## 4 Hybrid Thematic Analysis: Key Findings from NLP and Expert Review
### Sectoral Applications and Monitoring of Critical Failures  
#### 4.2.1 Predictive Monitoring of Failures in Critical Systems  
Predictive monitoring of failures in critical systems is essential for ensuring operational continuity and minimizing costs. The integration of machine learning and digital twin systems has driven significant advancements in this field. In mining, monitoring wear in drill bits has enabled early failure detection, leading to reduced operational expenses [6]. Similarly, deep neural networks applied to anomaly detection in electric motors have enhanced fault prediction accuracy and contributed to lower downtime [56, 57].  
In the sphere of ball bearing systems, several comparative analyses confirm the effectiveness of machine learning-based approaches in identifying incipient faults and mitigating sudden breakdowns [14, 23, 25]. These models often incorporate advanced feature extraction methods to handle challenging industrial environments characterized by high noise levels and limited data availability [30, 69]. Moreover, newly proposed hybrid frameworks combine physics-based models with AI-driven methodologies, enabling more robust tracking of fault progression in critical components, such as turbine guide bearings and rotating shafts [4, 55, 70].  
Convolutional neural networks have been successfully employed to identify cracks in gearboxes by analyzing vibration and acoustic data under non-stationary conditions [51, 71]. In hydroelectric plants, the analysis of bearing vibrations has improved the precision and speed of anomaly detection [55, 60]. Additionally, data mining techniques applied to internal combustion engine components have proven effective in predicting antifriction bearing failures [72].  
Large rotating machines have particularly benefited from combined vibrational and thermal monitoring, allowing for an integrated assessment of gear meshing conditions [15; 16; 17]. Studies indicate that the implementation of advanced random forest classification techniques has resulted in a nearly 70% reduction in catastrophic failures in underground mechanical shovels, highlighting the potential of machine learning in high-risk environments [19; 20].  
In the cement sector, TCN-LSTM networks have been utilized to model thermal processes in rotary kilns, enabling the anticipation of suboptimal operating conditions and contributing to improvements in both safety and energy efficiency [73]. Hybrid models that merge machine learning with multibody dynamic simulations have enhanced imbalance predictions in large rotors [74], transforming asset management and increasing reliability. Furthermore, the analysis of hidden chaotic attractors has provided new perspectives on controlling complex system behaviors under uncertain conditions [75].  
In nuclear power plants, unsupervised neural network approaches, such as autoencoders, have been tested for early anomaly detection, demonstrating promising results in failure prediction and maintenance scheduling [76; 77]. Additionally, newly proposed survival analysis frameworks, combined with Bayesian statistics, have been shown to forecast the remaining useful life of pumps and other rotating components under multiple fault types. These advancements further reinforce the importance of data-driven methodologies in high-risk industries, ensuring improved reliability and operational efficiency [17; 64].