## 3 Prediction Hybrid Method
### Technical State Prediction Hybrid Model
#### 3.1.1 Stage I--Training a Predictive Model Based on Data Collected during Reviews in Level 2  
In Stage I, based on measurement data obtained during the technical review at Level 2, as well as the rules for identifying the reasons for technical object replacement and the current technical condition, a set of input (Table 2) and output data was developed to teach predictive models (and thus a hybrid model). The result of Stage I is the state S = {1, 2, 3}, the value obtained during the prediction of the technical condition for the hybrid model. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Input Data Structure Number**} \\ \cline{2-4}  & **1** & **2** & **3** \\ \hline The number of input data \(\text{s}_{\text{in}}\) & 14 & 12 & 10 \\
1 & Review number & X & X & X \\
2 & New measuring cycle & X & X & X \\
3 & The number of days since the exchange & X & X & X \\
4 & Quarter of the year & X & X & X \\
5 & Average temperature in the month (\({}^{\circ}\)C) & X & X & \\
6 & Average wind speed for the month (km/h) & X & X & \\
7 & Total rainfall for the month (mm) & X & X & \\
8 & Pantograph type & X & X & X \\
9 & Front/rear pantograph & X & X & X \\
10 & The difference in thickness of the strip N1 between inspections & X & X & X \\
11 & The difference in thickness of the strip N2 between inspections & X & X & X \\
12 & Sliding strip thickness N1 & X & & \\
13 & Sliding strip thickness N2 & X & & \\
14 & Reason for replacement during the previous measurement & X & X & \\
15 & Earlier technical condition & & & X \\
16 & Reason for replacement & & & X \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Input data for the prediction model. For the technical review, the following levels were determined:  
* Level 1, short interval reviews, about 2-3 days;
* Level 2, medium interval reviews, about one week;
* Level 3 and more, long-term reviews, over one month are not important for this method. In Figure 6, the training process of a predictive model is presented. The developed training data were used in the hybrid model to predict the technical state in the form of \(S\) variable. 1.2 Stage II--Prediction of the Technical State of a Technical Object Using the Developed Hybrid Model for Reviews at Level 2  
In the process of predicting the technical state of the technical object, the same hybrid model is used as in Stage I. In Stage II, work begins with data that were analyzed and imputed during the review at Level 2. A total of about 750 service cards from one of the main polish rail carriers with over 1500 measurements of current collectors made over 2 years were analyzed. The measurements concerned 62 locomotives, types EP09 and EU07. The data were preprocessed; the paper service cards were transformed to digital and the reason for the changing pantograph parts was presented in previous research [38]. These cards show that during the P2/P3 maintenance (at the P2/P3 \"maintenance level\" according to DSU), 8.3% of the current collectors were replaced in full (127 cases), while in 273 cases, the sliding block was replaced, which accounts for as much as 17.8% of all cases. The analysis of the collected data shows that in the enterprise, the most serviced were vehicles with AKP-4E collectors (56.6%), and vehicles with 5 ZL collectors constituted 29.9%. It follows that 86.5% of the serviced current collectors were four-arm collectors and only 13.5% were single-arm DSA 150 collectors. The input data structure is presented below. Table 3 lists 12 predictive models.