Predictive Maintenance
36 articles · search the full text for this term
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TinyML-Based Real-Time Industrial Fault Detection Using Vibration Signals
Abstract: Industrial rotating machinery is commonly monitored through vibration measurements because changes in bearing condition can alter signal amplitude, impulsiveness, and spectral distribution. Cloud-centred analytics may introduce communication dependence, bandwidth demand, and response delay. Tiny Machine Learning (TinyML) offers a complementary approach by executing compact inference locally on resource-constrained edge hardware. This paper presents a reproducible, deployment-oriented workflow for vibration-based machine-state classification using statistical and spectral features with small classifiers suitable …
Published in Journal of Instrumentation Technology & Innovations · Vol. 16, Issue 3, 2026 Read article
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Real Time Ai Based Fault Detection in EV
Abstract: Electric vehicles (EVs) are rapidly increasing in usage, but motor faults can affect their performance and safety. This paper presents a real- time AI-based fault detection system using sensor data and embedded processing. The system utilizes an ESP32 microcontroller along with current, vibration, and temperature sensors to continuously monitor motor conditions. An intelligent algorithm analyzes real-time data to detect abnormalities such as overheating, overcurrent, and mechanical faults. When a fault …
Published in Journal of Microcontroller Engineering and Applications · Vol. 13, Issue 3, 2026 Read article
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Digital Twin and Machine Learning Framework for Predictive Maintenance of Nuclear Power Systems
Abstract: Nuclear power systems contain safety-critical components whose degradation can affect availability, reliability, and maintenance planning. Conventional maintenance strategies are largely based on scheduled inspection, threshold monitoring, and condition-based assessment, which may not fully exploit the large volume of continuously generated plant data. Digital Twin (DT) technology provides a mechanism for maintaining a synchronized virtual representation of a physical nuclear system, while machine learning (ML) can extract degradation patterns from multivariate …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 2, 2026 Read article
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Hybrid Nanoclay–Polymer Coatings with Embedded Corrosion-Sensing Microchips for Predictive Maintenance Systems
Abstract: Polymer-composite coatings provide a versatile route for combining corrosion protection, barrier reinforcement, and functional monitoring within a single material architecture. In this study, an epoxy-based polymer composite reinforced with organically modified montmorillonite (OMMT) was developed as a multifunctional protective coating and integrated with an embedded corrosion-sensing microchip. OMMT contents of 0.5, 1.0, 1.5, and 2.0 wt.% were incorporated into the epoxy polymer-composite matrix through mechanical stirring and ultrasonication, while neat …
Published in Journal of Polymer & Composites · Vol. 14, Issue 6, 2026 Read article
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Real-Time Fault Detection and Diagnosis in Switched Reluctance Motor Using Advance Deep Neural Network for Electrical Vehicles and Industrial Applications
Abstract: In today world, electric motor play very big role in industry and daily life. Switched reluctance motor (SRM) is one of the important types of motor because it is simple in construction, low cost and high efficiency but SRM also faces many problems like noise, vibration and fault in power converter or winding. When these faults come, performance of motor goes down and sometime full system stops working. To solve …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 3, 2026 Read article
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AI Based Electrical Safety and Predictive Maintenance System
Abstract: This review examines twenty research papers on artificial intelligence (AI), Internet of Things (IoT), condition monitoring, fault diagnosis and predictive maintenance, with emphasis on their relevance to electrical safety. Electrical equipment such as induction motors, contactors, transformers, batteries and renewable-energy systems can develop abnormal conditions that lead to unexpected shutdowns, equipment damage or unsafe operation. Conventional maintenance based only on fixed schedules or after-failure repair may not identify every developing …
Published in Recent Trends in Sensor Research & Technology · Vol. 13, Issue 2, 2026 Read article
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Digital Twin Assisted Intelligent Prediction of Polymer Composite Degradation Under Environmental Exposure
