## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sector** & **Citation Index** & **Trends** & **Challenges** & **Future Directions** \\ \hline  & & Adoption of LSTM & Sensor noise, missing & Improve data quality, \\  & [42] & models and Grafana & data, model complexity, & integrate hybrid models, \\  & dashboards for & and lack of transformer & and explore edge \\  & time-based maintenance & model exploration & computing solutions \\  & insights & & & \\ \hline  & & Two-phase detection & Small and narrow & Expand datasets, add \\  & and classification of & dataset, reliance on one & more sensors, and use \\  & motor faults using & type of sensor, & digital twins for model \\  & vibration data and SVM & inconsistent fault & training \\ \hline  & & & \\  & [44] & Shift from vibration to & Imbalanced data, no & Use real-time data, \\  & oil analysis with ML for & real-time monitoring, & combine methods like \\  & fault detection & and limited model & vibration and oil, \\  & & diversity & explore deeper models \\ \hline  & & Real-time motor & Small dataset, limited & Add more sensor types, \\  & monitoring using IoT & sensor variety, and & test on larger systems, \\  & sensors and ML & communication delays & and assess financial \\  & & when scaling & feasibility \\ \hline  & & Use of ensemble and DL & Small dataset, missing & Expand datasets, \\  & models to analyze & real-world validation, & improve signal \\  & vibration data for & and no advanced feature & processing, and test in \\  & bearing faults & extraction & real-time factory \\  & & brain functions & environments \\ \hline  & & Al-driven modeling of & & Validate in real plants, \\  & machining force and & Hard to generalize, & add real-time \\  & tool wear under & ANN tuning issues, and & monitoring, and test \\  & different lubrication & limited to lab conditions & more learning \\  & methods & & & techniques \\ \hline  & & Cloud-based ML and & & Improve real-time input, \\  & sensor integration for & Single-site data, manual & expand model range, \\  & predicting mining & data entry, and lack of & & and assess economic \\  & equipment faults & DL models & feasibility \\ \hline  & & Multi-sensor fusion and & Controlled test setting, & Test in diverse plants, \\  & data preprocessing to & no DL models, and lack & explore advanced \\  & improve motor & of real-world & algorithms, and \\  & condition classification & deployment & evaluate cost-benefit \\ \hline  & & Semi-automated & & Use richer datasets, \\  & diagnostics with & Imbalanced data, & refine features, and \\  & frequency domain & limited feature methods, & explore real-time \\  & vibration analysis and & and no external factor & economic \\  & ensemble models & handling & implementation \\ \hline  & & Rise of hybrid DL & High model complexity, & Test in real factories, \\  & & models for accurate & limited real-world & reduce computing \\  & [51] & fault detection; growing & testing, and narrow fault & demands, and explore \\  & & use of real-time data & coverage from one & models that predict time \\  & & from industrial sensors & dataset & to failure \\ \hline  & & Data-driven decision & Model assumptions & Add live data from \\  & systems & making combining ML & limit real-time use, & sensors, improve speed \\  & & and optimization; use of & optimization is slow for & for large setups, and \\  & & smart scheduling to & big systems, and lacks & study financial impact \\  & & reduce costs & detailed cost info & across industries \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._