با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
Unbalance evaluation of a scaled wind turbine under different rotational regimes via detrended fluctuation analysis of vibration signals combined with pattern recognition techniques
ارزیابی عدم تعادل یک توربین بادی مقیاس پذیر تحت رژیمهای مختلف چرخش از طریق تجزیه و تحلیل نوسانات آشکار سیگنال های لرزش همراه با تکنیک های تشخیص الگو-2019 This work aims to propose a different approach to evaluate the operating conditions of a scaled wind
turbine through vibration analysis. The turbine blades were built based on the NREL S809 profile and a
40-cm diameter, while the design blade tip speed ratio (l) is equal to 7. Masses weighing 0.5, 1.0, and
1.5 g were added to the tip of one or two blades in a varying sequence with the intent of simulating
potential problems and producing several scenarios from simple imbalances to severe rotor vibration
levels to be compared to the control condition where the three blades and the system were balanced. The
signals were processed and classified by a combination of detrended fluctuation analysis with Karhunen-
Loeve Transform, Gaussian discriminator, and Artificial Neural Network, which are pattern recognition
techniques with supervised learning. Good results were achieved by employing the above cited recognition
techniques as more than 95% of normal and imbalanced cases were correctly classified. In a
general way, it was also possible to identify different levels of blade imbalance, thus proving that the
present approach may be an excellent predictive maintenance tool for vibration monitoring of wind
turbines. Keywords: Machine learning | Signal processing | Fault detection | Condition monitoring | Non-stationary vibration | Condition based maintenance |
مقاله انگلیسی |
2 |
Intelligent fault diagnosis of cooling radiator based on deep learning analysis of infrared thermal images
تشخیص خطای هوشمند رادیاتور خنک کننده بر اساس تجزیه و تحلیل یادگیری عمیق از تصاویر حرارتی مادون قرمز-2019 Detection of faults and intelligent monitoring of equipment operations are essential for modern industries.
Cooling radiator condition is one of the factors that affects engine performance. This paper proposes a novel and
accurate radiator condition monitoring and intelligent fault detection based on thermal images and using a deep
convolutional neural network (CNN) which has a specific configuration to combine the feature extraction and
classification steps. The CNN model is constructed from VGG-16 structure that is followed by batch normalization
layer, dropout layer, and dense layer. The suggested CNN model directly uses infrared thermal images as
input to classify six conditions of the radiator: normal, tubes blockage, coolant leakage, cap failure, loose
connections between fins & tubes and fins blockage. Evaluation of the model demonstrates that leads to results
better than traditional computational intelligence methods, such as an artificial neural network, and can be
employed with high performance and accuracy for fault diagnosis and condition monitoring of the cooling
radiator under various working circumstances. Keywords: Cooling radiator | Fault detection | Thermal image analysis | Deep learning | Convolutional neural network |
مقاله انگلیسی |
3 |
Robust on-line diagnosis tool for the early accident detection in nuclear power plants
ابزار تشخیص آنلاین قوی برای تشخیص زود هنگام تصادف در نیروگاه های هسته ای-2019 Any loss of coolant accident mitigation strategy is necessarily bound by the promptness of the break detection as
well as the accuracy of its diagnosis. The availability of on-line monitoring tools is then crucial for enhancing
safety of nuclear facilities. The requirements of robustness and short latency implied by the necessity for fast and
effective actions are undermined by the challenges associated with break prediction during transients.
This study presents a novel approach to tackle the challenges associated with the on-line diagnostics of loss of
coolant accidents and the limitations of the current state of the art. Based on the combination of a set of artificial
neural network architectures through the use of Bayesian statistics, it allows to robustly absorb different sources
of uncertainty without requiring their explicit characterization in input. It provides the quantification of the
output confidence bounds but also enhances of the model response accuracy. The implemented methodology
allows to relax the need for model selection as well as to limit the demand for user-defined analysis parameters.
