## 4 Hybrid Thematic Analysis: Key Findings from NLP and Expert Review
### Foundational Technologies and Advanced Methodologies
#### 4.4.4 Sensors and Their Role in Data Acquisition for Predictive Maintenance  
Advanced sensors--including vibration, thermal, acoustic, and pressure--form the backbone of predictive systems [25; 76]. In motors and bearings, vibration data have been used to detect incipient failures, while thermal sensors have enabled the early identification of gear and turbine faults. Additionally, acoustic sensors have been employed to detect cavitation and structural issues in turbines [33; 71].  
Recent advancements in sensor technology, such as fiber optic-based temperature arrays [44; 45] and ultrasonic thickness gauging [46; 47], have provided refined data for real-time digital twins, capturing wear rates in critical components [49; 50]. Moreover, autonomous sensor nodes have improved coverage in remote mining expansions [38], facilitating condition monitoring across vast geographical areas.  
For instance, applying radiometric infrared thermography in photovoltaic systems enables early identification of hotspot anomalies, significantly improving maintenance planning for solar farms [67]. Similarly, sensor-driven approaches in hydropower plants, employing multi-sensor data fusion, have facilitated more robust classification of bearing conditions under shifting operational regimes [14; 55]. In parallel, next-generation sensorsintegrated with edge AI modules show promise in high-noise, high-vibration contexts, such as nuclear reactor cooling loops and large-scale mechanical shovels [64; 84].  
In the mining sector, pressure sensors have been employed to monitor conveyor systems, preventing collapses and ensuring consistent material flow [52; 53]. Smart and wireless sensors have further enabled remote, real-time data acquisition, particularly in harsh or inaccessible locations. As sensor networks continue to evolve, predictive maintenance has become more scalable, reducing downtime and optimizing resource utilization. Additionally, intelligent sensor fusion has enhanced data quality, increasing reliability in advanced analytics frameworks throughout the mining industry [51; 60; 68].  
Taken together, these sensor-based innovations underscore the increasingly interconnected nature of modern industrial environments, where collaborative data streams, combined with advanced AI, can revolutionize conventional maintenance practices [2; 3; 84]. This shift is integral to realizing a more sustainable, efficient, and digitally enabled future across mining, energy, and numerous other domains.  
Beyond their role in failure detection and condition monitoring, advanced sensor networks contribute significantly to energy efficiency and sustainability in predictive maintenance strategies. By integrating real-time data streams from vibration, thermal, and acoustic sensors, industrial systems can optimize operational parameters, reducing unnecessary energy consumption and extending equipment lifespan. This alignment with sustainability objectives is particularly relevant in sectors such as mining and energy, where AI-driven sensor analytics facilitate resource conservation and lower carbon footprints. The impact of these sensor-driven approaches, as well as their integration with digital twins and advanced predictive models, is further detailed in Table 2, which synthesizes key findings from the thematic analysis and highlights their implications across multiple industrial applications.  
\begin{table}
\begin{tabular}{c c c} \hline
**Thematic** & **Key Findings (NLP + Expert Review)** & **Relevance and Potential Impact** \\
**Category** & & \\ \hline \multirow{6}{*}{Energy Efficiency and Sustainability} & \multirow{6}{*}{Emphasis on reducing energy consumption and carbon footprint through AI-driven process optimization [51; 52; 53].} & \multirow{6}{*}{Ensures alignment with broader sustainability mandates by linking automated fault detection to resource savings.} \\  & & & \\ \cline{1-1}  & & \\