Опубликована: Янв. 1, 2024
Язык: Английский
ACM Transactions on Embedded Computing Systems, Год журнала: 2025, Номер unknown
Опубликована: Апрель 24, 2025
With the advent of Industrial 4.0 and push towards Industry 5.0, data generated by industries have become surprisingly large. This abundance significantly boosts machine deep learning models for Predictive Maintenance (PdM). The PdM plays a vital role in extending lifespan industrial equipment machines while also helping to reduce risk unscheduled downtime. Given its multidisciplinary nature, field has been approached from many different angles: this comprehensive survey aims provide an up-to-date overview focused on all learning-based strategies, discussing weaknesses strengths. is based Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) methodological flow, allowing systematic complete review literature. In particular, firstly, we explore main used PdM, mainly Convolutional Neural Networks (ConvNets), Autoencoders (AEs), Generative Adversarial (GANs), Transformers, giving newest such as diffusion foundation models. Then, discuss paradigms applied i.e. , supervised, unsupervised, ensemble, transfer, federated, reinforcement learning. Furthermore, work discusses pipeline data-driven benefits, practical applications, datasets, benchmarks. addition, evaluation metrics each stage state-of-the-art hardware devices are discussed. Finally, challenges future presented.
Язык: Английский
Процитировано
1
PeerJ Computer Science, Год журнала: 2024, Номер 10, С. e1943 - e1943
Опубликована: Апрель 22, 2024
Background Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order improve maintenance processes. PdM technologies have capacity significantly profitability, safety, sustainability various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical efficacy conjunction with DT development. This study underscores DT, exploring transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields healthcare, utilities (smart water management), agriculture farm), aligning latest frontiers these areas. Methodology Employing Preferred Reporting Items Systematic Review Meta-Analyses (PRISMA) criteria, this highlights diverse modeling techniques shaping asset lifetime evaluation within context from 34 scholarly articles. Results The revealed four important findings: modelling their approaches, predictive outcomes, implementation management. These findings align ongoing exploration applications farm). In addition, sheds light on functions emphasising extraordinary ability drive revolutionary change dynamic challenges. results highlight methodologies’ flexibility many industries, providing vital insights revolutionise management practice Conclusions Therefore, systematic review provides current essential resource academics, practitioners, policymakers refine strategies expand applicability sectors.
Язык: Английский
Процитировано
6
Sustainability, Год журнала: 2025, Номер 17(8), С. 3312 - 3312
Опубликована: Апрель 8, 2025
Traditional HVAC designs often struggle to respond promptly and accurately dynamic changes in complex environments like hospital usage. This paper introduces a novel framework that integrates Building Information Modeling (BIM), digital twin technology, practical medical processes transform design for construction. The ensured smarter (with reduction of 90% calculation time an improvement 38.20–53.24% respondence speed) cleaner environment after identifying calculating the rational layout functional areas optimizing intersecting flow lines. A key innovation this research was application Support Vector Machine (SVM) deep learning algorithm (Long Short-Term Memory) networks real-time pedestrian traffic prediction. implementation validated through multiple simulations applications including horizontal vertical negative pressure analyses three distinct departments. findings underline potential BIM twins optimize systems design, providing adaptive, data-driven solutions both routine operations emergency scenarios. offers scalable approach modernizing healthcare infrastructure, ensuring resilience efficiency diverse operational contexts.
Язык: Английский
Процитировано
0
Smart Cities, Год журнала: 2025, Номер 8(2), С. 66 - 66
Опубликована: Апрель 10, 2025
This article explores the integration of Maintenance 4.0 into HVAC (heating, ventilation, and air conditioning) systems, highlighting its essential role within framework Industry 4.0. utilizes advanced technologies such as artificial intelligence IoT sensing technologies. It also incorporates sophisticated data management techniques to transform maintenance strategies indoor ventilation systems. These innovations work together enhance energy efficiency, quality, overall system performance. The paper provides an overview various frameworks, discussing sensors in real-time monitoring environmental conditions, equipment health, consumption. highlights how AI-driven analytics, supported by data, enable predictive fault detection. Additionally, identifies key research gaps challenges that hinder widespread implementation 4.0, including issues related model interpretability, integration, scalability. proposes solutions address these challenges, techniques, explainable AI models, robust strategies, user-centered design approaches. By addressing gaps, this aims accelerate adoption contributing more sustainable, efficient, intelligent built environments.
