are combined with General-Purpose Graphics Processing Units (GPGPUs) to develop a fast and low cost framework for PN modelling. GPGPUs are composed of many smaller, parallel compute units which has made them ideally suited to highly parallelized computing tasks.Monte Carlo (MC) simulation is used to evaluate the probabilistic performance of the system. The high computational cost of this approach is mitigated through parallelisation. The efficiency of different approaches to parallelization of the problem is evaluated. The developed framework is then used on a PN model example which supports decision-making in the field of infrastructure asset management.The model incorporates deterioration, inspection and maintenance into a complete decision-support tool. The results obtained show that this method allows the combination of complex PN modelling with rapid computation in a desktop computer.

A novel approach to comparing bridge deterioration rates under different environmental conditions is employed using a network analysis approach. This approach uses a matrix condition scoring system utilised by Network Rail (NR). It does not require any conversion factors which can introduce subjectivity. A number of different factors were analysed to ascertain if they have an effect on bridge deterioration. The key factors were identified and their deterioration profiles incorporated into a probabilistic Petri-Net (PN) model, calibrated with historical data. From these, comparative model outputs pinpointing which factors affect bridge deterioration the most can be computed. Finally, simulations were carried out on the PN model to evaluate which of the factors would have the most financial effect for a transport agency. This allows a bridge manager to categorize bridges in different deterioration sets allowing the definition of different optimal inspection and maintenance strategies for each set.