## 4 Discussion
By examining the fourth research question, which explores challenges and future directions, this review reveals that issues with incomplete or imbalanced data, lack of standardization in data formats, and the high computational overhead of complex models continue to slow the widespread adoption of ML-based PdM. Sectors such as aerospace and automotive have demonstrated encouraging results with distributed or federated learning to reduce the burden of transferring massive amounts of sensor data, but these frameworks require more robust edge or fog computing infrastructure. Additionally, the interpretability of black-box deep networks remains a frequent point of concern for industrial practitioners who need transparent reasoning to justify significant maintenance decisions. Knowledge-driven approaches and explainable AI techniques are therefore emerging as critical trends that could bolster trust in automated PdM systems. Overall, the review indicates that intelligent maintenance systems are a vital enabler of data-centric strategies in Industry 4.0. However, further validation in live industrial settings is necessary to address real-time constraints, deployment costs, and integration with legacy systems. Encouragingly, among the 60 studies surveyed, 40 papers have already taken their models out of the lab and into everyday service. These \"yes\" cases stream live sensor data from pumps, turbines, production lines, and similar assets, raise work orders automatically through the plant's maintenance software, and report tangible payoffs such as shorter downtime or higher overall equipment effectiveness. Four additional papers sit in the \"partial\" category; they analyzed authentic plant data retrospectively but have yet to let their models issue live alerts. The remaining 16 papers are still at the \"no\" stage, evaluated only on test rigs, simulations, or public benchmark datasets. Details of this deployment classification are provided in Supplementary Table S1. A common thread runs through the fully deployed studies and offers a practical reusable roadmap for others. Each deployed study relies on an edge-to-cloud data pipeline that uses open protocols for consistent real-time collection of sensor streams. The predictive models themselves are packaged in containers, enabling hot swaps and automatic retraining whenever data drift is detected. A lightweight explainability layer helps technicians understand and trust the alerts. Finally, the prediction output is wired straight into the plant's computerized maintenance management system so that every alert is converted immediately into an actionable work order. Because this architecture has been demonstrated in wind farms, refineries, hospitals, and discrete part factories alike, it serves as an adaptable framework for moving AI models from controlled tests to reliable real-world deployments. Research should continue to explore hybrid models that fuse domain knowledge with data-driven analytics to mitigate challenges associated with explainability, data quality, and domain adaptation. Such efforts will likely accelerate the deployment of robust PdM frameworks that can adapt to evolving operational demands while remaining economically viable and transparent to stakeholders.