## 5 Proposed Approach  
The architecture of the approach we propose is depicted in Figure 1 and consists of four main modules:  
* Data reconstruction module;
* Module for categorization of maintenance alerts;
* Data preparation module;
* Prediction module.  
As already mentioned, the provided data are characterized by a huge amount of unstructured data lacking the most important information, namely the name of the component that has failed.  
For the first module, it is necessary to define support data that is useful for the reconstruction step. For such a purpose, we have used the genealogy of the plant components, where the plant name will represent the root, the type of the components will be internal nodes named NodetypeA, and the component names will be internal nodes named NodetypeB. Sometimes, a NodetypeB can have children that are considered leave elements. This last element specifies the part of the component. To build the link between the type of component and the respective component of the plant, we start with the names of the components, and we will create special \"dictionaries\". A dictionary is a structure that contains key-value pairs. Three dictionaries will be defined to properly handle the structure of the components, as discussed in Section 5.1. The link between the key and the value will allow us to walk through the family tree to reconstruct the unstructured data and identify the location of the fault.  
The first module will be described in Section 5.1, in which the data reconstruction techniques that we have employed will be shown. In particular, we are interested in reconstructing the \"technical site\", which has the task of indicating which component has failed. For us, these data are important, because for each component, we are interested in extracting the time series of the related faults and using it in the forecast step. After  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **Components** \\ \hline FURNACES & RT2F1A, RT2F1B \\ \hline COMPR & RT2 MC1A, RT2 MC1B \\ \hline COLON & L1T3, L1T4, RT2T1, RT2T2A, RT2T2B, RT2T2C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of three component classes and the elements they include.  
Figure 1: Flow chart of the four main modules.  
reconstructing the data, we will proceed with the categorization of the alerts. This module will be described in Section 5.2. The categorization of the maintenance notices can be done by attributing to each notice a word deemed important, which may represent a fault phenomenon or a maintenance activity. This process may be important for future work on the prediction of faults. We may be interested not only in predicting the date of the failure but also the category of the component. This may provide useful information, such as the cause of the failure or suggesting what activities should be carried out following a failure and how much it could cost. As the last step, the total data will be reorganized by dividing the information of each component and sorting them in chronological order. In Section 5.3, the module dealing with the final data structure obtained through the previous steps and the reordering of the alerts will be shown.  
Finally, in Section 5.4, the data pre-processing module to generate the input for the predictions will be shown, and the self-learning models used will be described. In this phase, the forecasts of the next failure will be calculated for all system components that have a sufficient number of historical data.