## 2 Systematic Analysis Methodology  
In this section, the methodology employed for the systematic analysis is described in detail. Each stage of the process, from data extraction and validation to topic identification and result synthesis, is outlined to provide a comprehensive understanding of the approach. The general workflow of the methodology is illustrated in Figure 1, which highlights the key steps, including the integration of NLP techniques and expert knowledge for thematic analysis, as well as the synthesis of results through quantitative and qualitative evaluations. Execute Extraction Criteria: This stage involves running queries in both the _Scopus_ and _Web of Science (WoS)_ databases to retrieve relevant documents. The process is based on a predefined search strategy:  
* Search Terms: The query used was: _\"fault detection\"_ AND _\"machine learning\"_ AND _\"predictive maintenance\"_ (1)
* Document Types: The search included various types of indexed publications, such as research articles, reviews, and conference proceedings. Sources of Information:
* Scopus: Selected for its extensive coverage of high-quality scientific literature across diverse disciplines. * Web of Science (WoS): Chosen due to its credibility and focus on engineering and technical research. Figure 1: Workflow of the systematic analysis methodology, including data extraction, validation, topic identification through NLP, and result synthesis through quantitative and qualitative analyses. Merge and Clean Documents: After retrieving documents from _Scopus_ and _WoS_, this stage focuses on consolidating the data and applying rigorous inclusion and exclusion criteria to filter relevant studies. This ensures that the final dataset aligns with the research objectives and maintains a high standard of quality. * **Inclusion Criteria:*
* Articles published between 2015 and 2025. * Studies written in English. * Publications in journals classified as Q1 or Q2 in the _Scopus_ or _WoS_ indices, as well as in peer-reviewed conference proceedings indexed in _Scopus_ or _WoS_. * At least five verified citations. * Direct applications in industrial equipment focused on mining or related industries. * **Exclusion Criteria:*
* Articles with restricted access without verification possibility. * Publications in languages other than English. * Duplicate documents across the consulted databases. Thematic Analysis: NLP + Experts: Following references [16; 17; 18], along with a comprehensive understanding of the selected texts, a thematic analysis was conducted to identify key topics. This stage combined advanced Natural Language Processing (NLP) techniques with bibliometric and semantic analysis to ensure a robust and contextually relevant categorization of themes. NLP algorithms were used to extract latent patterns, identify frequently occurring terms, and structure the relationships between concepts, allowing for an unbiased, scalable, and efficient processing of a large number of documents. The collaboration between automated analysis and expert interpretation facilitated the generation of a comprehensive list of critical topics for further exploration and analysis. By leveraging NLP, this methodology allowed the detection of emerging themes beyond predefined categories, ensuring an adaptive and data-driven selection of topics. This NLP-driven classification was validated by domain experts to refine and contextualize the detected themes, ensuring their practical relevance. The combination of domain experts and machine learning specialists enabled a rigorous cross-validation of the extracted evidence [27]. Full Review and Inclusion Validation: In this stage, the curated dataset underwent a thorough review and validation process. The research team carefully evaluated each document to ensure compliance with the predefined inclusion and exclusion criteria, as well as its relevance to the study objectives.