## 5 Discussion and Future Research Directions
### Integration with Classical Maintenance Management Models  
The adoption of machine learning, deep learning, and hybrid modelling techniques in industrial maintenance is not isolated. These technologies are being progressively integrated into classical approaches such as Reliability-Centred Maintenance (RCM), Condition-Based Maintenance (CBM), and Total Productive Maintenance (TPM). The methodologies and findings presented throughout this work--particularly those summarized in the tables related to fault diagnosis, signal processing, deep learning, and hybrid approaches--support and extend the application of these established maintenance strategies. RCM focuses maintenance strategies on system and component criticality and associated risk. Traditionally, it relies on failure-mode and effect analysis (FMEA/FMECA) to guide preventive or corrective actions based on the probability and impact of failures. Studies such as those by [PERSON] et al. (2018) and [PERSON] et al. (2019) address early detection of critical failures (e.g., pump cavitation, bearing degradation, and soot build-up in boilers), allowing maintenance to concentrate on components whose failure could lead to major shutdowns or safety hazards. In this way, predictive models strengthen RCM by identifying early signs of failure in critical components, improving the effectiveness of inspections and interventions. Moreover, the use of automatic classification methods (e.g., convolutional neural networks or ensemble algorithms) enables FMEA to incorporate real-time operational data. Hybrid models that combine signal processing and ML, such as those proposed by [PERSON] et al. (2015) and [PERSON] et al. (2018), dynamically estimate failure probabilities instead of assuming static values. This continuous assessment of risk allows the criticality matrix (probability/impact) to be updated according to changing operating conditions and reallocates maintenance resources accordingly. However, since RCM heavily depends on expert validation of failure modes and consequences, scepticism may arise when AI models generate difficult-to-interpret outputs. To address this, explainable AI (XAI) methods such as LIME, SHAP, and DeepSHAP--as demonstrated in works by [PERSON] et al. (2018) and [PERSON] et al. (2018)--can improve transparency and trust between advanced analytics and traditional risk analysis. CBM is based on continuous monitoring of equipment health indicators (e.g., temperature, vibration, and pressure) to trigger maintenance when thresholds are exceeded. Historically, these thresholds were fixed, but the techniques reviewed here show that indicators can be refined using predictive and diagnostic models. For example, vibration signals processed through Fourier or wavelet transforms can extract features that are then evaluated using ML techniques (e.g., SVM and random forest), as in the work of [PERSON] et al. (2018), enabling probabilistic fault diagnosis rather than relying on fixed alert levels. Real-time monitoring using IoT sensors, as seen in the works of [PERSON] et al. (2018) and [PERSON] et al. (2018), allows the condition of the asset to be dynamically updated. When progressive degradation is detected, the system can adapt the alert criteria based on signal evolution. Advanced techniques like LSTM and GRU networks model the future trajectory of health indicators, aiding in maintenance scheduling (e.g., the works of [PERSON] et al. (2018) and [PERSON] et al. (2018)). Additionally, combining multiple sensor modalities (vibration, acoustics, and temperature) leads to synthetic condition indicators, such as the Bayesian approach proposed by [PERSON] et al. (2018), which detects early risk events in mining applications. This reinforces CBM by reducing dependence on single metrics. TPM emphasizes operator autonomy, proactive failure management, and continuous improvement, promoting a culture in which personnel participate in early detection and the resolution of issues to minimize downtime.