Information assurance for maintenance of railway track

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.Godkänd; 2016; 20160509 (yamald); Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Yamur K. Al-Douri Ämne: Drift och underhållsteknik /Operation and Maintenance Engineering Uppsats: Information Assurance for Maintenance of Railway Track Examinator: Professor Uday Kumar, Institutionen för samhällsbyggnad och naturresurser, Avdelning: Drift, underhåll och akustik, Luleå tekniska universitet. Diskutant: Dr Rikard Granström, Trafikverket, Luleå. Tid: Fredag 10 juni, 2016 kl 10.00 Plats: F1031, Luleå tekniska universitet</p

A critical review of Information Assurance (IA) framework forcondition-based maintenance of railway tracks

Railway maintenance is faced with increasing demands, including the need to improve service.Data measuring the track state and suitable models or applications are needed to make good maintenancedecisions. This critical review paper investigates many research papers on the use of information assurance (IA)within condition-based maintenance (CBM) on a railway track. An IA framework sheds light on the data andinformation used to make maintenance decisions. The paper considers work on data processing and decisionmakingin CBM. The results show condition monitoring suffers from an inability to determine exact positioningon the track; some data are inaccurate or unavailable. Existing studies have not adequately dealt with data contentor the various technologies used. They focus on integrity, availability, authentication, authorisation and accuracy,but do not consider other IA principles important to understand data.CBMmodels and algorithms have difficultyunderstanding degradation models, and data problems mean it is difficult to make good decisions. There is alack of long term maintenance plans. Models also need to be integrated for more realistic but not necessarilyoptimum solutions and to ensure practical predictions of maintenance. Some models focus on degradation, othersconsider prediction, and still others calculate the maintenance cost; it is difficult to combine these. Overall, dataare inaccurate, there is no testing phase using realistic data, and existing models are insufficient. This has anegative impact on maintenance decisions

Time Series Forecasting using a Two-level Multi-objective Genetic Algorithm : A case study of cost data for tunnel fans

The aim of this study is to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). The first level is for the process of forecasting time series cost data, while the second level evaluates the forecasting. The first level implements either a multi-objective GA based on the ARIMA model or based on the dynamic regression model. The second level utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with the ARIMA model only. The results show the drawbacks of time series forecasting using the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In the second level, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis.Validerad;2018;Nivå 2;2018-08-14 (inah)</p

Time Series Forecasting using ARIMA Model : A Case Study of Mining Face Drilling Rig

This study implements an Autoregressive Integrated Moving Average (ARIMA) model to forecast total cost of a face drilling rig used in the Swedish mining industry. The ARIMA model shows different forecasting abilities using different values of ARIMA parameters (p, d, q). However, better estimation for the ARIMA parameters is required for accurate forecasting. Artificial intelligence, such as multi objective genetic algorithm based on the ARIMA model, could provide other possibilities for estimating the parameters. Time series forecasting is widely used for production control, production planning, optimizing industrial processes and economic planning. Therefore, the forecasted total cost data of the face drilling rig can be used for life cycle cost analysis to estimate the optimal replacement time of this rig.ISBN för värdpublikation: 978-1-61208-677-4</p