## 1 Introduction
Building on these advances, this study presents a hybrid systematic literature review (SLR) that couples LLM-assisted screening with expert validation. LLMs first reveal high-impact themes; experts then refine and contextualise the findings, yielding an in-depth portrait of state-of-the-art algorithms, challenges, and industrial relevance. This review addresses five research questions: (i) Which ML and DL methods are most effective for fault diagnosis and predictive maintenance in industrial equipment? (ii) What principal challenges persist, and how can they be mitigated? (iii) How do big data, IoT, and Industry 4.0 technologies improve scalability, speed, and reliability? (iv) How do hybrid models and XAI enhance transparency and trust? (v) What bibliometric insights illuminate the trajectory of sustainable infrastructure and its link to predictive maintenance? By combining automated text mining with domain expertise, this work delivers both breadth--spanning signal processing, classical machine learning, and deep neural approaches--and depth--addressing real-time deployment, interpretability, and Industry 5.0 principles. This synthesis aims to support the development of safer, more energy-efficient, and human-centric maintenance solutions while identifying research gaps to motivate future work. To facilitate reuse and comparison, supplementary summary tables were compiled for each reviewed study, detailing the applied techniques, application domains, performance metrics, and reported limitations. These resources are provided as machine-readable material to assist both researchers and practitioners. Unlike earlier surveys that relied exclusively on manual screening or fully automated text mining, this study employed a hybrid, two-stage pipeline. In the first stage, transformer-based embeddings--combined with UMAP-HDBSCAN and BERTopic--were used to cluster over 19,918 abstracts into coherent topic groups. What sets this approach apart is its explicit focus on industrial equipment: the resulting clusters revolve around five core asset classes--compressors, bearings, hydraulic pumps, conveyor systems, and cable infrastructure--allowing for targeted analysis of condition monitoring strategies across equipment types. In the second stage, a panel of domain experts refined this thematic landscape by discarding marginal topics, merging overlaps, and selecting high-quality studies that exemplify each retained cluster. This expert-in-the-loop curation ensures that the final corpus combines methodological diversity with industrial relevance. The synthesised findings are captured in structured supplementary tables, which catalogue the applied techniques, main application, reported metrics and outcomes, and acknowledged limitations for each study. These tables not only enable systematic cross-comparison across asset types but also serve as a reusable evidence base for both researchers and practitioners seeking to deploy predictive maintenance strategies in complex, real-world environments. The remainder of this paper is organised as follows: Section 2 details the methodology, Section 3 provides a bibliometric overview, Section 4 reports qualitative findings, Section 5 discusses implications and future directions, and Section 6 concludes the study.