## 4 Qualitative Analysis of Fault Diagnosis and Predictive Maintenance
### Artificial Intelligence and Predictive Maintenance in Conveyor Belt Systems  
Conveyor belts are critical to bulk material handling in mining, logistics, and manufacturing, yet they operate under abrasive, dusty, and highly variable conditions that accelerate wear and heighten failure risk. Recent work has blended classical machine learning (ML), deep learning (DL), advanced signal processing, and physics-informed modelling to anticipate defects, schedule maintenance, and optimise energy use.  
Classical and hybrid ML models--linear or polynomial regression, decision trees, \(k\)-nearest neighbours, random forest (RF), gradient boosting (GB), linear/quadratic discriminant analysis, and support vector machines--remain popular for the classification of roller wear, belt damage, and motor faults. Ensemble schemes often embed meta-learners such as ridge regression, while multilayer perceptrons and probabilistic neural networks address non-linear patterns [127; 128; 129; 130; 131].  
Deep learning methods dominate recent studies: CNN, LSTM, GRU, and temporal CNN-LSTM hybrids, frequently augmented with autoencoders or attention blocks, excel at feature extraction and time-series forecasting under varying loads and speeds [132; 133; 134; 135; 136].  
Signal processing and feature extraction pipelines employ MODWPT, DWT, CEEM-DAN, and FFT to isolate vibration or acoustic signatures; statistical descriptors (RMS, kurtosis, and variance) and optimisation tools such as genetic algorithms, PCA, and Kernel PCA refine the inputs [133; 136; 129].  
Physics-based and hybrid models integrate thermodynamic or mechanical simulations, e.g., fluid-dynamic analyses and pressure-volume correlations--with neural surrogates to accelerate what-if studies and improve failure prediction [134; 131].  
Anomaly detection frameworks rely on one-class SVM, isolation forests, and sequence models (LSTM and RNN) to flag outliers and track degradation trends, enabling proactive maintenance planning [137; 138; 130].  
These techniques underpin practical applications: fault diagnosis and classification of roller misalignment, belt tears, and material build-up; predictive maintenance scheduling that reduces unplanned outages; energy consumption optimisation by detecting overloads or blockages; degradation analysis of rollers, bearings, and drives; and real-time multivariate monitoring of speed, load, vibration, and temperature. IoT sensors stream high-frequency data to cloud platforms, where big data analytics and interactive dashboards support agile decision making.  
Persistent gaps hinder industrial uptake. High-quality, labelled datasets that capture the diversity of real operating regimes are scarce, as field instrumentation and granular annotation are labour-intensive. Advanced DL models can be computationally burdensome for near-real-time diagnostics unless pruned or quantised for edge deployment. Limited model interpretability slows acceptance among engineers, and heterogeneous sensor protocols complicate cross-site transfer.  
Therefore, research should pursue scalable data collection strategies--federated learning, digital-twin augmentation, and standardised labelling--to enrich training corpora while preserving confidentiality. Lightweight, physics-guided architectures and edge-computing pipelines can balance accuracy with latency constraints. Explainable AI techniques such as Layer-wise relevance propagation or DeepSHAP, coupled with hybrid physics-data models, can bolster trust and aid in root-cause analysis. Finally, transfer learning and domain adaptation methods are needed to port models across industries and environments, enabling robust, energy-efficient predictive maintenance for conveyor systems worldwide.