## 3 Results
### Applications Across Industrial Domains
#### 3.3.1 Multi-Industry Manufacturing  
ML-based PdM across sectors uses IoT, real-time data, and scalable AI tools. Ensemble methods like random forest (RF) and extreme gradient boosting (XGBoost) [16] effectively process high-dimensional sensor data for fault prediction. Fog computing with logistic regression and genetic algorithms enhances real-time decision making in Industry 4.0 contexts [17]. RF supports cloud-based systems for estimating RUL [18], while, in [19], a hybrid DL model achieves high predictive performance using engine and battery data. Algorithm selection studies show that RFs and DTs excel on small datasets, while k-nearest neighbors (KNNs) suits larger ones [20]. For small and medium-sized enterprises (SMEs), a structured phased approach is recommended, emphasizing interdisciplinary teams and training [21]. Double attention or A\({}^{2}\) - LSTM prioritizes features using attention mechanisms for improved aircraft manufacturing predictions [22], while cyber-physical systems using historical data offer cost-effective solutions for SMEs [23]. The balanced k-star model addresses class imbalance with high accuracy and interpretability [24]. Building on these advancements, gradient boosting (GB) and XGBoost remain top performers in RUL prediction and maintenance planning, offering cost and reliability benefits [25; 26].  
In the steel and metals industry, a cloud-based fault classification system for hot-rolling mills integrated digital twin and XGBoost [27], while LSTM autoencoders enabled early fault detection in laser welders, with 97.3% accuracy [28].  
Textiles and wood product manufacturing emphasizes event-driven systems and cloud-based ML. A tree-based system using event logs achieved 98.9% accuracy in RUL prediction without hardware upgrades [29], while AdaBoost and IoT sensor data classified machine stoppages in knitting systems with 92% accuracy [30].  
Food, beverage, and consumer goods applications prioritize cost-effective decision support and ensemble learning. A DT combined with a failure mode, effects, and criticality analysis system optimized food production maintenance with 96.3% accuracy [31]. Similarly, Arduino sensor systems combined with RF improved overall equipment effectiveness by 13.1% and reduced failures by 62.4% [32]. Building on this trend, ensemble learning on IoT data reached 95.9% accuracy in wafer stick production [33].  
In the pharmaceutical and medical sectors, a hybrid framework using boosted decision trees and neural networks reduces production disruptions through accurate maintenance time estimation [34]. Expanding on this approach, a multi-model system combining SVMs, DTs, and ANNs predicted equipment failures across over \(13,000\) medical devices [35].  
Chemical and construction industries apply high accuracy and interpretable models. In concrete manufacturing, CatBoost outperformed six other classifiers (F1\({}_{\text{score}}=0.98\)) using sensor data [36]. For petrochemical compressors, XGBoost with Shapley values enabled fault prediction and root-cause transparency (area under the curve or AUC \(>90\%\)) [37].  
In aerospace, federated learning models predicted anomalies and RUL in aero-engines without raw data sharing [38]. In parallel, ensemble approaches, such as RFs and random under-sampling boosted trees, were effective for RUL and fault detection in turbofan engines and pump systems [39].