## 3 State-of-the-Art Techniques for Predictive Maintenance
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & \
\begin{tabular}{c} **Class of Repair** \\ **and Maintenance** \\ \end{tabular} & **Industry** & **Application** &
\begin{tabular}{c} **Problem Type/** \\ **Approach** \\ \end{tabular} & **Algorithm/Technology** \\ \hline
[109] & - & - & \
\begin{tabular}{c} PdM system by a \\ digital intelligent \\ assistant for \\ industry \\ \end{tabular} & - &
\begin{tabular}{c} NLP and user feedback \\ about the success of \\ maintenance \\ interventions \\ \end{tabular} \\ \hline
[110] & \begin{tabular}{c} \\ \end{tabular} & \
\begin{tabular}{c} Prescriptive \\ maintenance for \\ evaluating failure \\ and quality effects \\ in an international \\ manufacturer of \\ gearboxes and \\ engines for the \\ automotive sector \\ \end{tabular} & &
\begin{tabular}{c} Data management, \\ predictive data analytic \\ toolbox, recommender \\ and decision support \\ dashboard, and \\ semantic-based learning \\ and reasoning \\ \end{tabular} \\ \hline
[111] & \
\begin{tabular}{c} Fabricated metal \\ products \\ \end{tabular} & Chemical industry &
\begin{tabular}{c} The prediction of \\ failure time and \\ probability of a \\ pump \\ \end{tabular} & Classification & Ensembles of SVMs \\ \hline
[112] & \begin{tabular}{c} Electrical \\ equipment \\ \end{tabular} & Manufacturing & \begin{tabular}{c} The prediction of \\ potential \\ equipment failure, \\ expected failure \\ time, and expected \\ repair time and \\ providing the \\ appropriate action \\ for production \\ planning and \\ control in future \\ factories \\ \end{tabular} & \
\begin{tabular}{c} Reinforcement \\ Learning \\ \end{tabular} &
\begin{tabular}{c} RL \\ Digital twin \\ \end{tabular} \\ \hline
[113] & \begin{tabular}{c} Machinery \\ \end{tabular} & Manufacturing & \
\begin{tabular}{c} RUL estimation in \\ a machine park \\ consisting of 100 \\ machines \\ \end{tabular} &
\begin{tabular}{c} Reinforcement \\ Learning \\ \end{tabular} & DRL \\ \hline
[114] & \begin{tabular}{c} Machinery \\ \end{tabular} & Rail transport & \
\begin{tabular}{c} The optimal \\ maintenance \\ strategy jointly \\ incorporates the \\ effect of aging and \\ degradation for \\ locomotive \\ wheelsets \\ \end{tabular} & - &
\begin{tabular}{c} Reliability analysis, \\ sensitivity analysis, and \\ a continuous stochastic \\ process \\ \end{tabular} \\ \hline
[115] & \begin{tabular}{c} Transport \\ equipment \\ \end{tabular} & Aviation & \
\begin{tabular}{c} Discrete-event \\ simulation \\ framework for \\ post-prognostic \\ decision for \\ aircraft \\ maintenance using \\ tire pressure \\ indication system \\ for an Airbus A320 \\ \end{tabular} & - &
\begin{tabular}{c} The technological \\ maturity of an \\ underlying PHM system \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cont. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref. ** & \begin{tabular}{c} **Class of Repair** \\ **and Maintenance** \\ \end{tabular}  & **Industry** & **Application** & \ \begin{tabular}{c} **Problem Type/** \\ **Approach** \\ \end{tabular}  & **Algorithm/Technology** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} [116] \\ [117] \\ [117] \\ [117] \\ [118] \\ [118] \\ [119] \\ [120] \\ [121] \\ [122] \\ [123] \\ [124]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref. ** & \begin{tabular}{c} **Class of Repair** \\ **and Maintenance** \\ \end{tabular}  & **Industry** & **Application** & \ \begin{tabular}{c} **Problem Type/** \\ **Approach** \\ \end{tabular}  & **Algorithm/Technology** \\ \hline \hline \multirow{8}{*}{
\begin{tabular}{c} [125] \\ [125] \\ [126] \\ [127] \\ [127] \\ [127] \\ [128] \\ [128] \\ [128] \\ [128] \\ [128] \\ [128  
The transport equipment category in Table 3 is noted as an essential field with six studies. The other machinery and equipment category is also notable with five works. On the other hand, the categories computers and communication equipment, industrial machinery and equipment, and buildings and other structures have a lower level of interest, with one study each, respectively.