# Case Study of Expected Loss Failure Mode and Effect Analysis Model Based on Maintenance Data
## 2 Research Methodology
### Results  
Based on the maintenance record in Appendix A, the intermediate variables in part b in Table 1 were calculated as shown in Table 3. \(\tau_{D}\), \(\tau_{U}\), \(\lambda_{C}\), and \(\lambda_{S}\) were calculated for Item 5 to Item 8 without a detection sensor, and in the case of \(\lambda_{\text{sens}}\), it was calculated considering the case where a detection sensor was installed. Based on the results in Table 3, the EL-FMEA results are summarized in Table 4. First, we calculated the expected loss when there are detection sensors from Item 1 to 4. In the case of an item with a detection sensor, since it is detected before the system's failure, the loss due to the system's failure is not included, but the cost of the detection sensor is considered. Among the items with detection sensors, Item 1, which has the highest component price and the highest occurrence frequency, showed the highest expected loss. Next, in the case of Item 5 to 8, both RM and CM were performed, and each failure cause is different. In the case of RM, it is found that the expected loss is lower than that of CM, even for the same item, because it is found before system failure. In terms of items in Item 5, 6, and 7, the expected loss is relatively higher than that of Item 8 because it causes system failure and requires frequent repairs during the regular maintenance period. Also, in item 1 to item 3, the expected loss was high despite the presence of a detection sensor in terms of the entire item. In this case, it will be necessary to consider adjusting the sensor's sensitivity or replacing it with highly reliable parts to lower expected losses. Finally, the coefficient of determination for the best alternative (\(m\)) was derived based on the expected loss from items 5 to 8 without a detection sensor. First, the total expected loss from items 5 to 8 is summarized in Table 5. For example, the expected loss for Item 5 is $638, which is the sum of the RM cost ($120) and the CM cost ($5 \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Maintenance** & **No. of** & **No. of** & **No. of** & **Cause** & **Item** & \(n_{C}\) & \(\pi_{C}\) & \(n_{\text{people}}\) & \(\pi_{\text{geop}}\) & \(\pi_{\text{sens}}\) & \(L\) & \(L_{\text{sens}}\) \\
**Type** & _SM_ & _RM_ & _CM_ & **Type** & **Type** & & & & & & & & \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Sensor \\ maintenance \\ \end{tabular} } & 12 & - & - & CA01 & Item01 & 1 & \$75 & - & - & \$500 & - & \$1150 \\  & 4 & - & - & CA02 & Item02 & 2 & \$30 & - & - & \$400 & - & \$440 \\  & 8 & - & - & CA03 & Item03 & 1 & \$40 & - & - & \$450 & - & \$545 \\  & 5 & - & - & CA04 & Item04 & 1 & \$20 & - & - & \$300 & - & \$250 \\  & - & 8 & - & CA05 & Item05 & 1 & \$15 & - & - & - & \$120 & - \\  & - & 6 & - & CA06 & Item06 & 1 & \$18 & - & - & - & \$108 & - \\ Maintenance & - & 7 & - & CA07 & Item07 & 2 & \$10 & - & - & - & \$140 & - \\  & - & 4 & - & CA08 & Item08 & 3 & \$3 & - & - & - & \$36 & - \\  & - & - & 3 & CA09 & Item05 & 1 & \$15 & 1 & \$20 & - & \$518 & - \\  & - & - & 2 & CA10 & Item06 & 1 & \$18 & 2 & \$20 & - & \$442 & - \\  & - & - & 2 & CA11 & Item07 & 2 & \$10 & 2 & \$20 & - & \$471 & - \\  & - & - & 1 & CA12 & Item08 & 3 & \$3 & 1 & \$20 & - & \$134 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the EL-FMEA simulation for case study. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Item**Variable** & \(\tau_{D}\) [\(h\)] & \(\tau_{U}\) [\(h\)] & \(\lambda_{C}\) [\(10^{-4}h^{-1}\)] & \(\lambda_{S}\) [\(10^{-4}h^{-1}\)] & \(\lambda_{\text{sens}}\) [\(10^{-5}h^{-1}\)] \\ \hline Item05 & 1.33 & 8286.67 & 3.218 & 1.207 & 2.011 \\ Item06 & 2.50 & 12,429.50 & 2.413 & 0.804 & 2.011 \\ Item07 & 1.67 & 12,430.33 & 2.815 & 0.804 & 2.011 \\ Item08 & 2.00 & 24,862.00 & 1.609 & 0.402 & 2.011 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Reliability parameter estimation from maintenance record data. sensor is installed to lower the expected loss (\(L\)), the \(L_{\text{sens}}\) was calculated and based on this, \(m\) was derived.