A Systematic Review of Machine Learning Techniques for Predictive Maintenance In Industrial IOT (IIOT) Environments

Abstract

Background and Purpose: Predictive Maintenance (PdM) has emerged as a critical component of smart manufacturing, driven by Industrial Internet of Things (IIoT) technologies that enable continuous monitoring of industrial assets. Interconnected sensors and cyber-physical systems create opportunities for real-time diagnostics, early fault detection, and improved operational reliability. Machine Learning (ML) techniques transform heterogeneous industrial data streams into actionable insights, but challenges remain in model selection, performance evaluation, interpretability, and practical deployment. This review synthesizes ML techniques applied to PdM within IIoT ecosystems and examines methodological trends, strengths, limitations, and research gaps.

Methods: A systematic review methodology was adopted following PRISMA 2020 principles. Peer-reviewed studies published between 2015 and November 2022 were identified from IEEE Xplore, ACM Digital Library, Scopus, Web of Science, and ScienceDirect. Boolean search strategies targeted ML-based PdM models applied to IIoT data, cyber-physical systems, sensor networks, and digital twins. Extracted information included ML algorithms, datasets, feature-engineering approaches, performance metrics, deployment frameworks, and reported limitations. Comparative and thematic analyses were used to categorize the evidence.

Findings: Sixty-two studies met the inclusion criteria. Deep Learning architectures, including convolutional neural networks, long short-term memory networks, recurrent neural networks, and autoencoders, were prominent because of their capacity to learn complex temporal and multidimensional sensor patterns. Hybrid approaches combining deep learning with signal processing or classical ML demonstrated strong predictive performance. Persistent challenges included reliance on controlled or semi-synthetic datasets, limited real-time validation, class imbalance, lack of model interpretability, and constraints in integrating ML solutions with industrial hardware.

Theoretical Contributions: The review synthesizes theoretical perspectives underpinning ML-driven PdM. Data-driven modeling emphasizes the importance of sensor quality and feature representations; systems theory highlights interoperability across interconnected IIoT components; and decision-support theory frames predictive analytics as a tool for maintenance planning and operational optimization. Emerging approaches such as edge intelligence and hybrid data-driven models provide pathways for connecting predictive accuracy with industrial applicability.

Conclusions and Policy Implications: ML-enabled PdM has substantial potential to transform industrial asset management in IIoT environments. Wider implementation requires improved data quality, real-time processing, algorithm explainability, and reliable integration with edge and cloud infrastructures. Industrial and research stakeholders should prioritize standardized benchmark datasets, interoperable deployment frameworks, privacy-aware learning, transfer learning, and lightweight models suitable for operational environments.