# A Maturity Model Proposal for Industrial Maintenance and Its Application to the Railway Sector
## 4 M3: Maintenance Maturity Model
### Status  
In the status area, the first activity to be performed is the definition of the failure modes of the inventoried asset. The failure modes of an asset have an impact on its safety and reliability. They need to be rigorously defined. This implies carrying out a study of the key performance indicators (KPIs) required for the assessment of the different failure modes.  
When selecting the most critical failure modes and KPIs, methods such as FMECA (Failure Modes, Effects and Criticality Analysis) are often used [47; 48; 49]. This is a systematic analysis of potential failure modes of critical assets. It usually includes the identification of failure modes, their possible causes and the risk analysis associated with their effects and consequences. It also analyses the possible corrective and preventive actions to be taken that have an impact on the failure mode.  
Figure 4: M3: Maintenance Maturity Model.  
This analysis focuses on three parameters: Failure Severity (S), Failure Occurrence (O) and Failure Detection (O) in a range from 0 to 10. FMECA provides a quantitative way to calculate the Risk Priority Number (RPN).  
\[\text{RPN}=\text{S}*\text{O}*\text{D} \tag{1}\]  
The analysis performed results in a proposal of an innovation to minimize the severity of the failure, reduce the occurrence of the failure or improve the detection of the failure. The next step is based on quantifying the cost (C) of this innovation. The result obtained is identified as RCPI (Risk Cost Priority Index).  
\[\text{RCPI}=\text{S}*\text{O}*\text{D}*\text{C} \tag{2}\]  
This type of analysis helps to benchmark and determine what improvements should be made in terms of reliability, availability, maintainability and safety. FMECA provides identification of vulnerabilities and potential failures by capturing accurate information and engineering knowledge.  
In this same area, reference is also made to the different attributes for achieving a better assessment of these failure modes and the detection of defects. For the assessment of the condition of the asset, at the most basic level of maintenance (preventive), there are discrete inspections performed with a defined frequency. If the level of criticality of the asset is increased, more intelligent forms of inspection are used, such as continuous monitoring (e.g., enabled by IoT technologies). By means of different sensors, more continuous measurements can be obtained over time. Sometimes these measurements do not give direct results of the evaluation of the defined KPIs, therefore, indirect diagnostic algorithms must be developed. For the predictive and prescriptive maintenance levels, it would only remain to include asset health prognostics practices. This can be addressed using techniques based on AI (Artificial Intelligence) technologies. Through these attributes, improvements in the availability and profitability of the managed assets are achieved. With the support of data acquisition, analysis, physics-based modelling and uncertainty calculation, among other things, it is possible to assess the health condition of an asset.