دانلود و نمایش مقالات مرتبط با Structural health monitoring::صفحه 1
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نتیجه جستجو - Structural health monitoring

تعداد مقالات یافته شده: 9
ردیف عنوان نوع
1 A generalizable deep learning framework for localizing and characterizing acoustic emission sources in riveted metallic panels
یک چارچوب یادگیری عمیق قابل تعمیم برای محلی سازی و توصیف منابع انتشار صوتی در پانل های فلزی پرچین-2019
This paper introduces a deep learning-based framework to localize and characterize acoustic emission (AE) sources in plate-like structures that have complex geometric features, such as doublers and rivet connections. Specifically, stacked autoencoders are pre-trained and utilized in a two-step approach that first localizes AE sources and then characterizes them. To achieve these tasks with only one AE sensor, the paper leverages the reverberation patterns, multimodal characteristics, and dispersive behavior of AE waveforms. The considered waveforms include AE sources near rivet connections, on the surface of the plate-like structure, and on its edges. After identifying AE sources that occur near rivet connections, the proposed framework classifies them into four source-to-rivet distance categories. In addition, the paper investigates the sensitivity of localization results to the number of sensors and compares their localization accuracy with the triangulation method as well as machine learning algorithms, including support vector machine (SVM) and shallow neural network. Moreover, the generalization of the deep learning approach is evaluated for typical scenarios in which the training and testing conditions are not identical. To train and test the performance of the proposed approach, Hsu-Nielsen pencil lead break tests were carried out on two identical aluminum panels with a riveted stiffener. The results demonstrate the effectiveness of the deep learning-based framework for singlesensor, AE-based structural health monitoring of plate-like structures.
Keywords: Acoustic emission | Deep learning | Edge reflection | Reverberation patterns | Plate-like structures | Pattern recognition | Stacked autoencoders | Guided ultrasonic waves | Machine learning | Structural health monitoring
مقاله انگلیسی
2 Data interpretation framework integrating machine learning and pattern recognition for self-powered data-driven damage identification with harvested energy variations
چارچوب تفسیر داده ها ادغام یادگیری ماشین و شناخت الگوی برای شناسایی آسیب خود محور داده با تغییرات انرژی برداشت شده-2019
Data mining methods have been widely used for structural health monitoring (SHM) and damage identification for analysis of continuous signals. Nonetheless, the applicability and effectiveness of these techniques cannot be guaranteed when dealing with discrete binary and incomplete/missing signals (i.e., not continuous in time). In this paper a novel data interpretation framework for SHM with noisy and incomplete signals, using a through-substrate self-powered sensing technology, is presented within the context of artificial intelligence (AI). AI methods, namely, machine learning and pattern recognition, were integrated within the data interpretation framework developed for use in a practical engineering problem: data-driven SHM of platelike structures. Finite element simulations on an aircraft stabilizer wing and experimental vibration tests on a dynamically loaded plate were conducted to validate the proposed framework. Machine learning algorithms, including support vector machine, k-nearest neighbor, and artificial neural networks, were integrated within the developed learning framework for performance assessment of the monitored structures. Different levels of harvested energy were considered to evaluate the robustness of the SHM system with respect to such variations. Results demonstrate that the SHM methodology employing the proposed machine learning-based data interpretation framework is efficient and robust for damage detection with incomplete and sparse/missing binary signals, overcoming the notable issue of energy availability for smart damage identification platforms being used in structural/infrastructure and aerospace health monitoring. The present study aims to advance data mining and interpretation techniques in the SHM domain, promoting the practical application of machine learning and pattern recognition with incomplete and missing/sparse signals in smart cities and smart infrastructure monitoring.
