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1 |
3D pattern identification approach for cooling load profiles in different buildings
روش شناسایی الگوی سه بعدی برای خنک کردن پروفایل بار در ساختمانهای مختلف-2020 Building energy conservation has gained increasing concern owing to its large portion of energy consumption
and great potential of energy saving. In-depth understanding of representative patterns of daily cooling load
profile will facilitate effective building energy system scheduling, fault detection and diagnosis, as well as demand
and supply side management. In this study, a novel three-stage approach is proposed for pattern identification
of cooling load profiles in different types of buildings. The three stages include data preparation, data
clustering and data visualization. The initial measurement in the building energy management system is conducted
at the time step of 15 min. To further explore the characteristics of the building cooling load trend, 1-h
mean pattern, 4-h mean pattern and daily statistical information (i.e. average, minimum and maximum values)
of cooling load are also adopted for data clustering, respectively. To test the generality and robustness of the
proposed approach, one-year historical measurement data collected from the practical chilled water system in
two different buildings are adopted, respectively. The analysis demonstrates that the 3D pattern identification
approach can effectively discover the representative characteristics of the daily cooling load profiles in both
buildings. It is also expected that the proposed 3-stage pattern identification approach is general in adoption and
can be potentially adopted in various types of buildings in different climate zones. Keywords: Pattern identification | Gaussian mixture model clustering | Cooling load | Data visualization | Energy management |
مقاله انگلیسی |
2 |
Lithium-ion batteries fault diagnostic for electric vehicles using sample entropy analysis method
عیب یابی باتری های لیتیوم یون برای وسایل نقلیه الکتریکی با استفاده از روش تجزیه و تحلیل آنتروپی نمونه-2020 Fault detection plays a vital role in the operation of lithium-ion batteries in electric vehicles. Typically, during
the operation of battery systems, voltage signals are susceptible to noise interference. In this paper, a novel fault
detection method based on the Empirical Mode Decomposition and Sample Entropy is proposed to identify
battery faults under various operating conditions. Firstly, effective fault features are extracted through the
proposed Empirical Mode Decomposition method by decomposing battery voltage signals and removing the
noise interference during the voltage sampling process. Experiments are conducted to quantitatively illustrate
the fault features extracted by the Empirical Mode Decomposition. Then, based on these extracted fault features,
the Sample Entropy values are calculated to help accurately detect and locate the battery faults. Moreover, an
evaluation strategy of the detected faults is designed to indicate the battery fault level. Finally, the effectiveness
of the proposed approach is verified against real-world data measured from electric vehicles in the presence of
regular and sudden faults. Keywords: Electric vehicles | Lithium-ion batteries | Fault detection | Sample entropy |
مقاله انگلیسی |
3 |
A reinforcement learning based approach for on-line adaptive parameter extraction of photovoltaic array models
یک رویکرد مبتنی بر یادگیری تقویتی برای استخراج پارامتر تطبیقی آنلاین از مدل های آرایه فتوولتائیک-2020 At present, most methods for the fault detection and diagnosis (FDD) of the photovoltaic (PV) array strongly rely
on comparing the on-line measured electrical parameters with the modeled reference ones, which are challenging
the on-line accuracy and time cost of the parameter extraction for modeling the current-voltage (I-V) curves
of the PV array. In this paper, a reinforcement learning (RL) based approach for on-line adaptive parameter
extraction of PV array models is proposed. The model parameters, including the ideality factor, series and shunt
resistance, and the compensated irradiance for the uncalibrated pyranometer, are extracted. Corresponding
environmental states, actions, rewards, and the entire framework for the on-line adaptive parameter extraction
are reasonably designed and investigated. The annual experimental results verify that the proposed RL-based
approach can obtain higher on-line accuracy for modeling the I-V curve of PV array with fast extraction speed,
compared with the conventional meta-heuristic-based approach and the analytical approach for parameter extraction.
The annual experimental results reveal that the proposed approach can guarantee the 50% probability
for obtaining the root mean square error (RMSE) less than 0.1, and 90% probability for obtaining the RMSE less
than 0.25. The average computational time cost of the proposed approach is approximate 38.12 ms. In addition,
the annual trend of extracted model parameters is analyzed. The annual results also show that the series and
shunt resistance have the inverse seasonal trend. Besides, the measurement error of the pyranometer can be
identified statistically. The proposed RL-based approach can also be integrated with the presented on-line FDD
method, which realizes the on-line training of RL agents and the FDD of PV array simultaneously. Keywords: Reinforcement learning | On-line adaptive extraction | PV array | Parameter extraction | Mathematical model |
مقاله انگلیسی |
4 |
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 |
5 |
Research on deep learning in the field of mechanical equipment fault diagnosis image quality
پژوهش بر روی یادگیری عمیق در زمینه کیفیت تصویر تشخیص خطای تجهیزات مکانیکی-2019 Image quality assessment (IQA) is an indispensable technique in computer vision, which is widely
applied in image classification, image clustering. With the development of deep learning, deep neural
network (DNN)-based methods have shown impressive performance. Thus, in this paper, we propose a
novel method for mechanical equipment fault diagnosis based on IQA. More specifically, we first conduct
data acquisition base on our practice. Afterwards, we leverage image processing method for removing
noise. Subsequently, we leverage CNN-based method for image classification. Finally, different mechanical
equipment images will be grouped into different categories and fault detection can be achieved.
