# RETRACTED: Using Data Mining Methods for Predicting Sequential Maintenance Activities
## 5 Conclusions and Perspectives  
In this work, an attempt was made to suggest a list of sequential maintenance activities based on the historical records of maintenance data. Our sequential mining approach gave a better way of analyzing the sequences compared to the traditional statistical analyses used, particularly when the volume of the maintenance data is large. Generally, maintenance managers suggest maintenance activities on a rule of thumb basis or as derived from their experience and they physically control the equipment at regular intervals. The suggested method tries to avoid these manual efforts. Thus, one can generate a longer sequence which covers a higher number of maintenance activities \begin{table}
\begin{tabular}{c c c} \hline \hline
**Maintenance Activity** & **Spare Parts Group** & **Support (\%)** \\ \hline  & S33, S37, S39 & 57.14 \\  & S31, S33, S34 & 56.28 \\  & S33, S34, S38, S39 & 55.42 * \\  & S33, S34, S35, S39 & 53.42 \\ IDLER MAINTENANCE M03 & S31, S33, S34, S39 & 52.28 \\  & S35, S36, S37, S38 & 52.00 \\  & S31, S33, S34, S37 & 49.71 \\  & S31, S33, S34, S36, \(\ldots\), S39 & 46.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Selecting the spare parts group with a 50% support. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**S1. No.** & **Rules** & **\(\mathbf{\eta}_{\textit{cores}}\)** & **\(\mathbf{\eta}_{\textit{correct}}\)** & **Coverage (\%)** & **Accuracy (\%)** \\ \hline
1 & M05\(\rightarrow\)M06\(\rightarrow\)M03\(\Rightarrow\)M02 & 92 & 88 & 40.10 & 95.65 \\
2 & M05\(\rightarrow\)M06\(\Rightarrow\)M03\(\rightarrow\)M02 & 92 & 82 & 40.10 & 89.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The result of the classification. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Maintenance ID** & **Type of Maintenance** & \multicolumn{5}{c}{**Sparse Parts Information**} \\ \hline
2 & _M03_ & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 \\
3 & _M03_ & 1 & 1 & 0 & 0 & 1 & 1 & 1 & 1 & 0 \\
4 & _M03_ & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\
5 & _M03_ & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 0 \\
6 & _M03_ & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \(\ldots\) & _M03_ & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\
350 & _M03_ & 1 & 0 & 1 & 1 & 1 & 0 & 0 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The spare parts information for M03. based on the threshold value. Next, the frequent spare parts group is mapped to the maintenance activity of the generated sequence based on the threshold support values set by the decision-makers. If the managers want to cover a higher number of spares, they can reduce the threshold values. This helps the maintenance managers carry these spare parts directly to the maintenance location and the number of trips from the stores to the maintenance location will be reduced, thereby decreasing the transport costs. Finally, the proposed approach for suggesting maintenance activities is dynamic in nature as it is directly connected to the maintenance database. The generated sequential patterns may change periodically depending on the actual changes or personalizations to the maintenance activities. The developed model analyses the past preventive maintenance records of a given piece of equipment and tries to determine the sequential activities of different maintenance activities, including the time stamp information. In addition, the timestamp information can be used to prioritize a maintenance activity that has been ignored in a particular piece of equipment. In future studies, one can try to weigh the time intervals to determine the sequences. Our model can be extended for analyzing the failure maintenance activities and to perform root cause analyses which can give suggestions that are more valuable to maintenance managers in order for them to take corrective actions prior to the next occurrence of a failure. The authors of this article are specialists in the field of industrial engineering and more precisely in the preventive & corrective maintenance of systems and in the supervision of discrete event systems. \"Conceptualization, S.R. and Z.A. and N.R.