Keywords: Structural health monitoring | Machine learning | Low-rank matrix completion | Pattern recognition | Self-powered sensors | Plate-like structures | Incomplete signals | Energy harvesting
مقاله انگلیسی
3 Data mining methodology employing artificial intelligence and a probabilistic approach for energy-efficient structural health monitoring with noisy and delayed signals
روش داده کاوی با استفاده از هوش مصنوعی و یک رویکرد احتمالی برای نظارت بر سلامت ساختاری کارآمد با انرژی با سیگنال های پر سر و صدا و تأخیر-2019
Numerous methods have been developed in the context of expert and intelligent systems for structural health monitoring (SHM) with wireless sensor networks (WSNs). However, these techniques have been proven to be efficient when dealing with continuous signals, and the applicability of such expert sys- tems with discrete noisy signals has not yet been explored. This study presents an intelligent data min- ing methodology as part of an expert system developed for SHM with noisy and delayed signals, which are generated by a through-substrate self-powered sensor network. The noted sensor network has been demonstrated as an effective means for minimizing energy consumption in WSNs for SHM. Experimen- tal vibration tests were conducted on a cantilever plate to evaluate the developed expert system for SHM. The proposed data mining method is based on the integration of pattern recognition, an innova- tive probabilistic approach, and machine learning. The novelty of the proposed system for SHM with data interpretation methodology lies in the integration of the noted intelligent techniques on discrete, binary, noisy, and delayed patterns of signals collected from self-powered sensing technology in the applica- tion to a practical engineering problem, i.e., data-driven energy-efficient SHM. Results confirm that the proposed data mining method employing a probabilistic approach can be effectively used to reconstruct delayed and missing signals, thereby addressing the important issue of energy availability for intelligent SHM systems being used for damage identification in civil and aerospace structures. The applicability and effectiveness of the expert system with the data mining approach in detecting damage with noisy sig- nals was demonstrated for plate-like structures with an accuracy of 97%. The present study successfully contributes to advance data mining and signal processing techniques in the SHM domain, indicating a practical application of expert and intelligent systems applied to damage detection in SHM platforms. Findings from this research pave a way for development of the data analysis techniques that can be em- ployed for interpreting noisy and incomplete signals collected from various expert systems such as those being used in intelligent infrastructure monitoring systems and smart cities.
Keywords: Structural health monitoring | Data mining | Artificial intelligence| Probabilistic approach | Signal time delay
مقاله انگلیسی
4 Big Data Reduction for a Smart Citys Critica Infrastructural Health Monitoring
کاهش داده های بزرگ برای شهرهای هوشمند بحرانی نظارت بر زیرساخت بهداشت-2018
Critical infrastructure monitoring is one of the most important applications of a smart city. The objective is to monitor the integrity of the struc tures (e.g., buildings, bridges) and detect and pinpoint the locations of possible events (e.g., damages, cracks). Regarding today’s complex structures, collecting data using wireless sen sor data over extensive vertical lengths creates enormous challenges. With a direct BS deploy ment, a big amount of data will accumulate to be relayed to the BS. As a result, traditional models and schemes developed for health monitoring are largely challenged by low-cost, quality-guaran teed, and real-time event monitoring. In this arti cle, we propose BigReduce, a cloud based health monitoring application with an IoT framework that could cover most of the key infrastructures of a smart city under an umbrella and provide event monitoring. To reduce the burden of big data processing at the BS and enhance the quality of event detection, we integrate real-time data pro cessing and intelligent decision making capabili ties with BigReduce. Particularly, we provide two innovative schemes for health event monitoring so that an IoT sensor can use them locally; one is a big data reduction scheme, and the other is a decision making scheme. We believe that BigRe duce will result in a remarkable performance in terms of data reduction, energy cost reduction, and the quality of monitoring.
