## 3 Results
### Decision Algorithm to Determine a Failure-Free State and a Failure  
We used a decision tree to obtain an algorithm for the classification of states of individual failures [29; 30; 31]. The data were divided into two parts: a training set and a testing set. The training set contained 80% of the data, and the testing set contained 20% of the data. Figure 6 presents an example of a decision tree used to identify-classify a failure: a missing roller at position ID9. For every decision tree, its reliability was calculated based on the number of correctly classified objects (measurements) from the testing data set. The reliability of every decision tree was 100%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Failure** & \multicolumn{5}{c}{**Mutual Differences in Contact Forces (CFs) (N)**} \\
**m200** & **m200** & **m207** & **m208** & **m209** & **m210** & **m211** & **m212** \\ \hline
**m207** & \multicolumn{5}{c}{} \\
**m208** & 44.4 & \multicolumn{5}{c}{} \\
**m209** & 17.5 & \multicolumn{5}{c}{} \\
**m210** & 18.8 & \multicolumn{5}{c}{} \\
**m211** & 74.5 & \multicolumn{5}{c}{} \\
**m212** & 42.0 & \multicolumn{5}{c}{} \\
**TWOMA0** & 9.9 & \multicolumn{5}{c}{} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Mutual differences between contact forces (CFs) measured at the ID7 roller position for a failure-free condition and failures. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Roller Position** & **ID7** & **ID8** & **ID9** & **ID10** & **ID11** & **ID12** \\ \hline
**number of unsatisfactory differences** & 4 & 17 & 8 & 4 & 4 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The number of unsatisfactory measurement differences for the roller position. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Roller Position** & **ID7** & **ID8** & **ID9** & **ID10** & **ID11** & **ID12** \\ \hline not met & ID7 CF\({}_{\text{m}200}\)–ID7 CF\({}_{\text{m}209}\) & ID10 CF\({}_{\text{m}200}\)–ID10 CF\({}_{\text{m}207}\) & ID11 CF\({}_{\text{m}200}\)–ID11 CF\({}_{\text{m}208}\) \\ not met & ID7 CF\({}_{\text{m}200}\)–ID7 CF\({}_{\text{m}210}\) & ID10 CF\({}_{\text{m}207}\)–ID10 CF\({}_{\text{TWOMA0}}\) & ID11 CF\({}_{\text{m}200}\)–ID11 CF\({}_{\text{TWOMA0}}\) \\ not met & ID7 CF\({}_{\text{m}200}\)–ID7 CF\({}_{\text{TWOMA0}}\) & ID10 CF\({}_{\text{m}209}\)–ID10 CF\({}_{\text{TWOMA0}}\) & ID11 CF\({}_{\text{m}210}\)–ID11 CF\({}_{\text{m}212}\) \\ not met & ID7 CF\({}_{\text{m}210}\)–ID7 CF\({}_{\text{TWOMA0}}\) & ID10 CF\({}_{\text{m}209}\)–ID10 CF\({}_{\text{TWOMA0}}\) & ID11 CF\({}_{\text{m}210}\)–ID11 CF\({}_{\text{m}212}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Pairs of states (without a failure and a failure) that cannot be distinguished from each other. The classification criteria for a failure-free condition and a failure in the pipe conveyor operation, using the discrimination method, are listed in Table 10.  
Figure 7 presents the decision algorithm used to identify selected failures in the pipe conveyor operation with the use of the discrimination method as in Table 10. Testing of this algorithm can be found in Section 4, which discusses the decision algorithm's validity and reliability using simulated data.