The Bridge Between Artificial Intelligence and Predictive Maintenance in Industry 4.0: A Systematic Review
Source abstract
Abstract
This systematic literature review explores the intersection of Artificial Intelligence (AI) and Predictive Maintenance (PdM) within Industry 4.0. Using a PRISMA-based methodology, 123 studies published between 2014 and April 2024 were analyzed to characterize technological trends, algorithmic choices, industrial applications, and evaluation practices. The review reveals a consistent growth of research interest, driven by the widespread adoption of Internet of Things (IoT) devices and increased data availability. The manufacturing sector dominates the literature, although most studies rely on standardized datasets rather than real industrial environments. Among the identified AI methods, Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT) and K-Nearest Neighbors (KNNs) represent the most frequently applied algorithms for tasks such as failure prediction, fault detection, and remaining useful life (RUL) estimation. Model performance is commonly evaluated with Accuracy (Acc), Precision, Recall, F1-Score, and Root Mean Square Error (RMSE), reflecting the prevalence of both classification and regression-based PdM analyses. Despite significant advances, this review identifies persistent gaps, including limited domain diversity, scarce long-term real-world validation, and insufficient use of eXplainable AI (XAI) techniques. The findings highlight the need for broader domain coverage, improved interpretability, and validation under realistic industrial conditions. Overall, this review consolidates current knowledge on AI-enabled PdM and outlines critical directions to enhance reliability, transparency, and industrial relevance in the context of Industry 4.0.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Explainable Artificial Intelligence (XAI) · Imbalanced Data Classification Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
Centro de Investigação Naval (CINAV), Portuguese Naval Academy, 2810-001 Almada, Portugal
Research & Development Unit for Mechanical and Industrial Engineering (UNIDEMI), Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
Laboratório Associado de Sistemas Inteligentes (LASI), 4800-058 Guimarães, Portugal
Department of Electromechanical Engineering, Faculty of Engineering, University of Beira Interior, 6201-001 Covilhã, Portugal
Center for Mechanical and Aerospace Science and Technologies (C-MAST), University of Beira Interior, 6201-001 Covilhã, Portugal
Funding
Fundação para a Ciência e a Tecnologia
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