Keywords: Big Data, cloud computing, condition monitoring, critical infrastructures, data reduction, decision making, Internet of Things, public dministration,smart cities, wireless sensor networks
مقاله انگلیسی
5 The Strip Clustering Scheme for data collection in large-scale Wireless Sensing Network of the road
طرح خوشه بندی نوار برای جمع آوری داده ها در شبکه های حسگر بیسیم مقیاس بزرگ جاده ها -2017
For intelligent traffic and road structural health monitoring, Wireless Sensing Network has been applied widely in transportation, and large quantity of sensor nodes have been embedded in roadways. Now the service lives of sensors are limited mainly because of their battery power storage. So the life cycle of the whole network can be extended if the service life of each sensor in the network is prolonged. In this paper, the Strip Clustering Scheme (SCS) is proposed to replace the Conventional Scheme (CS). This method includes region division, cluster head node selection, link construction, data fusion and transmission. Adopting SCS can reduce a lot of redundant data and the total energy consumption of the network by data fusion. In addition, adopting SCS can also extend the monitoring area without increasing the communication range of the Access Point (AP), and facilitate further expansion of the network as a result. Based on the numerically simulated results, CS method can be used for the WSN within 75 m, and SCS method is more suitable when the monitoring range is larger than 75 m. To achieve the optimal network costs and the network life cycle by using SCS, the range of d (the longitudinal spacing of adjacent nodes), is suggested as 10–12.5 m and the energy of cluster head nodes is 60% higher than the energy of non-head nodes with fixed w (the transverse distance of adjacent nodes). And the extra energy of head nodes can be obtained by adding the number of battery within the head nodes or using renewable energy technologies.
Keywords: WSN | Road | Energy consumption | Conventional Scheme | Strip Clustering Scheme
مقاله انگلیسی
6 Experimental validation of cost-effective vision-based structural health monitoring
اعتبار تجربی از لحاظ نظارت بهداشتی مبتنی بر دید مبتنی بر مقرون به صرفه-2017
Monitoring structural displacement responses can provide quantitative information for both structural safety evaluations and maintenance purposes. To overcome the limitations of conventional displacement sensors, advanced noncontact vision-based systems offer a promising alternative. This study validates the potentials of the vision displacement sensor for cost-effective structural health monitoring. The results of laboratory experiments on simply-supported beam structures demonstrate the high accuracy of the vision sensor for dense full-field displacement measurements. The identified natural frequencies and mode shapes from measurements by using one camera match well with those from an array of accelerometers. Moreover, the smoother mode shapes make possible the noncontact damage detection based on the conven- tional mode shape curvature index. This study also discusses the issues concerning the practical applications of the vision displacement sensors, such as the scaling factor determination, measurement with small camera tilt angles, tradeoffs between the measurement resolution and measurement points or field of view, etc. Furthermore, the remote, real-time and multi-point measurement capacities of the vision sensor are confirmed through field tests of Manhattan Bridge during train passing.
Keywords:Vision-based displacement sensor | Displacement measurement | Structural health monitoring | Damage detection | Modal analysis
مقاله انگلیسی
7 ارزیابی عیب برای بررسی سلامت ساختاری، یافتن عیب، جلوگیری از شکست و پیش بینی
سال انتشار: 2016 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 11
در این مقاله، دو موضوع نگه داری بر حسب شرایط و بررسی سلامت ساختار و پیش بینی ها توضیح داده شده است. شناسایی و تخمین عیوب قدمی بسیار مهم و لازم در زمینه نگه داری بر حسب شرایط است. در این مقاله، آزمایشی به وسیله تجهیزات سفارشی یک تست با ایجاد نواقصی بر روی دوایر بیرونی و درونی یاتاقان غلتکی انجام شده است.در این مقاله همچنین، ارتباط بین اطلاعات بدست آمده از آزمایش ارتعاشات و رابطه بین نواقص ایجاد شده، بدست آمده است. هنگامی که آزمایش بر روی تجهیزات تست طراحی شده برای پیش بینی عیوب انجام شد، روش تحلیلی انتقال موجی نشان داد که ابزار موثری برای تحلیل سیگنال ارتعاشات است. در این مقاله، که توسط روش چگالی طیف قدرت تبعیت می شود، بر روی سیگنال های ارتعاشات ناشی از یک یاتاقان غلتکی معیوب اعمال شده است. پس از یافتن عیب موجود در یاتاقان، مکان و شدت آن، عمر مفید باقی مانده یاتاقان تخمین زده می شود.
