# A Maturity Model Proposal for Industrial Maintenance and Its Application to the Railway Sector
## 1 Introduction  
Maintenance costs are one of the main drivers of the operational expenses (OPEX) of companies, and are a very high dependent in the industrial sector [1]. Optimizing the execution of maintenance activities is key to reduce costs while maintaining the required quality standards. This is the reason why industries with higher maintenance costs (and those who are more mature in terms of technology deployment) are the ones that invest more in implementing more advanced and efficient maintenance strategies. [PERSON] describes the path towards more advanced maintenance strategies, from preventing to prescriptive maintenance, reaching a higher level of maturity at each step [2,3]. As shown in Figure 1 starting with the basics of maintenance operations, tactics based on the knowledge and experience of maintenance managers are planned, enabling preventive maintenance. Eventually, with the help of different optimization algorithms, the logistics for better on-time use of resources can be improved. However, this strategy is not efficient as it leads to over-maintenance to guarantee service requirements. The next step toward more efficient operations is the adoption of knowledge-intensive strategies such as condition-based maintenance (CBM). By incorporating status information of a given asset obtained from sensors, early detection of failures and diagnosis of different anomalies can be achieved. There are different approaches to be considered,such as fault detection, the influence of disturbances, modelling errors, and the various uncertainties in today's systems [4; 5; 6]. However, there are still several gaps and challenges to overcome, starting with the incorporation of technologies such as IoT/IIoT (Internet of Things/Industrial Internet of Things) for asset monitoring. Other challenges must also be considered, for example, given the high volume of information to be managed in this type of system, the design of the data storage. It must also be considered that the data stored must be of high quality. Today, determining and enforcing the quality of the information is another challenge to be faced. Once the actual condition of assets is addressed by means of CBM approaches, predictive maintenance adds an additional knowledge layer that is based on anticipated future conditions. To predict different failures affecting a component and the evolution of its condition over time, models that calculate its Remaining Useful Life (RUL), based on physical modelling, can be used. In lack of the necessary quantity or quality of data, data-driven models are not a viable solution. Moreover, they are models that can behave like a black box without providing deterministic information on their behavior. Physics-based models are better for this purpose; however, their high computational cost makes their applicability complex, depending on the use case. Functional alternatives would also be needed to overcome the lack of information, like hybrid models, which are also an alternative to the high computational costs of physical models. Finally, optimal maintenance operations can be enabled by cognitive or prescriptive analytics, allowing us to fully automate processes based on predictive features, formalization of maintenance procedures and AI (Artificial Intelligence). The information on the procedures of the inspection of maintenance activities is necessary for the development of the decision-making model (decision models). Thanks to the advances made in terms of optimization, process definition and impact calculation, more reliable decision models can be obtained. However, there are still challenges to be addressed such as the definition of a detailed methodology that would benefit the development and deployment of prescriptive maintenance strategies. Hence, the implementation of prescriptive analytics is not immediate, and their deployment is usually done gradually, by first addressing CBM-based operations, and moving forward until prescriptive strategies are reached.