Abstract: Polymer matrix composites (PMCs) deployed in aerospace, marine, automotive, and renewable-energy structures are continuously subjected to coupled environmental stressors — ultraviolet (UV) radiation, moisture ingress, thermal cycling, and mechanical loading — that progressively degrade their mechanical performance. Conventional accelerated ageing tests and empirical lifetime models are time-consuming, destructive, and poorly suited to in-service, asset-specific degradation forecasting. This paper proposes a Digital Twin (DT) assisted intelligent prediction framework that fuses a …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Intelligent Failure Detection in Biomedical Composite Materials Using Machine Vision
Abstract: The biomedical composite materials are intelligent failure-detecting, which is necessary to ensure the reliability, safety, and durability of the current healthcare equipment. This paper describes a machine vision design, which incorporates convolutional neural networks, transformer models, and ensemble learning to correctly detect and localize material defects. The proposed system takes advantage of the capabilities of high-resolution imaging, advanced preprocessing software, and deep feature learning in the identification of the intricate …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Industrial Prognostics via Ensemble Machine Learning: An Uncertainty Aware Framework for RUL Estimation on NASA FD004 Telemetry
Abstract: Estimating the Remaining Useful Life (RUL) of industrial machinery in real-time is now vital for both operational safety and smart resource management. In the aviation industry, turbofan engines deal with constantly shifting flight conditions, making traditional, scheduled maintenance both expensive and prone to error. This paper addresses the flaws in common “point-prediction” AI models, which offer a single failure date without any margin for error, by introducing a new, uncertainty-aware …
Published in Journal of Aerospace Engineering & Technology · Vol. 16, Issue 2, 2026 Read article
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Integrate AI and IoT to Develop Sustainable Polymer Structural Materials Processing Optimization: Enabled Monitoring Strategies for Performance and Lifecycle Assessment
Abstract: The need for long-lasting structural polymer materials that are both environmentally friendly and highly mechanically effective is driving demand for these materials as the industrial sector continues to grow. Optimizing processes, saving energy, detecting faults, and monitoring structures are all hindered by conventional polymer manufacture. This study suggests an AI-IoT system for environmentally friendly production of structural polymer materials to get around these problems. Tools for evaluating system performance and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 169–192 Read article
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Artificial Intelligence and Edge Computing in Oil and Gas: Applications, Architectures, and Operational Realities
Abstract: Artificial intelligence has arrived in oil and gas, and unlike some previous waves of digital enthusiasm in the sector, this one is sticking. Saudi Aramco analyses approximately 10 billion data point every day and reported USD 4 billion in technology-driven operational gains in 2024. ExxonMobil uses AI to increase shale well output by more than 5 percent. Shell has deployed machine learning across more than 10,000 assets using C3.ai to …
Published in Journal of Petroleum Engineering & Technology · Vol. 16, Issue 2, 2026 · pp. 01–06 Read article
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IOT and algorithmic intelligent motor health monitoring as well as maintenance prediction
Abstract: Manufacturing, transportation, and energy systems rely largely on industrial electric motors, and their untimely failure can result in expensive downtime, safety hazards, and decreased operational efficiency. The majority of traditional motor maintenance procedures rely on reactive methods or routine inspections, which frequently miss early-stage problems and lead to needless maintenance or unexpected breakdowns. This project offers an Intelligent Motor Health Monitoring and Predictive Maintenance System that combines Internet of Things …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 4, Issue 1, 2026 · pp. 28–37 Read article
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Development of a Generative AI Model for Early Detection and Prevention of Electrical Faults in Thermal Power Plants
Abstract: Electrical faults in thermal power plants can lead to severe equipment damage, production downtime, and safety hazards if not detected in advance. This study presents the development of a Generative Artificial Intelligence (GenAI) model for the early detection and prevention of electrical faults using predictive analytics. The proposed framework integrates Generative Adversarial Networks (GANs) with deep learning (CNN) and machine learning algorithms (Random Forest, Logistic Regression) to enhance data diversity, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 45–54 Read article
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IoT-Enabled Monitoring of AC Condensate Water for Quality Assessment and Early Detection of HVAC System Health
