Jul 2026· International Journal of Science, Strategic Management and Technology· 0 citations
TL;DR
An Industrial Internet of Things (IIoT)-based Predictive Maintenance System that integrates smart sensors, edge computing, cloud analytics, artificial intelligence, and digital twin technology is proposed that contributes to the development of intelligent and self-optimizing industrial environments aligned with Industry 4.0 objectives.
Abstract
The rapid adoption of Industry 4.0 technologies has transformed modern manufacturing by enabling intelligent monitoring and automation of industrial equipment. However, unexpected machine failures continue to cause production downtime, increased maintenance costs, and reduced operational efficiency. Predictive maintenance has emerged as an effective strategy to address these challenges by forecasting equipment failures before they occur. This paper proposes an Industrial Internet of Things (IIoT)-based Predictive Maintenance System that integrates smart sensors, edge computing, cloud analytics, artificial intelligence, and digital twin technology. The proposed framework continuously collects machine parameters such as vibration, temperature, pressure, current consumption, and acoustic signals through connected sensors. The collected data is analyzed using machine learning algorithms to identify anomalies, estimate remaining useful life (RUL), and generate maintenance recommendations. A digital twin model provides a virtual representation of industrial assets, enabling real-time simulation and performance evaluation. Experimental results demonstrate significant improvements in fault detection accuracy, equipment availability, and maintenance efficiency while reducing downtime and operational expenses. The proposed system contributes to the development of intelligent and self-optimizing industrial environments aligned with Industry 4.0 objectives.
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
Industry 4.0 has transformed manufacturing through the integration of Industrial IoT (IIoT), cyber-physical systems, cloud computing, and artificial intelligence, making predictive maintenance (PdM) a key strategy for improving equipment reliability. Unlike traditional maintenance, AI-driven PdM analyzes real-time sensor data to predict equipment failures before they occur. However, many AI models operate as black boxes, limiting trust and interpretability. The proposed Explainable AI-Based Predictive Maintenance Framework (XAI-PMF) addresses this challenge by integrating IIoT sensing, intelligent feature engineering, hybrid machine learning (Random Forest, Gradient Boosting, LSTM, and Transformers), and explainability techniques such as SHAP, LIME, and rule extraction. These methods provide transparent fault predictions and maintenance recommendations by highlighting the factors influencing equipment degradation. Continuous learning further enables adaptive model updates as new operational data become available. Overall, the framework improves prediction accuracy, reduces downtime and false alarms, enhances maintenance scheduling, and supports trustworthy, intelligent asset management for next-generation smart factories.
Narendra Karmarkar· International Journal of Mod...· 0 citations
The rapid advancement of Industry 4.0 technologies has transformed manufacturing systems through the integration of Artificial Intelligence (AI), Internet of Things (IoT), and Machine Learning (ML). Among these innovations, predictive maintenance has emerged as a critical strategy for improving equipment reliability, reducing operational costs, and minimizing unplanned downtime. Machine Learning techniques enable manufacturing organizations to analyze historical and real-time sensor data to predict equipment failures before they occur. This study examines the applications of Machine Learning in predictive maintenance within manufacturing environments, emphasizing its benefits, challenges, and future opportunities. The paper reviews existing literature, proposes a conceptual framework, and discusses the impact of ML-based predictive maintenance on operational efficiency and production sustainability. The findings reveal that ML algorithms significantly improve fault detection, Remaining Useful Life (RUL) estimation, and maintenance decision-making. However, challenges related to data quality, model interpretability, cybersecurity, and integration with legacy systems continue to affect implementation effectiveness. The study concludes that ML-driven predictive maintenance represents a strategic necessity for modern manufacturing enterprises aiming to achieve smart and sustainable industrial operations. Recent reviews also indicate increasing adoption of AI-based prognostics and health management systems in industrial machinery.
Unknown authors· Stanzaleaf International Jou...· 0 citations
The rapid advancement of Industry 4.0 has transformed conventional industrial automation by integrating intelligent sensing, cyber-physical systems, industrial Internet of Things (IIoT), cloud computing, and advanced artificial intelligence technologies into manufacturing environments. Among these innovations, predictive maintenance has emerged as a strategic approach for improving equipment reliability, minimizing unexpected failures, reducing maintenance costs, and enhancing production efficiency. However, the increasing complexity of industrial systems requires more robust and adaptive prediction mechanisms than those offered by conventional machine learning models. Hybrid artificial intelligence models, combining deep learning, ensemble learning, optimization algorithms, and explainable decision-making techniques, provide improved fault diagnosis, remaining useful life estimation, and maintenance scheduling under dynamic operating conditions. Furthermore, intelligent predictive maintenance contributes significantly to sustainable industrial automation by reducing energy consumption, minimizing material waste, extending equipment lifespan, and supporting environmentally responsible manufacturing practices. This study proposes a comprehensive research framework that investigates the integration of hybrid artificial intelligence models into predictive maintenance systems for Industry 4.0. The proposed framework aims to improve prediction accuracy, operational reliability, resource utilization, and sustainability while enabling autonomous maintenance decisions in smart factories. The study further establishes performance evaluation metrics and implementation strategies for next-generation intelligent industrial maintenance systems.
Dr. T. Thirumalaikumari¹, V. Naga, Dr Kishore Thota² et al.· Journal of Intelligent Decis...· 0 citations
Unexpected equipment failures are known to be one of the most critical causes of production losses. Such failures can result in increased maintenance cost, compromise safety of operators and increase operational inefficiency. Therefore, there is a growing interest to monitor equipment during normal operation and predict possible failure before it actually occurs. This paper describes a smart Industrial Internet of Things (IIoT) framework to monitor industrial equipment in real time using ensemble learning for failure prediction. The framework initially collects a variety of real time operating parameters of industrial equipment such as temperature, vibration, speed, torque, pressure, etc from various industrial sensors. The collected data is then preprocessed using techniques such as treatment of missing values, noise removal, feature scaling, handling of class imbalance and feature selection to select most relevant features. The preprocessed data is then fed into various machine learning models such as random forest, support vector machine, gradient boosting and multi layer perceptron. A stacking-based ensemble model is used to combine the predictions of individual models to improve the overall prediction accuracy and stability. The final output of the framework is a classification of equipment condition into normal or failure-prone, along with a probability of failure. Shapley Additive explanations (SHAP) is used to provide both global and local interpretability to the predictions made by the model. The performance of the model is evaluated using various metrics such as accuracy, precision, recall, F1-score, area under receiver operating characteristic curve, false-alarm rate and inference time. The framework has potential to provide better failure detection using combination of IIoT monitoring and explainable AI, and provide understandable reasons for failure to maintenance personnel responsible for maintenance.
Enoch Success Boakai, P. A. Mary, R. Singh et al.· International journal of com...· 0 citations
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.
Banoth Samya, V. Ramesh, A. Vathsala et al.· Journal of Intelligent Decis...· 0 citations