Information assurance for maintenance of railway track
Abstract: Railway traffic is steadily increasing, having a negative impact on maintenance and resulting in decreased track availability, comfort, and safety. Swedish railway track maintenance mostly focuses on the actual track condition via a nationwide condition-based maintenance (CBM) strategy. For maintenance to be conducted in an appropriate way, data on the actual track condition must be accurate; furthermore, those data need to be converted into accurate information for maintenance decisions. An information assurance (IA) framework has the potential to deal with the system risks from a technical perspective. The framework is a guideline that can be implemented within CBM to understand both condition monitoring data behaviour and the information processing used to reach maintenance decisions.This research investigates ways of an information assurance (IA) framework can be implemented in the following CBM steps: data collecting, data processing and making maintenance decisions on Swedish railway. The framework can be used to understand data behaviour, information processing and the communication between information layers for decisions at organisation, infrastructure and data/information levels. The research uses both qualitative and quantitative methods to investigate critical information data, parameters, and problems and to suggest which areas need improvement. Quantitative analysis of the Swedish track geometry database reveals specific information about the behaviour of the railway data and their processing to make maintenance decisions.A case study shows how certain sections of a railway track are monitored and evaluates maintenance practices on those sections. The study finds several different types of measurements are taken using several different measurement systems. It is difficult to integrate these data for proper processing. In addition, there are problems of incomplete or irregular data; this affects the derivation of information and the use of models to understand track irregularities.Given the problems of data processing and subsequent decision making, the study suggests implementing an IA framework with CBM. The study checks the achievement of three IA principles in the existing data: authenticity, integrity and availability. The results show data have problems of authenticity and integrity, something also mentioned by the stakeholders in interviews. In particular years and on certain track sections, CM data are more than 5 percent incomplete, significantly affecting analysis. Incomplete track measurement data reach as high as 63 percent for the parameters of standard deviation (STD), longitudinal level and STD cooperation. Inaccurate measured values for alignment long wavelength within certain speed limits reach as high as 71 percent. These indicators are important for calculating track quality but are either incomplete or incorrect, negatively affecting the calculation of the Q-value and estimations of the track quality. This, in turn, negatively affects the maintenance decisions. Using information assurance will increase the system performance by permitting stakeholders to make accurate decisions.The suggested information assurance framework can discover technical problems but it needs to be improved using technologies, techniques and services to ensure complete and accurate data are available to be processed for maintenance decisions.
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