## 2 Methodology  
Figure 1 provides an overview of the semi-automated systematic review pipeline that combines NLP and expert validation to identify, refine, and analyse key topics in condition monitoring and predictive maintenance. Figure 2 details the NLP stages described in Section 2. After an initial expert check, each topic is re-queried, and a second screening is performed against predefined relevance and quality criteria. This multi-stage selection and synthesis process adheres to the PRISMA 2020 guidelines for systematic reviews, ensuring transparency and reproducibility. A detailed compliance checklist is provided as Supplementary Material.  
Step 1--Structured search. A Scopus query (below) retrieves English-language journal, conference, chapter, and review papers (2017-2024) on predictive maintenance, condition monitoring, and related AI techniques.  
TITLE-ABS-KEY(\"predictive maintenance\" OR \"condition monitoring\" OR \"prognostics\" OR \"failure prediction\" OR \"asset management\") \"ND (\"machine learning\" OR \"artificial intelligence\" OR \"deep learning\" ON \"neural networks\") AND PURYERAR > 2016 AND PURYERAR < 2025 AND LIMIT-TO(LASUGAGE,\"English\") AND LIMIT-TO(DOCTYPE,\"ar\",\"cp\",\"ch\",\"re\")  
Step 2--NLP-based topic discovery. Embeddings are generated with the BGE-large-en-v1.5 transformer; UMAP [18] reduces dimensionality; HDBSCAN [19] clusters the points; c-TF-IDF refines keywords; and BERTopic, enhanced with SOLAR-10.7B-Instruct [20], selects representative papers [16; 17].  
Step 3--Expert filtering and validation. After the NLP-driven discovery phase, two domain specialists independently screen every candidate record in Zotero using the following _a priori_ quality gate:Citation strength. A dynamic threshold is applied to avoid bias against recent work: an article must be in at least the 60 th percentile of citations for its publication year and document type (journal, conference, or review). For articles published in 2023-2024--where absolute counts are still low--we accept \(\geq\)3 citations in Web of Science.
2. Thematic pertinence. A paper must (i) explicitly address at least one _target asset class_ (bearings, pumps, compressors, conveyor belts, or cables) _and_ (ii) apply an AI/ML, signal processing, or hybrid physics-data technique to condition monitoring, fault diagnosis, or RUL estimation.  
Step 4--Mixed quantitative/qualitative analysis. Bibliometrics (e.g., co-authorship, citations, and topic prevalence) complements an expert thematic review of methods, challenges, producing the evidence base for Sections 3-5.  
Figure 1: Semi-automated literature review framework outlining key steps, from document retrieval to topic validation and analysis.  
Embedding Generation and NLP Approaches in the SLR Framework  
Stage 1--Embeddings. The bge-large-en-v1.5 model captures contextual semantics more effectively than classic Word2 Vec or GloVe [21, 22], outperforming larger alternatives on MTEB [23].  
Stage 2--Dimensionality reduction. UMAP retains a local-global structure while remaining faster and more scalable than t-SNE [24].  
Stage 3--Clustering. HDBSCAN discovers clusters of varying density and flags noise without requiring a preset cluster count, yielding coherent topics for diverse, real-world literature [25, 26, 27].  
Stage 4--Topic keywords. c-TF-IDF [28] highlights discriminative terms at the cluster level, sharpening thematic separation compared with standard TF-IDF.  
Stage 5--Topic representation. BERTopic integrates SOLAR-10.7B-Instruct [20] for instruction-following summarisation, ensuring representative document selection and concise topic labels.  
Stage 6--Topic assessment. Citation impact, venue quality, and thematic fit govern article selection for each topic, providing a high-quality corpus for subsequent analysis.  
This streamlined, expert-in-the-loop workflow delivers a rigorous, scalable review that captures both quantitative trends and qualitative insights across the rapidly evolving predictive maintenance literature.