## 3 Results
### Machine Learning Architectures  
ML models demonstrate versatile and robust performance across industrial sectors, with each domain favoring architectures aligned to specific tasks. Tree-based models such as RFs, DTs, XGBoost, and GB are among the most frequently employed, as illustrated in Figure 6. Temporal networks like LSTM also appear prominently in commonly used architectures.  
In aerospace, federated models like FedLSTM offer superior RUL estimation, while traditional models show acceptable but limited accuracy. Buildings and HVAC systems benefit from SVMs and LSTM-based hybrids for early fault detection, though precision varies. CatBoost and XGBoost lead in chemical and construction domains, while ANN dominates general production for fault diagnosis and maintenance scheduling. Ensemble models consistently enhance accuracy in food, maritime, and rotating equipment sectors. Power systems, robotics, and wind energy increasingly rely on DL hybrids for anomaly detection and prediction, often surpassing conventional classifiers. These trends align with  
Figure 6: Frequently employed architectures for study evaluation.  
the evaluation emphasis shown in Figure 7, where accuracy, precision, recall, and F1-score are the most cited performance metrics.  
However, despite the widespread reporting of these metrics, their interpretation often lacks critical depth, particularly under conditions of class imbalance or imprecise labeling. For example, several studies achieve high overall accuracy (e.g., \(>95\%\) with RF or SVM models) without disclosing the distribution of faulty versus non-faulty instances. In imbalanced datasets like SECOM, this can be misleading, as high accuracy may result from bias toward the majority class. Although resampling techniques like SMOTE are sometimes used to address imbalance, many studies omit such details, raising concerns about the validity of reported results. Furthermore, inconsistencies in threshold settings and the lack of confusion matrices or AUC curves complicate meaningful cross-study comparisons and hinder critical evaluation of false positives and false negatives, which are crucial in maintenance-critical systems.  
In high-stakes industrial sectors such as aerospace, energy, and manufacturing, domain-specific metrics like RUL offer a more actionable perspective than traditional classification metrics. RUL estimates the time remaining before a component fails, enabling optimized maintenance scheduling and reduced downtime. Studies leveraging datasets like CMAPSS and National Aeronautics and Space Administration (NASA) increasingly model RUL as a regression task, evaluated through root mean square error and mean absolute error, with architectures such as LSTM and CNN excelling due to their ability to capture temporal degradation patterns.  
To enhance the rigor and operational relevance of predictive maintenance models, future research should consistently report dataset balance ratios, sampling methods, and error distributions, alongside standard metrics. More importantly, integrating prognostics-oriented metrics like RUL into evaluation frameworks not only improves interpretability but also ensures that model performance aligns with real-world maintenance objectives.