Paradigm Shift for Predictive Maintenance and Condition Monitoring from Industry 4.0 to Industry 5.0: A Systematic Review, Challenges and Case Study

Results in Engineering, Год журнала: 2024, Номер unknown, С. 102935 - 102935

Опубликована: Сен. 1, 2024

Язык: Английский

---

A systematic review of big data innovations in smart grids

Results in Engineering, Год журнала: 2024, Номер 22, С. 102132 - 102132

Опубликована: Апрель 21, 2024

Multiple industries have been revolutionized by the incorporation of data science advancements into intelligent environment technologies, specifically in context smart grids. Smart grids offer a dynamic and efficient framework for management optimization electricity generation, distribution, consumption, thanks to developments big analytics. This review delves integration Grid applications Big Data analytics reviewing 25 papers screened with PRISMA standard. The paper matter encompasses critical domains including adaptive energy management, canonical correlation analysis, novel methodologies blockchain machine learning. emphasizes contributions efficiency, security, sustainability means rigorous methodology.

Язык: Английский

---

Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance

Applied Sciences, Год журнала: 2025, Номер 15(2), С. 854 - 854

Опубликована: Янв. 16, 2025

This paper proposes a sustainable model for integrating robotic process automation (RPA) and machine learning (ML) in predictive maintenance to enhance operational efficiency failure prediction accuracy. The research identified key gap the literature, namely limited integration of RPA, ML, sustainability manufacturing, which led development this model. Using PICO methodology (Population, Intervention, Comparison, Outcome), study evaluated implementation these technologies Alpha Company, comparing results before after model’s adoption. intervention integrated RPA ML improve accuracy optimize operations. Results showed 100% increase mean time between failures (MTBF), 67% reduction repair (MTTR), 37.5% decrease costs, 71.4% unplanned downtime costs. Challenges such as initial costs need continuous training were also noted. Future could explore big data AI further demonstrates that leads improvements, cost reductions, environmental benefits, contributing industrial

Язык: Английский

---

AI in Structural Health Monitoring for Infrastructure Maintenance and Safety

Infrastructures, Год журнала: 2024, Номер 9(12), С. 225 - 225

Опубликована: Дек. 7, 2024

This study explores the growing influence of artificial intelligence (AI) on structural health monitoring (SHM), a critical aspect infrastructure maintenance and safety. begins with bibliometric analysis to identify current research trends, key contributing countries, emerging topics in AI-integrated SHM. We examine seven core areas where AI significantly advances SHM capabilities: (1) data acquisition sensor networks, highlighting improvements technology collection; (2) processing signal analysis, techniques enhance feature extraction noise reduction; (3) anomaly detection damage identification using machine learning (ML) deep (DL) for precise diagnostics; (4) predictive maintenance, optimize scheduling prevent failures; (5) reliability risk assessment, integrating diverse datasets real-time analysis; (6) visual inspection remote monitoring, showcasing role AI-powered drones imaging systems; (7) resilient adaptive infrastructure, enables systems respond dynamically changing conditions. review also addresses ethical considerations societal impacts SHM, such as privacy, equity, transparency. conclude by discussing future directions challenges, emphasizing potential efficiency, safety, sustainability systems.

Язык: Английский

---

Forecasting energy consumption and enhancing sustainability in microbreweries: Integrating ANN-based models with thermal storage solutions

Journal of Energy Storage, Год журнала: 2025, Номер 112, С. 115508 - 115508

Опубликована: Янв. 24, 2025

Язык: Английский

---

Artificial Intelligence-Based Fault Diagnosis for Steam Traps Using Statistical Time Series Features and a Transformer Encoder-Decoder Model

Electronics, Год журнала: 2025, Номер 14(5), С. 1010 - 1010

Опубликована: Март 3, 2025

Steam traps are essential for industrial systems, ensuring steam quality and energy efficiency by removing condensate preventing leakage. However, their failure results in loss, operational disruptions, increased greenhouse gas emissions. This paper proposes a novel predictive maintenance system that integrates statistical time series features transformer encoder–decoder models fault diagnosis visualization. The proposed combines IoT sensor data, parameters, open data (e.g., weather information public holiday calendars), machine learning, two-dimensional diagnostic projection to improve reliability interpretability. Experiments were conducted two plants: an aluminum processing plant food manufacturing plant, the achieved superior defect detection accuracy compared existing methods. transformer-based model outperformed traditional methods, including random forest, gradient boosting, variational autoencoder, classification clustering. also demonstrated average 6.92% reduction thermal across both sites, highlighting its potential reduce carbon research highlights transformative impact of AI-based technologies operations provides framework sustainable practices.

