## 4 Hybrid Thematic Analysis: Key Findings from NLP and Expert Review
### Sectoral Applications and Monitoring of Critical Failures
#### 4.2.3 Integration of IoT and Machine Learning in Asset Monitoring  
The integration of IoT and machine learning has transformed asset monitoring by enabling continuous oversight, real-time data analysis, and predictive insights [21; 56]. Remote monitoring is particularly beneficial in restricted or hazardous environments, where access to critical equipment is limited. In mining, sensor networks facilitate the real-time transmission of vibration and temperature data to machine learning algorithms, significantly improving fault detection and predictive maintenance capabilities [18; 55].  
IoT-based platforms also support custom Edge AI solutions that enhance decision-making processes in remote pit or underground environments [28; 76; 79]. Additionally, the implementation of advanced event-based data logging mechanisms has helped circumvent bandwidth limitations commonly encountered in large-scale mining operations, ensuring efficient data transmission and processing [29; 30]. Studies have further demonstrated the robust performance of neural network-enabled microcontrollers in real-time monitoring applications [31].  
Recent implementations of IoT-driven predictive maintenance solutions have shown promising results in heavy industrial machinery, including forced blowers, compressor pumps, and motor-fan assemblies, with significant reductions in unplanned downtime [23; 39; 66]. By embedding real-time data analytics at the edge level, practitioners can quickly detect anomalies without relying solely on cloud-based computations, resulting in faster response times and reduced operational costs [24; 80]. Furthermore, the integration of knowledge-based transformations into big-data analytic models has introduced new approaches for large-scale industrial deployments under Industry 4.0, supporting enhanced decision-making frameworks [40; 81].  
IoT-based predictive models, such as random regression and decision trees, have been successfully applied to forecast failures in energy systems [18; 21]. Likewise, deep neural networks have demonstrated effectiveness in enhancing real-time turbine monitoring [30; 68]. Condition-based maintenance strategies, informed by IoT-generated data, have minimized unnecessary interventions, improving asset availability and overall system reliability [16; 66]. Additionally, digital twins integrated with IoT technologies have refined simulation accuracy and optimized decision-making processes in industrial settings [58; 60].  
Reducing unplanned downtime has led to substantial cost savings, as evidenced by successful fault prevention in generators and transformers [18; 82]. Moreover, enhanced sustainability has been achieved through more efficient resource utilization, reinforcing the role of IoT in optimizing industrial energy consumption [62; 68].  
Simultaneously, secure data transfer protocols within IoT frameworks have been extensively studied to ensure robust encryption and prevent unauthorized access, a critical requirement in large-scale mining plants where data integrity is paramount [32; 33; 34; 65; 83].