## 4 Qualitative Analysis of Fault Diagnosis and Predictive Maintenance
### Machine Learning-Driven Fault Detection and Maintenance in Cable Systems  
Cables for power distribution, submarine communication, and robotic harnesses are pivotal in modern infrastructure, yet they are vulnerable to faults that can cause outages, safety hazards, and costly repairs. Recent studies have combined data-driven models with physics-informed insights to detect incipient defects, monitor operating conditions, and optimise maintenance schedules across diverse environments, from deep-sea installations to automated factories.  
Researchers first employ classical machine-learning algorithms--including \(k\)-Nearest Neighbours, random forest, decision trees, support vector machines, and isolation forests--to classify partial-discharge signals, temperature profiles, and power-line communication data, often within ensemble frameworks that reduce false alarms [43; 44; 45; 46]. Deep learning approaches extend these efforts: convolutional and recurrent architectures, such as CNN, LSTM, and GRU, automatically capture complex spatial-temporal patterns; autoencoders and attention mechanisms bolster anomaly detection when labelled data are scarce; and complex-valued or multi-valued neurons adapt neural networks to frequency-response and phasor inputs in high-voltage systems [43; 47; 48].  
Signal processing and reflectometry methods remain essential for feature extraction. Frequency- and time-domain reflectometry (FDR and TDR, respectively), Fourier and wavelet transforms, and empirical-mode decomposition isolate transient phenomena, while phase-diagram and Lissajous analyses reveal insulation ageing; the resulting descriptors feed both conventional and deep classifiers [43; 44; 45; 46]. Complementing these techniques, IoT-enabled sensors stream voltage, current, and thermal data to cloud platforms that combine machine learning predictions with physical rules in digital-twin frameworks, thereby supporting real-time remaining-useful-life estimation and cost-effective asset management [49].  
Practical applications concentrate on four themes: fault diagnosis and classification of partial discharges, insulation decay, and mechanical damage in underground and overhead cables; predictive maintenance scheduling based on health indices and failure-probability scores that mitigate downtime; thermal analysis and overheating detection using reflectometry or power-line communication signals to pre-empt insulation breakdown; and infrastructure-wide reliability assessment, where models trained on maintenance logs, weather data, and sensor measurements guide renewal priorities and investment planning.  
Despite these advances, several challenges limit widespread deployment. Many asset registers lack comprehensive failure histories or age or location details, constraining model generalisation; richer data collection strategies and auxiliary sources are needed. Environmental variability and fluctuating operating regimes also degrade algorithm robustness; transfer learning and domain adaptation represent promising remedies. Integrating data-driven predictions with physics-based digital twins can improve interpretability and reliability, yet such hybrids demand scalable edge-to-cloud architectures for low-latency inference. Finally, transparent models and standardised monitoring protocols are essential for regulatory acceptance and operator trust, motivating continued research into explainable AI and benchmarking frameworks. Overall, the literature demonstrates considerable progress in applying machine learning, deep learning, advanced signal processing, and IoT technologies to cable-system health management while underscoring the need for comprehensive datasets, real-time integration, and physics-aware modelling to realise a resilient, efficient future grid.