کلمات کلیدی: نظارت بر سلامت سازه | تجزیه و تحلیل داده های ارتعاش | تحول موج تحلیلی | باقی مانده عمر مفید
مقاله ترجمه شده
8 طراحي مشترك فيزيكي-سايبر مانيتورينگ سلامت ساختاری توزيع شده با شبكه هاي حسگر یی سیم
سال انتشار: 2014 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 35
زيرساخت مدني رو به زوال ما، با چالش هاي بحراني مانيتورينگ سلامت ساختاری درازمدت براي تشخيص آسيب و مكان سنجي مواجه است. بر خلاف تحقيقات موجود كه اغلب طراحي هاي شبكه هاي سنسور وايرلس و الگوريتم هاي مهندسي ساختماني را از يكديگر تفكيك مي كنند، اين مقاله يك روش طراحي مشترك ساير-فيزيكي را براي مانيتورينگ سلامت ساختاری بر مبناي شبكه هاي سنسور وايرلس پيشنهاد مي كند. روش ما موضوعات زير را پوشش ميدهد: 1) روش هاي مكان سنجي اسيب برپايه انعطاف پذيري كه امكان ارزيابي و مقايسه بين تعداد سنسورها و تحليل و تجزيه مكان سنجي آسيب را فراهم مي نمايد و 2) يك نوع ساختار محاسباتي چند سطحي و كم مصرف از لحاظ انرژي بخصوص طراحي شده براي بكارگيري مشخصخه چند تحليل و تجزيهي روش بر مبناي انعطاف پذيري. روش پيشنهادي روي پلتفرم Intel Imote2 اجرا شد. آزمايش روي ساختمان تراس شبيه سازي شده و ساختمان تراس واقعي در مقياس كامل، راندمان سيستم ها را در مكان سنجي آسيب و راندمان انرژي به اثبات رساند.
كلمات كليدي: شبكه هاي حسگر بی سیم | مانيتورينگ سلامت ساختاری | سيستم هاي سايبر-فيزيكي
مقاله ترجمه شده
9 تکنیک های اندازه گیری غیر تماسی برای نظارت بر ساختار سلامتی
سال انتشار: 2014 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 14
استفاده از نوسان سنجی لیزر اسکن کننده سه بعدی برای تصویرسازی انتشار امواج تنشی در نمونه هایی که نفوذ غیر سطحی در انها با عیوبی همراه است در این مطالعه ارایه شده است . نتایج ،نیازمندی به درک بهترِ معیارهای سنجش سرعت نوسانات سه بعدی را بمنور دستیابی به بینش بهتر در مورد انتشار موج تنشی در نواحیی تحت نظر تایید میکنند . در ادامه نشان داده خواهد شد که دانش کامل در مورد سرعت ،درکِ متقابل از انواع حالتهای مختلف موج که در ساختار خود با عیوبی روبرو هستند را تسهیل خواهد بخشید . بمنظور تحقق بخشیدن به اهداف این مطالعه ، از یک صفحه الومینیمی با داشتن ناپیوستگی در نفوذ غیر سطحی برای نشان دادن کاربرد این شکل از تکنیک های سنجشِ غیر تماسی برای انتشار امواج تنشی استفاده نمودیم . نشان داده خواهد شد که محتوای چند کیفیتی و زمان پیشرفت برخورد و حالتهای پراکندگی موج را میتوان با این نوع تکنیکِ سنجش مورد ارزیابی قرار داد .
واژگان کلیدی : موج لمب یا امواج سطحی فراصوت | PZT | نوسان سنجی لیزر سه بعدی | آسیب | حفره عمقی جزیی | تجسم | نظارت بر سلامت ساختاری .
مقاله ترجمه شده
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