## 5 Proposed Approach
### Module for the Categorization of Maintenance Alerts  
In this module, we want to classify the notices according to certain classes and using words that we need to define. These classes, which we will call \"Fault Categories\", can represent a failure phenomenon or a maintenance activity. Classifying the notice according to these \"Fault Categories\" can help understand which failure phenomena or maintenance activities are more frequent for each component type. Additionally, this information may be used as a predictive variable. First, the words contained in the \"Description\" were analyzed to identify which ones were the most recurrent overall and grouping components in categories. We used a list of \"stop words\" consisting of words that are not likely to provide information about the faults such as articles, prepositions, and conjunctions. When breaking down the \"Description\" in tokens, if a token was a stop word, we simply removed it. Besides common stop words like those mentioned above, other stop words have been defined in collaboration with ITALTELECO. When we considered all the components independently, the following steps were followed:  
1. Only the Descriptions that in the technical site have at least ROOT-NodetypeA are considered;
2. Each Description is broken down into tokens;
3. The obtained tokens are inserted into a list;
4. The output is represented by the frequency value of each word. The percentage of occurrence of each word was calculated using the formula: \[\mathit{presencepercentage}=(\mathit{numberofoccurencesofthesingleword*100})/( \mathit{totalnumberofwords})\] When considering the components grouped by typology, the following steps were carried out: 1. The \"Notices\" data were divided into several subgroups, each related to the type of components (which is basically the NodetypeA element present in the technical site);
2. For each subgroup of notices, points (a) to (e), defined in the previous case, were performed, obtaining the frequency value of each word (which this time is referred to each type of component). The obtained results were shown to the ITALTELECO company, which took care of manually choosing the words of interest by identifying 19 \"fault categories\" which are depicted in Table 8. These words partly represent failure phenomena, such as vibrations or leaks, or maintenance actions, such as cleaning, adjustment, and replacement. Each original word that we have provided has been mapped to one of the 19 categories. If a category includes more words, it means that they are all related or have the same meaning or are synonyms. They represent the final categories used to group the alerts. Clustering was performed to group together similar words or verbs in different forms. For example, words such as \"cleaned\", \"clean\", and \"cleaning\" are all mapped to the same category CLEANING. Once we had this list of 19 fault categories, we had to associate each alert with one of them. Therefore, this further categorization process that has been carried out dealt with checking each \"Description\" field to identify which fault class it contained. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Number of Notice** & **Description** & **Old Technical Site** & **New Technical Site** \\ \hline
10,336,724 & RT2 PCV7C DISASSEMBLY REGULATORS & RT2-VALVR & RT2-VALVR-PRESS-RT2 PV7 \\ \hline
10,162,193 & RT2 FRC22 function check (T1 column) & RT2-SPORT-FE & RT2-SPORT-FE-R2 FE22 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Example of reconstruction data applying the fifth methodology. At this point, we processed the \"Notices\" data by creating a dictionary having as keys the categories and as values the words present in the description corresponding to the categories (synonyms, verbal forms, plurals). This dictionary was created in collaboration with the company. Once we defined the fault categories, the categorization process involved the following points:  
1. Creating a new column in the \"Notice\" data called \"Fault Category\" initially empty;
2.