## 5 Discussion and Future Research Directions
### Integration with Classical Maintenance Management Models
Operators are seen as \"owners\" of the equipment, responsible for routine maintenance and anomaly detection. For this framework to benefit from AI tools, interpretability is critical. Studies such as that by [PERSON] et al. [91], which used DeepSHAP, and that by [PERSON] et al. [83], which visualized feature importance, demonstrate how transparent decision support systems empower operators by clarifying why an alarm is triggered. Similarly, implementations by [PERSON] et al. [137] and [PERSON] et al. [131] emphasize the need for dashboards and user-friendly interfaces that combine operational indicators with AI-generated diagnostics. In TPM contexts, such visualization enhances production-line decision making and enables non-expert operators to engage actively with machine health data. The implementation of advanced monitoring and diagnostic methodologies--such as those reviewed in this paper for bearing failure detection [112; 113], pump cavitation identification [82; 86], and RUL estimation [54; 61]--naturally aligns with the objectives of RCM, CBM, and TPM. RCM benefits from analytics to identify risk \"hot spots\" and justify maintenance prioritization. CBM is enhanced by predictive models capable of anticipating intervention timing based on trends rather than static thresholds. TPM gains value by democratizing access to predictive intelligence, allowing in situ operators to understand and act on health assessments, increasing ownership and engagement. Ultimately, the convergence of the reviewed methods with established maintenance models supports the development of flexible, explainable, and risk-aware systems that improve industrial decision making. However, further efforts are needed in three key areas: (1) standardization of data and process integration, as sensor infrastructure and communication protocols remain fragmented in many industries; (2) assurance of trust and return on investment, particularly in TPM contexts where operator motivation may decline if AI is perceived as a black box; and (3) the development of unified evaluation frameworks and metrics, since measuring impact (e.g., reduced downtime, cost savings, and risk mitigation) requires consistent and transparent indicators across RCM, CBM, and TPM. Future Work--Key Questions (Summary Box)  
To conclude the discussion, we highlight a set of forward-looking questions that emerged from our systematic review and may guide future research in predictive maintenance. These topics address critical gaps in lightweight deployment, model robustness, and operator interaction. * Lightweight transforms: Can approximate wavelet/EMD variants run in real time on edge MCUs? * Concept-drift handling: How often must models be retrained, and which incremental schemes work best in safety-critical plants? * Physics-guided explainability: What level of physical fidelity is sufficient to gain operator trust while keeping models compact? * Federated data sharing: Which privacy-preserving protocols are acceptable for cross-company PdM benchmarks? * Unified ROI metrics: How should downtime, energy, and carbon savings be normalised across industries? * Human-centric UIs: Which visual cues (e.g., causal graphs and SHAP heat maps) most effectively support TPM operators?