## 1 Introduction  
Fault diagnosis in critical industrial assets--including bearings, compressors, hydraulic pumps, and conveyor belts--remains essential to minimise downtime, financial losses, and safety hazards [1, 2, 3]. Predictive maintenance seeks to detect incipient faults before catastrophic failure [4], yet traditional diagnostic techniques face persistent obstacles such as data scarcity, non-stationary operating conditions, and elevated noise levels [5]. From a macro-economic standpoint, the International Energy Agency (IEA) estimates that unplanned outages cost energy-intensive industries nearly USD 50 billion every year, with individual facilities losing 3-5% of their annual production capacity as a direct consequence of equipment failures [6]. The same analysis shows that data-driven predictive-maintenance programmes--combining high-frequency sensing with machine learning analytics--can cut downtime by 10-20%, yielding global savings of USD 8-15 billion per annum while simultaneously curbing energy waste and associated CO2 emissions. Quantifying these losses highlights the concrete economic value of predictive maintenance in reducing capacity shortfalls and provides a compelling motivation for the systematic review presented here. A major limitation is the shortage of representative fault data, especially for specialised systems like rolling bearings and hydraulic circuits [7]. In addition, variable speeds and loads and environmental interference complicate the extraction of reliable features [8; 9; 10]. These problems are amplified in real-world plants, where fluctuating ambient conditions can mask fault signatures [11]. Therefore, manual, expert-driven feature engineering is time-consuming, subjective, and difficult to scale across heterogeneous machinery [12]. To address these issues, recent frameworks such as that proposed by [PERSON] et al. [13] introduce refined entropy measures and support vector classifiers to enhance diagnostic accuracy under low-data conditions. Maintenance practice spans a continuum from time-based servicing, where assets are replaced or overhauled at fixed intervals; through _condition-based maintenance_ (CBM), which triggers work when measurable indicators cross preset thresholds; to _predictive maintenance_ (PdM), which forecasts failures in advance. Recent surveys on industrial equipment highlight three dominant technical streams. First, signal-driven CBM schemes still rely on straightforward statistical limits applied to vibration or current signatures collected from bearings, pumps, or conveyor drives [2; 3]. Second, physics-driven approaches embed mechanistic wear or leakage models, e.g., for rolling bearings or hydraulic circuits, into Bayesian or particle-filter frameworks to refine health estimates [7]. Third, hybrid data-driven PdM combines advanced analytics such as convolutional or temporal CNN-LSTM networks with engineered features or wavelet packets, delivering early fault recognition and remaining-useful-life prediction across diverse machinery [12; 14]. Machine learning (ML) and deep learning (DL) techniques have been widely adopted to overcome these drawbacks [7; 14]. Convolutional and recurrent architectures automatically learn informative representations from raw sensor streams, capturing complex temporal-spatial patterns in noisy, high-dimensional data [15]. These models can adapt to changing regimes and detect subtle degradation patterns earlier than classical methods. Nevertheless, progress is still hindered by the limited availability of diverse datasets and by the \"black-box\" nature of many DL models, which restricts interpretability and trust. Recent work has integrated explainable artificial intelligence (XAI) and hybrid physics-informed models to address these issues without sacrificing predictive power. Given the rapid pace of innovation, systematic synthesis is indispensable. Recent reviews have used large language models (LLMs) to accelerate topic discovery and mapping [16; 17].