# A Maturity Model Proposal for Industrial Maintenance and Its Application to the Railway Sector
## 5 Discussion: Railway Infrastructure Maintenance
The proposal presented in [56] presents a solution based on machine learning classifier models for the diagnosis of irregularities such as track geometry. On the other hand, ref. [57] presents a review of the characteristics of IMUs for their concrete use in the monitoring of track geometry. Methods can also be found that deal with different problems with the data obtained, such as the need to align large amounts of track geometry data despite measurement errors and/or missing data [58]. With these approaches, new options for track geometry monitoring would be available, and it should be noted that CBM (together with proper, digitalized historical data collection and the use of approaches such as those mentioned above to address data quality issues) is an important step before progressing towards the maturity model. However, the objective is to determine what the appropriate frequency would be. To get closer to that goal, as proposed in M3 and the innovations proposed by SUSTRAIL IMs, new methods for degradation prediction (status prognosis) should be used. In the literature, one can find proposals such as those launched by [59] for predicting track geometry that have been tested on United States railway networks. There are also approaches such as those shown in [60] that test an artificial neural network model for track geometry prediction on Swedish railway networks. Among the parameters studied by the model, the standard deviation of longitudinal level is the one that determines the tamping planning, one of the most applied maintenance activities [61; 62]. Hence, the maintenance procedure (tamping) is based on the prediction of the asset status (standard deviation of longitudinal level). Considering the guidelines set by M3, the next level of maturity to acquire would be prescriptive maintenance. For this, it would be necessary to comply with the previous points, to obtain prediction of the state of the asset and thus be able to optimize maintenance procedures based on variables such as safety or cost. For example, ref. [63] shows a proposal to determine appropriate track geometry limits to minimize annual maintenance costs. There are also works such as [64], which presents a new approach for optimizing maintenance intervals, an innovation objective set by SUSTRAIL. This proposal is based on optimization based on costs, costs due to service unavailability and safety costs, using the value of avoided mortality to weigh the importance of safety. It would be interesting to be able to apply solutions such as the one proposed in [65] to support IMs in making maintenance decisions in terms of reliability, safety, availability and cost. The track geometry analysis in this section is only one of the failure modes identified by participants in SUSTRAIL project. Other potential failure modes of concern to the IMs are rail rolling contact fatigue (RCF), erosion and slip-on embankments, rock-falls, switch rail wear, rail joint failure and missing or worn rail pads. The proposed M3 maturity model can systematically be applied to the different potential assets and failure modes and is identified as future work. Many of the solutions presented in the literature focus on the results of the developed system. They generally provide theoretical information on the solution, and define in a generalist way its implementation in practical cases or experimental pilots. One of the future works detected is the need to make visible the challenges that occur when applying a solution in production. In most cases, the proposed solution must be readjusted to real-life needs, so that the performance and functionality of the proposed solution meet the objective set. Another future work that has been detected is the need to use tools to prevail in the integration and interoperability between the different designed solutions. In this way, the interaction between the different modules implemented can be simplified.