## 3 Proposed Approach
### Management Rules Generation  
In order to this study to have higher accuracy, numerous decision methods must be used to forecast unknown data groups, based on ([PERSON] and [PERSON], 2006). Figure 6 shows a diagram related to the above.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Performing** & & & & & & & \\
**Maintenance after an Inspection** & & & & & & & \\ \hline Y & 17\(\sim\)22 \({}^{\circ}\)C & C2 & L4 & 120–180 K & Y & 240–300 kph & Y \\ \hline Y & 17–22 \({}^{\circ}\)C & C1 & L5 & 120–180 K & Y & 240–300 kph & Y \\ \hline Y & 27–32 \({}^{\circ}\)C & C1 & L5 & 180–240 K & N & 240–300 kph & Y \\ \hline N & 27–32 \({}^{\circ}\)C & C2 & L1 & 120–180 K & N & 240–300 kph & Y \\ \hline Y & 22–27 \({}^{\circ}\)C & C2 & L5 & 240–300 K & Y & 240–300 kph & Y \\ \hline N & 17–22 \({}^{\circ}\)C & C2 & L4 & 240–300 K & Y & 240–300 kph & Y \\ \hline \hline \end{tabular}
\end{table}
Table 11: Partial sample set of original data.  
Figure 3: Screenshots of the PolyAnalyst 6.5 decision tree properties settings.  
Figure 4: A screenshot of the decision tree to determine whether maintenance is needed following an BIDS alarm.  
Figure 5: A decision tree to determine whether maintenance is needed following a BIDS alarm.  
This study follows this principle by conducting 10 experiments and counting the number of times each rule occurs.  
For this rule \"If temperature = T1 and speed = S4 and accumulated mileages = M4 or M5, BIDS alarms are activated and maintenance is needed after an inspection\". It appears in five out of ten experiments, so it is labeled \"5 times\". From this figure, we can also obtain the rules and their number of occurrences generated by ten experiments.  
As explained by numerous decisions theory, Figure 7 could be processed \(n\) more times. The most frequent groups among the experiments were groups M3, M4 and M5. This means that these three groups also need to be further studied.  
In this study, there are five categories of accumulated mileage factor, as can be seen from Figure 8. Group M1 represents train sets with the lowest accumulated mileage, while group M5 represents train sets with the highest accumulated mileage. In light of wheel  
Figure 6: Increased model accuracy methodology.  
Figure 7: Ten analysis results of decision tree.  
roundness and accumulated mileage, it can be concluded that M3 and M5 have the best wheel roundness. This concept states that since the roundness of the wheels on M4/M5 is better than the ones on M3, M3 is more likely to send out BIDS alarms. By taking wheel roundness into account, rules of train set with low accumulated mileage already contain rules of train sets with high accumulated mileage.  
With respect to train sets belonging to T1, train sets with accumulated mileages of M4 and M5 each occur five times, but rules of M4 already contain rules of M5; therefore, all can be expressed by M4. With the same concept, accumulated mileage rules of train sets belonging to T1 can all be expressed by M3. The decision tree is thus pruned as illustrated in Figure 9.  
In analyzing Figure 9, it can be seen that temperature is the main factor for BIDS alarms; train speed and accumulated miles are subordinate factors, as the following rules imply:  
1. If temperature = T1 and speed = S4 and accumulated mileages is M4, BIDS alarm is activated and maintenance is needed after an inspection.  
Figure 8: Trainset accumulated mileage factor.  
Figure 9: A decision tree for predict BIDS alarms with or without maintenances.  
2. If temperature = T2 and accumulated mileages is M3, BIDS alarm is activated and maintenance is needed after an inspection.
3. If temperature = T3 and speed = S4 and accumulated mileages is M4, BIDS alarm is activated and maintenance is needed after an inspection.
4. If temperature = T3 and speed = S4, accumulated mileages is M5 and location is L4 or L5, BIDS alarm is activated and maintenance is needed after an inspection.
5. If temperature =T4 or T5, BIDS alarm is activated and maintenance is NOT needed after an inspection.