Abstract: The shortage of water and expensive reactive maintenance of HVAC are major problems in the modern building management. The paper introduces an Internet of Things (IoT)-enabled air conditioning (AC) condensate to water resource (predictive maintenance) and sustainable water reuse. The nature of our approach defines the quality of the condensate water at the baseline and indicates that it contains low levels of total dissolved solids (TDS) and has almost neutral …
Published in Journal of Instrumentation Technology & Innovations · Vol. 16, Issue 1, 2026 · pp. 25–35 Read article
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PREDICTIVE MAINTENANCE IN SEMICONDUCTOR SYSTEMS: INSIGHTS FROM MACHINE INTELLIGENCE AND DATA-DRIVEN METHODS
Abstract: With the fast-paced development of semiconductor technology comes the need to focus on device reliability, or how long devices will function and the likelihood of devices having operational issues. Predicting failures and avoiding downtime with the implementation of timely, actionable, and data-driven maintenance strategies are essential to insure devices function sustainably within predetermined performance levels. The implementation of predictive maintenance within artificial intelligence and machine learning technologies will provide the …
Published in Journal of Semiconductor Devices and Circuits · Vol. 13, Issue 1, 2026 · pp. 1–9 Read article
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Enhancing Maintenance Decision-Making in Thermal Power Plants Using Generative AI-Based Fault Diagnosis
Abstract: The growing complexity of operation and power consumption of thermal power stations involve the need to have intelligent fault diagnosis systems that can be used to guarantee reliability and safety in operation. In this research, a Generative AI (GenAI)-based hybrid architecture of early fault detection and predictive maintenance is proposed to improve the decision-making process of the maintenance team. The data-driven analytic approach combines methods of data-driven analytics, Generative AI …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 25–33 Read article
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Advancements and Challenges in Automated Guided Vehicles for Smart Industrial Automation
Abstract: Automated Guided Vehicles (AGVs) are increasingly central to modern industrial automation, enhancing operational efficiency in manufacturing, warehousing, and logistics. Traditionally reliant on fixed paths using magnetic tapes or wired tracks, AGVs were limited in flexibility. However, recent technological advances have enabled the development of autonomous AGVs equipped with sensor fusion, LiDAR, computer vision, and artificial intelligence (AI). These features support real-time obstacle detection, dynamic path planning, and robust performance in …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 4, Issue 1, 2026 · pp. 1–5 Read article
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A Review on AI and Machine Learning for Predictive Maintenance and FDD in RAC Systems
Abstract: The paper reviews the existing AI/ML methods first in the general context of predictive maintenance and FDD of RAC systems, then specifically focusing on granular cooling appliances. Perspectives and insights are provided on the reasons why potentially valuable models do not make it into practice more often, and where future research and development should be headed. New emerging topics for decision support systems to include domain knowledge and physics-based modeling …
Published in Journal of Refrigeration, Air conditioning, Heating and ventilation · Vol. 13, Issue 1, 2026 · pp. 15–25 Read article
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AI-Driven Predictive Maintenance Framework for Intelligent Vehicle Health Monitoring
Abstract: The accelerated development of smart and connected car systems made the necessity to find the accurate and real-time predictive maintenance solutions which would minimize the number of unexpected failures as well as increase the cars on-road safety. The current paper proposes an artificial intelligence-based hybrid predictive maintenance system that combines Long Short-Memory (LSTM) networks and the XGBoost predictor to provide a potent vehicle fault diagnosis, Remaining Useful Life (RUL) prediction, …
Published in Trends in Machine design · Vol. 13, Issue 1, 2026 · pp. 1–17 Read article
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Analysis of Machine Learning in Metal Processing: A Novel Prospect
Abstract: Metal is processed by a wide range of procedures, from forming and casting to machining and riveting. Metal processing is a crucial part of modern manufacturing. The application of machine learning (ML) is driving a significant change in the sector, which has historically depended on empirical knowledge and trial-and-error techniques. Increased production, improved product quality, and resource optimization are expected outcomes of this action. This study aims to explore the …
Published in Journal of Materials & Metallurgical Engineering · Vol. 16, Issue 1, 2026 · pp. 41–51 Read article