Язык: Английский
Процитировано
0
Smart Cities, Год журнала: 2025, Номер 8(2), С. 65 - 65
Опубликована: Апрель 10, 2025
Accurately forecasting load profiles, especially peak catching, is a challenge due to the stochastic nature of consumption. In this paper, we applied following three models for forecasting: Long Short-Term Memory (LSTM); Support Vector Regression (SVR); and combined model, which blend SVR, Gated Recurrent Units (GRU), Linear (LR) forecast 24 h-ahead profiles. Household (HH), heat pump (HP), electric vehicle (EV) loads are singular, these were collectively considered with one-year This study tackles issue accurately profiles by evaluating LSTM, an ensemble model predicting energy consumption in HH, HP, EV loads. A novel correction mechanism introduced, adjusting forecasts every eight hours increase reliability. The findings highlight potential deep learning enhancing demand forecasting, identifying loads, contributes more stable efficient grid operations. Visual validation data investigated, along models’ performances at different levels, such as off-peak, on-peak, entirely. Among all models, LSTM performed slightly better most factors, particularly capturing. However, blended showed performance than power on-peak mean absolute percentage error (MAPE) 21.45%, compared 29.24% 22.02% SVR respectively. Nevertheless, visual analysis clearly strong ability capture peaks. was also shown symmetric (SMAPE) during period, around 3–5% improvement model. Finally, employed day-ahead using measured from four grids high capturing peaks MAPE values less 10% grids.
Язык: Английский
Процитировано
0
Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115781 - 115781
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0
Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 34 - 47
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
2
Applied Sciences, Год журнала: 2024, Номер 14(15), С. 6587 - 6587
Опубликована: Июль 28, 2024
With the rapid advancement in Building Information Modelling (BIM) technology to strengthen and Construction (B&C) industry, effective methods are required for analysis improvement of as-built BIM, which reflects completed building project captures all deviations updates from initial design. However, most existing studies focused on as-designed while inspection BIM rely labour-intensive visual manual approaches that overlook interdependent relationships among components. To address these issues, we propose a network analysis-based approach managing improving BIM. Networks generated geometric attributes extracted Industry Foundation Classes (IFC) documents, analytical techniques applied facilitate analysis. In addition, practical dataset is utilised verify feasibility proposed approach. The results demonstrate our method significantly enhances comparison model matching. Specifically, innovative contribution leverages global information relations, providing more comprehensive understanding management optimisation. Our findings suggest can serve as powerful tool structure asset B&C offering new perspectives methodologies comparison. Finally, detailed discussion future suggestions presented.
Язык: Английский
Процитировано
1
Materials, Год журнала: 2024, Номер 17(7), С. 1575 - 1575
Опубликована: Март 29, 2024
Flexible La-doped Sm2Zr2O7/polyurethane (PU) coated leather composites were synthesized using a one-step hydrothermal method, with highly efficient photocatalytic degradation properties by coating the Sm2Zr2O7/PU emulsion onto and drying it. The phase composition optical of as-prepared material systematically characterized. result revealed that La was doped in Sm2Zr2O7 successfully, prepared samples still possessed pyrochlore structure. absorption edge exhibited red-shift increase doping, indicating doping could broaden absorbance range materials. catalytic performance composite on studied Congo red solution as target pollutant. results showed best property found 5% nanomaterial at concentration 3 g/L. resulting high specific surface area 73.5 m2/g. After 40 min irradiation 450 W xenon lamp, rate reached 93%. Moreover, after coating, obviously improved mechanical properties, tensile strength increased from 6.3 to 8.4 MPa. enhanced hold promising applications treatment indoor volatile organic compounds.
Язык: Английский
Процитировано
1
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
0