Extensive experiments demonstrate the effectiveness and robustness of our method. Keywords: Deep learning | Mechanical equipment | Equipment maintenance | Image quality |
مقاله انگلیسی |
6 |
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 |
7 |
Deep understanding in industrial processes by complementing human expertise with interpretable patterns of machine learning
درک عمیق در فرآیندهای صنعتی با تکمیل تخصص انسانی با الگوهای قابل تفسیر یادگیری ماشین-2019 Experts in industrial processes rely on domain knowledge (DK) repositories to identify the causes of ab- normal situations in order to make appropriate decisions that mitigate the negative effects of such events. These DK repositories need to be enriched and updated continuously as different unexpected events oc- cur. A common causality analysis method in DK repositories is the fault tree analysis (FTA). The major limitation of updating a fault tree is that it requires in-depth system knowledge, which involves a high level of human experience. Data exploitation based on machine learning (ML) can address this limitation by deeply analyzing process historical data to discover hidden phenomena that are difficult for human ex- perts to identify and to analyze. This paper proposes an innovative methodology that combines domain knowledge, in the form of FTA, with additional knowledge extracted by a descriptive ML method called logical analysis of data (LAD). More specifically, LAD is a classification method, which provides as a by- product a set of interpretable rules (patterns) explaining the classification results. The patterns extracted from historical data represent an important and complementary source of knowledge that provides ex- perts with insights and allows them to better understand the process operations. The objective of using these patterns in the proposed methodology is to provide automatic enrichment and updating of exist- ing fault trees in order to achieve accurate fault detection and diagnosis (FDD) in industrial processes. The proposed methodology is demonstrated using fault trees constructed for two different systems in the process industry. The fault tree for each system was updated successfully with minimal effort from process experts. Keywords: Fault detection and diagnosis (FDD) | Logical analysis of data (LAD) | Fault tree analysis (FTA) | Machine learning and pattern recognition | Causality analysis |
مقاله انگلیسی |
8 |
Machine learning-based statistical testing hypothesis for fault detection in photovoltaic systems
فرضیه آزمون آماری مبتنی بر یادگیری ماشین برای تشخیص گسل در سیستم های فتوولتائیک-2019 In this paper, we consider a machine learning approach merged with statistical testing hypothesis for enhanced
fault detection performance in photovoltaic (PV) systems. The developed method makes use of a machine
learning based Gaussian process regression (GPR) technique as a modeling framework, while a generalized
likelihood ratio test (GLRT) chart is applied to detect PV system faults. The developed GPR-based GLRT approach
is assessed using simulated and real PV data through monitoring the key PV system variables (current, voltage,
and power). The computation time, missed detection rate (MDR), and false alarm rate (FAR) are computed to
evaluate the fault detection performance of the proposed approach Keywords: Fault detection | Photovoltaic (PV) systems | Machine learning | Generalized likelihood ratio test (GLRT) | Gaussian process regression (GPR) |
مقاله انگلیسی |
9 |
Valve stiction detection through improved pattern recognition using neural networks
تشخیص استیک دریچه از طریق تشخیص الگوی بهبود یافته با استفاده از شبکه های عصبی-2019 A non-invasive method for detecting valves suffering from stiction using multi-layer feed-forward neural
networks (NN) is proposed, via a simple class-based diagnosis. The proposed Stiction Detection Network (SDN)
uses a transformation of PV (process variable) and OP (controller output) operational data. Verification of
the proposed SDN model’s detection accuracy is done through cross-validation with generated samples and
benchmarking with various industrial loops. The industrial loop benchmark predictions of the proposed SDN
method has a combined accuracy of 78% (75% in predicting stiction, and 81% for non-stiction) in predicting
loop condition, matching capabilities of other established methods in accurately predicting realistic industrial
loops suffering from stiction, while also being applicable to all types of oscillatory control signals. Keywords: Valve stiction | Fault detection | Artificial neural network | Classification | Pattern recognition |
مقاله انگلیسی |
10 |
Error analysis and detection procedures for elliptic curve cryptography
روشهای تجزیه و تحلیل خطا و روشهای تشخیص رمزنگاری منحنی بیضوی-2019 In this paper, a fault detection scheme is introduced with the ability to perform with increased protection
and reliability of the Elliptic Curve Cryptography (ECC) in realistic environments at a competitive price
point. Fall of errors in encrypted/decrypted data delivery with the transmission can create the situation
of erroneous data occurring. Without fault detection, in the context of encryption as well as decryption
process can create the unprotected system due to random faults, and vulnerable to attack. We are striving
to overcome these system weaknesses by introducing the application of nonlinear fault detection codes
as it allows safeguarding the elliptic curve point while adding and doubling the operations from the fault
attacks. The use of these codes ensures almost perfect error discovery capacity with a reasonably priced
upfront cost. The ECC cryptosystem offered a failsafe fault detection scheme using an error detecting code
with a proven rate of 99% fault detection coverage. Keywords: Elliptic Curve Cryptography | Fault attack | Fault detection |
مقاله انگلیسی |