# Assisted-Driven Design of Customized Maintenance Plans for Industrial Plants
## 3 Application: Results and Discussion  
The Case-Based Reasoning (CBR) and Reliability-Centered Maintenance (RCM) methodologies have been integrated with the ultimate objective of obtaining a customized design of maintenance plan adjusted to the real needs of industrial plants and assisting the person in charge of maintenance in its design. This integration of both methodologies, implemented in the Java programming language, results in the obtention of an independent computer application for the driven RCM model called Artificial Intelligence Reliability-Centered Maintenance (AIRCM) and designed for the effect to be applied in any industrial environment.  
In particular, the AIRCM application has been conceived in two parts. The first part implements the FMECA, typical of the RCM method, with the CBR method using the jCOLIBRI environment and the second part, where the maintenance policy has been implemented according to the decisional flow chart in Figure 2, using the operating context of the new case raised. Therefore, the maintenance policy adopted for the recovered case is discarded. This is because the maintenance policy depends more on the operational context where the equipment is located--that is, its effects and not the failure mode--as this is more related to the type of equipment and the maintenance policy with the productive context.  
Every Knowledge-Based System (KBS), such as the CBR method, must have access to and the ability to modify a database where the different historical cases validated as successful are stored. This case base is made up of the worksheets (Figure 1) of FMECA information once the classic RCM method has been successfully applied in a productive environment. These record sheets have been considered as already existing and will suppose that the input information for a record in the database is a base of cases that have been interconnected to the AIRCM application that is the object of this work. Since the data sheets are usually arranged in spreadsheets, it is easy to dump the information and its structure into a new spreadsheet that will make up the case record and therefore the case base that the AIRCM application needs. This case base is a Comma-Separated Values (CSV) file. Figure 4 shows the values of the CSV file in tabular form, each column corresponding to the attributes specified in Table 1.  
Figure 4: Records of the CSV file that make up the Case Base. Attributes (IDP).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attributes** & **Description** & **Typology** \\ \hline IDP & Problem Identifier & Sequential value of cases. Integer \\ \hline S & Machine Tree Section & Alphanumeric code that identifies the section \\ \hline I & Installation inside the machine tree section & Alphanumeric code that identifies the installation \\ \hline E & Equipment within the machine tree installation & Alphanumeric code that identifies the equipment \\ \hline FF & Functional Failure & Description of the failure that makes the partially required \\  & & or total required function. Character string \\ \hline IDS & Solution Identifier & Sequential value of cases. Integer number \\ \hline F & Equipment Required Function & Description of the required function and its nominal \\  & & operating values. Character string \\ \hline \hline \end{tabular}
\end{table}
Table 1: Attributes of the Case Base. CSV Database File Column Headers.  
This section has been divided into three phases, according to chronological order: the preparation of the case base used the design and implementation of the AIRCM application and the use of it to solve a new case raised, enabling the applicability of the driven RCM method through running the AIRCM application.