A numerical case-study entailing a 220 MWe heavy-water reactor is analysed in order to test the efficiency of the
developed computational tool. Keywords: LOCA | Neural networks | Pattern recognition | Bayesian statistics | Fault diagnostics | On-line condition monitoring |
مقاله انگلیسی |
4 |
A deep learning approach to condition monitoring of cantilever beams via time-frequency extended signatures
یک رویکرد یادگیری عمیق برای نظارت بر وضعیت پرتوهای کانسیلر از طریق امضاهای طولانی با فرکانس زمان-2019 We introduce with this work a deep learning approach for non-invasive condition monitoring of
cantilever beams. The deep learning classifier is used to recognize a damaged or undamaged beam
via time-frequency extended signatures. These signatures are the distributions over several
measurements of the natural frequencies extracted from the refined time-frequency adaptive
spectrum of vibrating beams. The test results showed that we are able to cancel ambient effects like
the temperature and to obtain a high accuracy of the results which for the considered cases reach
100%. Keywords: Damage detection | Time-frequency spectrum | Natural frequency | Deep learning | Signatures |
مقاله انگلیسی |
5 |
Machine learning based concept drift detection for predictive maintenance
مفهوم یادگیری ماشین مبتنی بر تشخیص رانش برای تعمیر و نگهداری پیشگویانه-2019 In this work we present a machine learning based approach for detecting drifting behavior – so-called concept
drifts – in continuous data streams. The motivation for this contribution originates from the currently intensively
investigated topic Predictive Maintenance (PdM), which refers to a proactive way of triggering servicing actions
for industrial machinery. The aim of this maintenance strategy is to identify wear and tear, and consequent
malfunctioning by analyzing condition monitoring data, recorded by sensor equipped machinery, in real-time.
Recent developments in this area have shown potential to save time and material by preventing breakdowns and
improving the overall predictability of industrial processes. However, due to the lack of high quality monitoring
data and only little experience concerning the applicability of analysis methods, real-world implementations of
Predictive Maintenance are still rare. Within this contribution, we present a method, to detect concept drift in
data streams as potential indication for defective system behavior and depict initial tests on synthetic data sets.
Further on, we present a real-world case study with industrial radial fans and discuss promising results gained
from applying the detailed approach in this scope. Keywords: Predictive maintenance | Machine learning | Concept drift detection | Time series regression | Industrial radial fans |
مقاله انگلیسی |
6 |
PWR heat exchanger tube defects: Trends, signatures and diagnostic techniques
نقص لوله مبدل حرارتی PWR: روند ، امضاها و تکنیک های تشخیصی-2019 In pressurized water reactor, one of the most important barriers to the release of radioactive material to the
environment is the steam generator heat exchanger tubes. Degradation of steam generator tubes could result in
increased primary-to-secondary leakage and delayed diagnosis of the event could eventually result in throughwall
tube rupture. To prevent through-wall tube rupture, an effective trend monitor is essential. Moreover, to
increase the cost-effectiveness and reliability of energy production from nuclear plants, reduction in maintenance
and repair downtime caused by steam generator tube defects is crucial.
This paper reviews the state-of-the-art diagnostic techniques and condition monitoring methods for heat
exchanger tubes. In particular, it discusses steam generator tube degradation and integrity issues, the physical
phenomena, and the analysis of theoretical and experimental research conducted in the past few decades on the
inspection of steam generator tubes. In addition, plants’ monitoring systems are categorized and the predictive
models utilized for the monitoring and evaluation of water chemistry is discussed. The major contribution is the
presentation of critical parameter trends, deviations and empirical signatures observed when steam generator
tube rupture occurs in a fully functional CNP1000 pressurized water reactor, and the analysis of machine
learning approach for rupture event diagnosis. Furthermore, the operator response to thermal-hydraulic parameter
trend indicating cracks and incipient leaks in steam generator tubes, as well as comparative advantages
and demerits of water chemistry monitors are also presented. Finally, possible future research focus and likely
challenges are discussed and a technique appropriate for effective online diagnosis of increased primary-tosecondary
leakage event is recommended. Keywords: Heat exchanger non-destructive testing (NDE) | Fault diagnosis | Process online monitoring | Steam generator tube rupture | Water chemistry | Pattern recognition |
مقاله انگلیسی |
7 |
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 |
8 |
استفاده از تستهای فرضی بهعنوان شاخصهای آماری برای تحلیل پاسخ فرکانسی ماشینآلات الکتریکی
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 29 این مقاله شاخص آماری جدیدی مبتنی بر تستهای فرضی را پیشنهاد میدهد تا در تحلیل پاسخ فرکانسی (FRA) ماشینآلات الکتریکی مورداستفاده قرار بگیرد. این شاخص بهویژه در تشخیص ویژگیهای اولیه (که بهعنوان انحرافهای کوچک از طیف پایه پدیدار میشوند) جالبتوجه هستند حتی وقتیکه اندازهگیریها دارای تغییرپذیری زیادی هستند (که نمونه آن اندازهگیریهای آنلاین در ماشینهای در حال چرخش است). بخش اعظمی از شاخصهای پیشنهادی که در ادبیات و مقالات مربوط به FRA بسیار پیشنهاد دادهشده است تنها دو طیف را در یکزمان مقایسه میکند و داده را از اندازهگیریها در فرکانسهای مختلف ترکیب میکند. شاخص پیشنهادی در این مقاله تنها یک تست فرضی را برای هر فرکانس (بهمنظور اطمینان از تصادفی بودن، استقلال و نرمال بودن) در طیف حاصل از چندین اندازهگیری (بهمنظور در نظر گرفتن تغییرپذیری بر اساس اندازهگیری) را محاسبه میکند و مجموع نرمالیزه شده تستهای انفرادی را اجرا میکند. نتایج تجربی بر اساس مولد سنکرون (آنلاین و آفلاین) برای اعتبارسنجی شاخص پیشنهادی ارائهشدهاند.
