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 |
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1 |
A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network
یک رویکرد تشخیص خطا مبتنی بر یادگیری عمیق برای فرآیند شیمیایی با شبکه باور عمیق گسترده-2019 Deep learning networks have been recently utilized for fault detection and diagnosis (FDD) due to its
effectiveness in handling industrial process data, which are often with high nonlinearities and strong
correlations. However, the valuable information in the raw data may be filtered with the layer-wise
feature compression in traditional deep networks. This cannot benefit for the subsequent fine-tuning
phase of fault classification. To alleviate this problem, an extended deep belief network (EDBN) is
proposed to fully exploit useful information in the raw data, in which raw data is combined with
the hidden features as inputs to each extended restricted Boltzmann machine (ERBM) during the
pre-training phase. Then, a dynamic EDBN-based fault classifier is constructed to take the dynamic
characteristics of process data into consideration. Finally, to test the performance of the proposed
method, it is applied to the Tennessee Eastman (TE) process for fault classification. By comparing
EDBN and DBN under different network structures, the results show that EDBN has better feature
extraction and fault classification performance than traditional DBN. Keywords: Fault detection and diagnosis | Deep learning | Deep belief network | Extended DBN |
مقاله انگلیسی |
2 |
Deep-learning-based fault detection and diagnosis of air-handling units
تشخیص خطای مبتنی بر یادگیری عمیق و تشخیص واحدهای انتقال هوا-2019 This study proposed a real-time fault diagnostic model for air-handling units (AHUs); the model used deep
learning to improve the operational efficiency of AHUs and thereby reduce the energy consumption of
HVAC—heating, ventilating, and air conditioning—systems in buildings. Additionally, EnergyPlus simulation
software was employed to establish different types of fault operation behavior data to serve as references for
deep learning, thus reducing the complexity of data preprocessing, retaining data completeness, and improving
the reliability of the diagnostic model.
The proposed deep neural network fault diagnostic model can serve as a reference for this research field; the
model features five hidden layers, each comprising 200 neurons. Additionally, this study tested abnormal faults
commonly observed in AHUs, including failure to control two-way hydronic valves and variable air volume box
dampers as well as supply air temperature sensors exhibiting measurement error. After performing diagnosis
with data that had not been used in the training or verification process, the diagnostic results indicated that the
diagnostic model exhibited 95.16% accuracy. Keywords: Deep learning | Deep neural network | Fault detection and diagnosis |
مقاله انگلیسی |