# Assisted-Driven Design of Customized Maintenance Plans for Industrial Plants
## 3 Application: Results and Discussion
### Results Analysis  
A conductive RCM model has been developed in such a way that it assists the user responsible for the customized design of maintenance plans for an industrial plant, thus enabling three major advantages: the first is the considerable time savings involved in carrying out a Failure Modes, Effects and Criticalities Analysis (FMECA); the second is the minimization of human error in the treatment of an extensive series of historical cases; and the third advantage is that the AIRCM application created for this purpose is used as training for the inexperienced user of maintenance, where he can be totally guided in the process, accessing all the historical cases stored and successfully resolved. In this way, he has an AIRCM application that can be used as a training system through case simulation, providing a didactic component. Once the AIRCM application has been validated, different input cases are entered and the corresponding solution is obtained. The advantages and benefits of the proposed conductive RCM model have been successfully tested by proposing a series of new cases in the EW section, where there is a database of successful cases of Reliability-Centered Maintenance (RCM) programs already implemented. The new cases have enabled the simulation in the AIRCM application environment, obtaining the results shown in Table 3. The total number of simulated cases for each of the input variables {INSTALLATION, EQUIPMENT and FUNCTIONAL FAILURE} is shown with X.  
Figure 11: Case Base in the RCM_EW.CSV File with the new Case ID = 36 REGISTERED. It should be noted that the robustness of the proposed conductive RCM model has been verified, leaving some values corresponding to the input variables empty, as evidenced in Table 3 for simulations 1 to 3, where only one input variable has been reported, for simulations 4 to 6, reporting two input variables, and for simulation 7, reporting all the input variables. For all the simulations carried out, it has been observed that the retrieved cases most similar to the problem posed are correctly displayed as results, even with a lack of input information, thus concluding the validation process. In the same way, the goodness of the system has been verified, introducing some input cases as a query to the problem. This only informs the FUNCTIONAL FAILURE (FF) input field in the query, approximately as what would be done in a real case, leaving the rest of the fields--EQUIPMENT, INSTALLATION and SECTION--empty, without input information. That is, it is about describing the problem of an existing case by means of a similar description to observe the response of the system. Table 4 highlights this type of simulation to show the results, such as similar recovered cases, concluding that there is a good system response. The use of the application developed in a maintenance design process in an industrial plant in operation has allowed us to observe the following improvements in the results. Empirically, a notable reduction in the time devoted to applying the Reliability-Centered Maintenance (RCM) methodology has been achieved. Considering that a case like the one presented in this work requires a series of meetings of the RCM group created expressly, in addition to a documentary analysis of the specifications of the assets subject to maintenance and their operational context, the development of the Mode Analysis process of Failure, Effects and Criticalities (FMECA) and the correct application of the Reliability-Centered Maintenance (RCM) flowchart, a reduction of the four hours dedicated to the classic RCM process by the person responsible for the maintenance design is obtained--only thirty minutes is required to use the conductive RCM process through the AIRCM application developed and used in this work. This does not account for the time that has had to be devoted by the members of the RCM group in the different meetings organized.