# A Maturity Model Proposal for Industrial Maintenance and Its Application to the Railway Sector
## 5 Discussion: Railway Infrastructure Maintenance  
At European level, maintenance costs for rail infrastructure and vehicles are esti-mated at more than EUR 20 billion per year. The distribution of maintenance costs varies between countries and organizations, but according to [50], on the infrastructure side, track costs represent between 40 and 70% (where rail defects account for 30%) of the above values respectively. The above figures are similar for all infrastructure managers (IMs) and train operating companies, totaling more than 330 in the EU and 1500 organizations worldwide, and accounting for more than EUR 8 billion of recurrent costs in the EU. RAMS (Reliability, Availability, Maintainability, Safety) analysis is a well-known tool for creating safe and robust systems. Its practices and methods are described in the standard [51]. Although traditionally used with safety-related electronic systems, the use of RAMS has not been very popular when supporting the management of mechanical systems, such as railway infrastructure until recently [52]. The SUBTRAIL project [53] has conducted an FMECA analysis resulting in a ranking of the different identified failure modes suffered by railway infrastructure [54]. This analysis is an example of how to identify critical assets and their fault modes and KPIs. This is a first step in M3 before going forward with the definition of the maintenance strategy. Amongst it, it is identified that one potential failure mode for the track is poor track geometry. This can be caused by deterioration of components and general geometry degradation due to traffic. The analysis defines the potential effects of this failure mode to be premature component failure, poor performance, high maintenance and derailment. The IMs involved in the project decided to adopt several innovations to deal with defective track geometry. Specifically, these are the IMs Network Rails (UK) and ADIF (Spain). To minimize the severity of the failure, they proposed to improve the quality and maintenance of the installation and fix it before it reached unacceptable levels. To reduce the occurrence of such a failure mode, they suggested specific geo-grids and under-sleeper pads. Finally, to improve the detection of faulty track geometry, they proposed monitoring the geometry at the appropriate frequency by optimized maintenance scheduling using new methods for degradation prediction. IMs are guided by common standards such as EN 13848. This standard defines the acceptance criteria for track geometry. It contains several parts defining design and maintenance requirements. Part five of this standard gives a detailed description of what the acceptance levels are for four different parameters for track geometry: defects on gauge, level, alignment and twist. For each of them, the standard defines certain limits that determine alertness, the need for intervention or the need for immediate action. However, this standard does not determine how often you should inspect and evaluate the parameters to be considered. Nowadays, the railway sector makes use of very traditional preventive maintenance strategies, which are mainly based on periodic inspections and on the experience/knowledge of the maintenance staff [55]. Hence, IMs mainly have a preventive maintenance maturity level for the track geometry maintenance: discrete inspection activities are carried out at a frequency dictated by preventive procedures. These inspections can be either visual or carried out by an instrumented inspection vehicle. Maintenance activities, such as tamping, are also governed by preventive procedures. This takes into account the management of the necessary resources, e.g., tamping machine, driver and operators. There are also some other examples showing a higher maturity level for the same asset and failure mode (track geometry). As determined by the M3 maturity model, the next condition-based maturity model implies more frequent track monitoring. There are several examples dealing with both track monitoring and fault diagnosis in the literature.