کلمات کلیدی: ماشینهای AC | نظارت بر شرایط | تحلیل پاسخ فرکانسی | تست ایزوله سازی ماشین در حال چرخش |
مقاله ترجمه شده |
9 |
رویکردهای حسگر فشرده برای نظارت بر وضعیت و تجسم آسیب مبتنی بر اسکن لیزر
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 15 نظارت بر وضعیت (CM) و تست غیر تخریبی با مشکل دادههای بزرگ مواجه میشوند زیرا نیاز دارند که به طور مداوم دادههای موج یا ارتعاش را با سرعت نمونه گیری بالا بسنجند. در این مقاله رویکردهای حسگر فشرده برای هر دو طرح نظارت بر شرایط و تست غیرتخریبی پیشنهاد شدهاند تا به طور موثر میزان زیادی داده را مدیریت کنند و همچنین قابلیت تشخیص آسیب فرآیند فعلی را بهبود ببخشند. حسگر فشرده یک پارادایم حسگر/نمونه برداری جدید است که در مقایسه با روشهای نمونه گیری سنتی، تعداد نمونههای بسیار کمتری را در نظر میگیرد. برای آزمایشات CM، یک سیستم چرخشی ساخته شده استفاده میشود و تمامی دادهها به طور فشرده نمونهگیری میشوند تا دادههای فشرده ایجاد شوند. ویژگیهای سیگنالهای بهینه بدون فرآیند بازسازی استخراج شده و برای تشخیص و طبقه بندی آسیب استفاده شدهاند. همچنین تست غیرمخرب با استفاده از اسکن فشرده و سیستم لرزش گیر لیزر دوپلر (LDV)مجهز به دستگاه کج آینه انجام شد. فیلدهای موج با اسکن یک الگوی تصادفی و فشرده در مقیاس فضایی انجام شد و فیلدهای موج کامل از دادههای فشردهی اندازه گیری شده بدست آمدند. سپس منطقهی آسیب شناسایی شده و با استفاده از پردازش سیگنال مبتنی بر عدد موج تجسم سازی شد. نتایج تجربی نشان داد که روش پیشنهادی میتواند به طور موثر سرعت پردازش داده و دقت تشخیص نظارت بر وضعیت و تست غیرمخرب را بهبود بخشد.
کلمات کلیدی: حسگر فشار | نظارت بر وضعیت | تست غیرتخریبی | ارتعاش سنج لیزری داپلر | شناسایی خسارت و آسیب |
مقاله ترجمه شده |
10 |
Influence of rotor position on the repeatability of frequency response analysis measurements on rotating machines and a statistical approach for more meaningful diagnostics
تاثیر موقعیت روتور در قابلیت تکرار اندازه گیری تجزیه و تحلیل پاسخ فرکانسی بر روی ماشین های دوار و یک روش آماری برای تشخیص معنی دار تر-2016 This work presents an investigation on the influence of rotor position on the Frequency Response Analysis (FRA) of electric machines. Different types of machines have been analyzed. Contrary to common
belief, not only the salient-pole machine suffered from rotor position influence on the FRA. This can have
severe impact on the repeatability of the tests and, consequently, their ability to identify early damage
in the insulation system of the machine. This paper is intended to warn practitioners of FRA that care
should be taken while analyzing the results, in order to avoid false positives in their measurements. Recommendations are made aiming to avoid the influence of rotor position on the results. Also, the use of
statistical techniques is proposed, in order to improve the diagnosis, even when there is some lack of
repeatability.
Keywords: AC machines | Condition monitoring | Frequency response analysis | Rotating machine insulation testing |
مقاله انگلیسی |