## 4 Qualitative Analysis of Fault Diagnosis and Predictive Maintenance
### Bearing Fault Diagnosis as a Common Function in Rotating Equipment  
Bearings are ubiquitous in rotating machinery, and their premature failure can jeopardise safety, inflate energy consumption, and interrupt production. Recent studies have fused signal processing, machine learning (ML), and deep learning (DL) to detect incipient defects under noisy, variable loads. A concise survey clarifies how these methods complement one another, highlights persisting gaps, and outlines promising research directions.  
Classical ML remains a staple for supervised health assessment. Algorithms such as random forest, support vector machines, and \(k\)-nearest neighbours discriminate between fault types using statistical descriptors--root mean square, kurtosis, or variance--or transforms such as FFT and wavelets; feature selection tools like PCA or genetic algorithms improve robustness [98; 99; 100]. Ensemble and hybrid schemes further curb misclassification.  
Deep learning architectures automate feature learning from raw vibration streams or time-frequency images. One- and two-dimensional CNNs with multiscale filters, attention blocks, and residual paths excel at spatial pattern recognition, whereas LSTM, GRU, and CNN-LSTM hybrids capture temporal dependencies and cope with variable speeds [7; 11; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125].  
Signal processing and sensor fusion techniques--STFT, CWT, wavelet packets, EEMD/CEEMDAN, and denoising filters--extract salient components, while multi-sensor combinations of vibration, acoustics, and currents offer a system-level view [102; 105; 107; 109; 112; 114; 116; 117; 118; 120; 123; 125; 126]. When labelled failures are scarce, anomaly detection with one-class SVM or isolation forests, domain adaptation, and transfer learning enhance generalisability, and reinforcement learning tackles class imbalance or feature selection [11; 106; 117; 126]. Physics-informed networks embed bearing kinematics or impulse periodicity into loss functions, boosting interpretability and trust [7; 98; 104; 108; 110; 113; 119; 121].  
Industry deploys these tools for early crack or spall detection, predictive maintenance scheduling, and online monitoring under fluctuating loads. Research has also targeted robustness to electromagnetic interference, lightweight models for edge devices, and multi-modal health indices that combine vibration, current, image, and acoustic streams. Tables S11-S13 synthesise representative studies, techniques, results, and limitations.  
Several obstacles still impede large-scale adoption. Most public datasets are laboratory-based and lack the diversity of real operating regimes, while high-quality field labelling remains labour-intensive. Concept drift induced by evolving processes demands continual recalibration, and deep and ensemble models can exceed the computational budgets of real-time systems. Moreover, engineers require transparent diagnostics, yet explainable AI toolkits are only beginning to gain traction.  
Future work should expand data acquisition through digital-twin simulation, federated learning, and standardised labelling protocols; develop quantised or pruned models for embedded deployment; and advance hybrid, physics-guided architectures that reconcile data-driven accuracy with domain knowledge. Addressing these challenges will accelerate the transition of bearing diagnosis research from controlled experiments to resilient, cost-effective solutions in petrochemical plants, transportation, and advanced manufacturing.