A critical review of Information Assurance (IA) framework for condition-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 maintenance decisions. 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 and information used to make maintenance decisions. The paper considers work on data processing and decisionmaking in CBM. The results show condition monitoring suffers from an inability to determine exact positioning on the track; some data are inaccurate or unavailable. Existing studies have not adequately dealt with data content or 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 difficulty understanding degradation models, and data problems mean it is difficult to make good decisions. There is a lack of long term maintenance plans. Models also need to be integrated for more realistic but not necessarily optimum solutions and to ensure practical predictions of maintenance. Some models focus on degradation, others consider prediction, and still others calculate the maintenance cost; it is difficult to combine these. Overall, data are inaccurate, there is no testing phase using realistic data, and existing models are insufficient. This has a negative impact on maintenance decisions.