Язык: Английский

---

A survey on potentials, pathways and challenges of large language models in new-generation intelligent manufacturing

Robotics and Computer-Integrated Manufacturing, Год журнала: 2024, Номер 92, С. 102883 - 102883

Опубликована: Сен. 26, 2024

Язык: Английский

---

Predictive Failure Analytics in Critical Automotive Applications: Enhancing Reliability and Safety through Advanced AI Techniques

Journal of Artificial Intelligence and Big Data, Год журнала: 2024, Номер 4(1), С. 48 - 60

Опубликована: Фев. 15, 2024

Failure prediction can be achieved through prognostics, which provides timely warnings before failure. is crucial in an effective prognostic system, allowing preventive maintenance actions to avoid downtime. The prognostics problem involves estimating the remaining useful life (RUL) of a system or component at any given time. RUL defined as time from current goal make accurate predictions close failure provide early warnings. J S Grewal and J. comprehensive definition their paper "The Kalman Filter approach estimation." A process quadruple (XU f P), where X state space, U control P set possible paths, represents transition between states. applying values change system's over

Язык: Английский

---

Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions

Journal of Applied Data Sciences, Год журнала: 2024, Номер 5(2), С. 455 - 473

Опубликована: Май 15, 2024

Integrating Artificial Intelligence (AI) within Industry 4.0 has propelled the evolution of fault diagnosis and predictive maintenance (PdM) strategies, marking a significant shift towards smarter paradigms in mechatronics sector. With advent 4.0, mechatronic systems have become increasingly sophisticated, highlighting critical need for advanced methodologies that are both efficient effective. This paper delves into confluence cutting-edge AI techniques, including machine learning (ML) deep (DL), with multi-agent (MAS) to enhance precision facilitate PdM context 4.0. Specifically, we explore use various ML models, Support Vector Machines (SVMs) Random Forests (RFs), DL architectures like Convolutional Neural Networks (CNNs) Recurrent (RNNs), which been effectively oriented analyses complex industrial data. Initially, study examines progress algorithms accelerate identification by leveraging data from system operations, sensors, historical trends. AI-enabled rapidly detects irregularities discerns fundamental causes, thereby minimizing downtime enhancing reliability efficiency. Furthermore, this underscores adoption AI-driven approaches, emphasizing prognostics predict Remaining Useful Life (RUL) machinery. capability allows strategic scheduling activities, optimizing resource use, prolonging lifespan expensive assets, refining management spare parts inventory. The tangible advantages employing showcased through case authentic implementations. highlights successful implementations, documenting real-world challenges such as integration issues interoperability, elaborates on strategies deployed navigate these obstacles. results demonstrate improved operational cost savings shed light pragmatic considerations solutions MAS applications. also navigates prospective research avenues applying domain setting stage ongoing innovation exploration transformative domain.

Язык: Английский

---

Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive Maintenance Framework

Sensors, Год журнала: 2024, Номер 24(24), С. 7918 - 7918

Опубликована: Дек. 11, 2024

Sensor networks generate vast amounts of data in real-time, which challenges existing predictive maintenance frameworks due to high latency, energy consumption, and bandwidth requirements. This research addresses these limitations by proposing an edge-cloud hybrid framework, leveraging edge devices for immediate anomaly detection cloud servers in-depth failure prediction. A K-Nearest Neighbors (KNNs) model is deployed on detect anomalies reducing the need continuous transfer cloud. Meanwhile, a Long Short-Term Memory (LSTM) analyzes time-series analysis, enhancing scheduling operational efficiency. The framework’s dynamic workload management algorithm optimizes task distribution between resources, balancing usage, consumption. Experimental results show that approach achieves 35% reduction 28% decrease 60% usage compared cloud-only solutions. framework offers scalable, efficient solution real-time maintenance, making it highly applicable resource-constrained, data-intensive environments.

Язык: Английский

---