Aug 2026· Journal of Intelligent Decision Making and Information Science· 0 citations· 40 references
TL;DR
Artificial intelligence-driven predictive maintenance represents a critical enabler of operational excellence, resilient manufacturing systems, and sustainable industrial transformation in the era of Industry 4.0.
Abstract
Predictive maintenance has emerged as a transformative strategy within Industry 4.0, enabling organizations to transition from reactive and preventive maintenance approaches toward intelligent, data-driven asset management. The convergence of artificial intelligence, Industrial Internet of Things (IIoT), cyber-physical systems, cloud computing, digital twins, and edge computing facilitates continuous monitoring of equipment health, early fault diagnosis, and accurate prediction of component failures. These capabilities significantly reduce unexpected machine downtime, optimize maintenance scheduling, extend equipment lifespan, minimize operational costs, and improve production quality. Artificial intelligence techniques, including machine learning, deep learning, reinforcement learning, and hybrid predictive analytics, enhance the ability to process large-scale industrial data and generate reliable maintenance decisions in real time. Furthermore, predictive maintenance supports sustainability objectives through improved resource utilization, energy efficiency, and reduced material waste while strengthening organizational competitiveness. Despite implementation challenges related to data quality, interoperability, cybersecurity, and model interpretability, continuous technological advancements are accelerating industrial adoption across manufacturing, energy, transportation, healthcare, and process industries. Consequently, artificial intelligence-driven predictive maintenance represents a critical enabler of operational excellence, resilient manufacturing systems, and sustainable industrial transformation in the era of Industry 4.0.
This study proposes a comprehensive research framework that investigates the integration of hybrid artificial intelligence models into predictive maintenance systems for Industry 4.0 and aims to improve prediction accuracy, operational reliability, resource utilization, and sustainability while enabling autonomous maintenance decisions in smart factories.
Dr. T. Thirumalaikumari¹, V. Naga, Dr Kishore Thota² et al.· Journal of Intelligent Decis...· 0 citations
The findings reveal that ML algorithms significantly improve fault detection, Remaining Useful Life (RUL) estimation, and maintenance decision-making, but challenges related to data quality, model interpretability, cybersecurity, and integration with legacy systems continue to affect implementation effectiveness.
P. Siva, Sankar Shunmuga, Sundaram et al.· Stanzaleaf International Jou...· 0 citations
The proposed Explainable AI-Based Predictive Maintenance Framework (XAI-PMF) addresses this challenge by integrating IIoT sensing, intelligent feature engineering, hybrid machine learning, and explainability techniques such as SHAP, LIME, and rule extraction.
Narendra Karmarkar· International Journal of Mod...· 0 citations
The integration of Artificial Intelligence (AI) and Machine Learning (ML) into renewable energy systems (RES) is increasingly recognized as a practical pathway for improving operational efficiency, reliability, and sustainability. Renewable sources, such as solar, wind, and hydropower, as well as hybrid configurations, are inherently intermittent and operationally complex, creating persistent challenges for grid stability, asset reliability, and energy optimization. Conventional maintenance strategies, whether reactive or preventive, often lead to unplanned downtime, inefficient inspections, and increased lifecycle costs. In response, AI/ML-enabled predictive maintenance (PdM) leverages high-frequency sensor data, SCADA streams, and historical performance records to detect anomalies, diagnose faults, and estimate remaining useful life (RUL), enabling proactive maintenance interventions. Beyond maintenance, AI/ML supports operational optimization through energy generation forecasting, load prediction, grid integration, storage scheduling, and adaptive control, thereby strengthening system resilience and lowering operational costs. Unlike prior reviews that treat PdM and RES optimization as separate topics, this work provides a unified, decision-oriented synthesis that explicitly links (i) maintenance outcomes (fault detection/diagnosis/RUL) and (ii) operational outcomes (forecasting, dispatch, storage scheduling, and grid control) through shared data pipelines and coupled decision trade-offs. The review further provides a structured mapping that connects AI/ML methods to (a) RES asset types (solar, wind, hydropower, hybrid, and emerging marine systems), (b) decision objectives (fault/RUL, forecasting, dispatch and control), and (c) deployment settings (IoT/SCADA, edge-cloud, and digital-twin-enabled monitoring). Emerging technologies, including digital twins, edge AI, federated learning, and explainable AI (XAI), are discussed as enabling mechanisms for real-time monitoring, privacy-preserving learning, adaptive decision-making, and transparency in critical infrastructure. Cybersecurity risks including vulnerabilities arising from expanded IoT/edge/cloud connectivity and adversarial threats to AI-driven control are highlighted as a critical adoption barrier alongside data quality, interoperability with legacy systems, scalability, and model interpretability. By consolidating fragmented evidence across maintenance and optimization and highlighting deployment trade-offs, this review provides an implementation-oriented reference for AI/ML-enabled operation of modern renewable energy systems.
Ugwu Chinyere Nneoma, O. Chukwudi, U. Nnenna et al.· Frontiers in Energy Research· 0 citations
A structured methodology for ML-based PdM frameworks, covering data-driven, physics-based, and hybrid approaches, including supervised, unsupervised, and deep learning models is proposed, offering valuable insights for developing efficient and scalable PdM solutions.
Sithik Shah· International Journal of App...· 0 citations
Experimental results demonstrate that the proposed framework achieves high fault prediction accuracy, enhances system reliability, reduces maintenance costs, and supports data-driven decision-making in industrial environments.
Govind D. More, Shreyas Hon, Piyush Kotkar et al.· International Journal of Cre...· 0 citations
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