## 3 Evidential Reasoning Algorithm
### Decomposition Model and Transformer Condition Assessment
#### 3.5.2 Condition Assessment
For this purpose, it is first necessary to determine the intervals corresponding to the five basic grades. Suppose the values are chosen as follows:  
* \(u\) (1) = 0;
* \(u\) (2) = 0.35;
* \(u\) (3) = 0.55;
* \(u\) (4) = 0.85
* \(u\) (5) = 1. By applying (14-28) to the above distributions, the overall condition assessment of the observed object is obtained as follows:  
\[u((ET)_{\text{Seventh/May}})_{\text{min}}=0.9661 \tag{45}\]  
\[u((ET)_{\text{Seventh/May}})_{\text{max}}=0.9921 \tag{46}\]  
\[u((ET)_{\text{Seventh/May}})_{\text{avg}}=0.9791 \tag{47}\]  
with the following being true:  
\[u((ET)_{\text{Seventh/May}})=u((ET)_{\text{Seventh/May}})_{\text{avg}}\pm\Delta u =0.9791\pm 0.013 \tag{48}\]  
Using the final condition assessment and the interval of the final rating, a numerical value and the width of the value interval that represents the overall condition of the observed object can be determined. Such a value can be used to compare the state of different observed objects. It is also possible to represent the final condition rating as a function of time and observe the change of the condition during the period of use and the improvement of the condition rating after maintenance. For all selected time points, the aggregation procedure was carried out, and the results are shown in Table 3 from which the exact moments of data acquisition (time), confidence rating in the individual ratings (Rating 1, Rating 2, etc. ), uncertainty and the overall condition assessment (\(u_{\text{avg}}\), \(u_{\text{min}}\), \(u_{\text{max}}\)) are clearly visible. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \# & **Year** & **Month** & **Rating 1** & **Rating 2** & **Rating 3** & **Rating 4** & **Rating 5** & **Uncertainty** & **Uavg** & **Umin** & **Umax** \\ \hline
1 & First & August & 0.0000 & 0.0000 & 0.0331 & 0.0592 & 0.3420 & 0.5656 & 0.6934 & 0.4106 & 0.9762 \\ \hline
2 & Second & February & 0.0000 & 0.0000 & 0.0304 & 0.0244 & 0.4030 & 0.5422 & 0.7116 & 0.4405 & 0.9827 \\ \hline
3 & Second & August & 0.0000 & 0.0000 & 0.0586 & 0.0000 & 0.3814 & 0.5600 & 0.6936 & 0.4136 & 0.9736 \\ \hline
4 & Third & February & 0.0000 & 0.0000 & 0.0665 & 0.0000 & 0.3503 & 0.5832 & 0.6785 & 0.3869 & 0.9701 \\ \hline
5 & Third & August & 0.0000 & 0.0000 & 0.0486 & 0.0184 & 0.7001 & 0.2329 & 0.8589 & 0.7425 & 0.9754 \\ \hline
6 & Forth & February & 0.0000 & 0.0000 & 0.0059 & 0.0600 & 0.3285 & 0.6056 & 0.6855 & 0.3827 & 0.9884 \\ \hline
7 & Forth & September & 0.0000 & 0.0057 & 0.0643 & 0.0620 & 0.727 & 0.5954 & 0.6604 & 0.3627 & 0.9581 \\ \hline
8 & Fifth & September & 0.0000 & 0.0046 & 0.0000 & 0.0508 & 0.4003 & 0.5443 & 0.7172 & 0.4451 & 0.9894 \\ \hline
9 & Sixth & February & 0.0000 & 0.0025 & 0.0045 & 0.0350 & 0.7651 & 0.1930 & 0.8946 & 0.7981 & 0.9911 \\ \hline
10 & Sixth & May & 0.0000 & 0.0030 & 0.0000 & 0.0211 & 0.7525 & 0.2234 & 0.8832 & 0.7714 & 0.9949 \\ \hline
11 & Sixth & September & 0.0000 & 0.0023 & 0.0006 & 0.0190 & 0.7843 & 0.1938 & 0.8985 & 0.8016 & 0.9954 \\ \hline
12 & Sixth & December & 0.0026 & 0.0000 & 0.0027 & 0.0103 & 0.7724 & 0.2119 & 0.8886 & 0.7827 & 0.9946 \\ \hline
13 & Seventh & May & 0.0019 & 0.0000 & 0.0005 & 0.0385 & 0.9331 & 0.0261 & 0.9791 & 0.9661 & 0.9922 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Power transformer condition assessments at different points in time. The greatest uncertainty is present for the measurements carried out in February of the fourth data year, when, of the input data predicted by the model, only some of the values were available, measured ones (chromatography, oil moisture, thermal imaging of conductors) and inspected ones (boiler, pump conditions, ventilator conditions). Data from the remote data collection system were not available after February of the sixth data year.