# A Fault Analysis Method Based on Association Rule Mining for Distribution Terminal Unit
## 6 Application Example  
A fault analysis model was constructed and inserted into a DTU device. This model was based on the proposed method. It contained rule bases obtained from a historical DTU fault database. Rule bases were updated regularly according to the newly generated DTU fault data. When a fault happened, information of the fault type was sent into this model. The outputs were the analysis results of possible factors and corresponding fault modules in the rule bases. Information of factors was used to guide planned maintenance and information of fault modules was used for fault repairing.  
For comparison, this fault analysis model was incorporated into 1000 DTUs in a certain area of Nanjing in China, while the other 4600 DTUs in the same area remained unchanged. Initial rule bases were formed as depicted in Figure 6. Based on the newly generated fault data, rule bases were updated every week. Additionally, planned maintenance was also scheduled on a weekly basis.  
DTU with a fault analysis model is represented as DTU\({}_{1}\), the other type without the model is marked as DTU\({}_{2}\). From September 2020 to December 2020, fault times and fault  
Figure 7: Strong rules including brand information.  
rate are listed in Table 6. Noted that fault times \(FT\) (Equation (11)) are the average fault times of all devices in each group for 3 months, while fault rate \(FR\) (Equation (12)) refers to fault times per device per hour.  
\[FT=\frac{1}{N}\sum_{n=1}^{N}FT_{n} \tag{11}\]  
\[FR=\frac{1}{Nt}\sum_{n=1}^{N}FT_{n} \tag{12}\]  
where \(N\) is the number of devices, \(FT_{n}\) is the fault times of \(n\)th device throughout 3 months, and \(t\) represents the total counted hours.  
As can be seen from Table 6, the fault times and fault rate of DTU\({}_{1}\) decreased to more than half of DTU\({}_{2}\). This was due to the prevention of risk factors in planned maintenance.  
Mean fault elimination time in terms of different fault types is shown in Figure 8. Fault elimination time is defined as the duration from the fault happening to the fault being eliminated.  
As shown in Figure 8, the mean fault elimination time of DTU\({}_{1}\) reduced to less than half of DTU\({}_{2}\) in terms of all fault types except the telemetering fault. Time reduction was due to the model's guidance for engineers during fault repairing. In some cases, faults could be eliminated by restarting certain modules remotely; engineers no longer needed to be on the scene. This kind of saved time was also reflected in the reduction of mean fault elimination time.  
As for the telemetering fault, the saved time of DTU\({}_{1}\) was not as remarkable as other fault types. After analyzing the DTU fault database, it was found that the telemetering fault is not common in this area. The support value of the telemetering fault was 2.12%, while the support values of other common faults were 26.20% (remote control fault), 49.79% (remote signaling fault), and 21.54% (DTU offline). Therefore, the insufficient case of the telemetering fault limited the improvement of this model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **DTU\({}_{1}\)** & \multicolumn{2}{c}{**DTU\({}_{2}\)**} \\ \hline Fault times & Fault rate & Fault times & Fault rate \\ \hline
34.44 & 0.00159 & 79.45 & 0.03678 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fault times and fault rate of two groups of DTUs.  
Figure 8: Mean fault elimination time of two groups of DTUs.