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 the proliferation of Industrial Internet of Things (IIoT) technologies that enable continuous monitoring of industrial assets. The extensive data generated through interconnected sensors and cyber-physical systems has created new opportunities for real-time equipment diagnostics, early fault detection, and improved operational reliability. Machine Learning (ML) techniques play a central role in transforming these heterogeneous data streams into meaningful insights, reducing unplanned downtime and enhancing productivity. Despite rapid advancements, significant challenges remain regarding model selection, performance evaluation, interpretability, and practical deployment in industrial environments. This study provides a comprehensive synthesis of ML techniques applied to PdM within IIoT ecosystems, examining methodological trends, strengths, limitations, and research gaps.

Methods: A systematic review methodology was adopted following PRISMA 2020 guidelines. Peer-reviewed studies published between 2015 and 2025 were retrieved from IEEE Xplore, ACM Digital Library, Scopus, Web of Science, and ScienceDirect. Boolean search strategies were used to identify literature focused on ML-based PdM models applied to IIoT data, cyber-physical systems, sensor networks, and digital twins. Data extracted from eligible studies included ML algorithms, datasets, feature engineering approaches, performance metrics, deployment frameworks, and identified limitations. Comparative and thematic analyses were employed to categorize methods and evaluate their effectiveness across different industrial contexts.

Findings: Sixty-two studies met the inclusion criteria. The findings show that Deep Learning (DL) architectures, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and autoencoders, predominate in contemporary PdM research due to their capacity to learn complex temporal and multidimensional sensor patterns. Hybrid models integrating DL with signal processing and classical ML methods demonstrated improved robustness and predictive accuracy. However, the review reveals persistent challenges, including the reliance on controlled or semi-synthetic datasets, limited real-time validation, data imbalance, lack of model interpretability, and constraints in integrating ML solutions with industrial hardware. These limitations hinder the scalability and practical adoption of PdM systems in real-world manufacturing environments.

Theoretical Contributions: The review synthesizes key theoretical perspectives underpinning ML-driven PdM. Data-driven modeling theory underscores the importance of high-quality sensor data and feature representations for accurate prediction. Systems theory highlights the interconnected nature of IIoT architectures and the need for interoperability across devices and platforms. Decision-support theory contextualizes the role of predictive analytics in optimizing maintenance planning and operational strategies. Additionally, emerging paradigms such as physics-informed ML and edge intelligence illustrate how theoretical advancements can bridge gaps between algorithmic accuracy and industrial applicability.

Conclusion and Implications: ML-enabled PdM offers substantial potential to transform industrial asset management within IIoT environments. To achieve large-scale implementation, future efforts must prioritize data quality improvement, real-time processing capabilities, algorithm explainability, and seamless integration with edge and cloud infrastructures. Research should advance toward federated learning, transfer learning, standardized benchmark datasets, and hybrid physics-data models to enhance model generalizability and industrial adoption. A holistic, technically informed, and context-specific framework is essential for maximizing the impact of ML-driven PdM in smart manufacturing ecosystems.