## 4 Discussion  
The findings from this review underscore how ML-driven PdM has expanded across a diverse set of industrial settings, including manufacturing, power generation, buildings, healthcare, and transportation. Addressing the first research question, which investigated the industry applications and latest trends, the analysis shows that many sectors have adopted ensemble models and DL architectures to manage complex time-series data from sensors such as vibration, temperature, and pressure. Within manufacturing, there is a pronounced move from purely rule-based or preventive maintenance protocols to proactive data-rich strategies leveraging AI, while heavy industries, including steel, mining, and petrochemicals, are increasingly incorporating digital twins to enhance decision making. This broadened application scope reveals a pattern in which access to multivariate sensor data, combined with scalable ML frameworks, helps in detecting faults earlier and reducing the risk of unplanned downtime. A key aspect of the second research question highlights that tree-based methods, including RF and GB, dominate a large part of the literature, often due to their interpretability and ease of parameter tuning when dealing with high dimensional signals. DL networks, \begin{table}
\begin{tabular}{c c} \hline
**Dataset** & **Domain** \\ \hline CMAPSS (NASA) & Aerospace \\ NASA turbofan engine degradation & \\ \hline MIMII & General production systems \\ \hline SECOM & Semiconductor manufacturing \\ \hline AI4I 2020 PdM & Cross-industry frameworks \\ \hline Vibration (NASA’s prognostic center) & Rotating equipment \\ \hline \end{tabular}
\end{table} 
Table 2: Open-source datasets by domain. notably LSTM and CNN architectures, are particularly prominent when analyzing noisy or unstructured data such as acoustic signals or multivariate time-series measurements in domains like wind turbines, rotating machinery, and building HVAC systems. The observed performance metrics (accuracy, precision, recall, F1-score) confirm that these advanced models outperform simpler statistical or rule-based approaches in many real-world scenarios. Still, model selection remains context-dependent. Low-sensor environments favor robust ensemble approaches, while data-rich environments use more complex neural networks for greater predictive accuracy. Addressing the third research question on common benchmark datasets, the review underscores the repeated appearance of NASA's CMAPSS, MIMII, SECOM, and other publicly available industrial datasets. These open datasets remain essential for reproducible evaluations, although real-world applications still rely heavily on proprietary data. Data scarcity, confidentiality restrictions, and inconsistent sensor coverage frequently limit the ability to generalize findings across different industrial environments. Building on these observations, one recurring challenge in ML-based PdM systems is the limited diversity of machines and sensor types represented in widely used datasets. Models trained on homogeneous data, such as single-type machinery or mono-sensor inputs, often underperform when deployed in complex industrial settings characterized by varied equipment and environmental conditions. Future research should prioritize the development and utilization of multi-machine multi-sensor datasets, integrating telemetry from pumps, compressors, conveyor belts, robotic actuators, and other asset types across acoustic, vibration, infrared, and temperature sensing modalities. This broader inclusion would enhance generalization and significantly strengthen models' resilience to unforeseen fault patterns. Moreover, data fusion frameworks and federated learning offer promising avenues to achieve this diversity while respecting industrial privacy and data-sharing constraints.