A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network

Wang, Yalin; Pan, Zhuofu; Yuan, Xiaofeng; Yang, Chunhua; Gui, Weihua

doi:10.1016/j.isatra.2019.07.001

Cited by 295 publications

(99 citation statements)

References 33 publications

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“…A linear hypothesis is described as the combination of the first order terms and it is detailed as the sum of multiplication of the first order modeling parameters with the first order inputs [7], [8], [15], [16]. A linear hypothesis is described to relate the input variable of the end-effector (…”

Section: A Linear Hypothesismentioning

confidence: 99%

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Novel Nonlinear Hypothesis for the Delta Parallel Robot Modeling

et al. 2020

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In previous investigations, the nonlinear hypothesis use the linear bounded maps. Nonlinear hypothesis are described as the combination of the first order terms, and after of the mentioned combination, one bounded map is applied to alter the result. This document proposes two nonlinear hypothesis which use different structures instead of using the linear bounded maps. They are termed as novel nonlinear hypothesis and second order nonlinear hypothesis and their goal is to improve the second order processes modeling. The proposed nonlinear hypothesis are described as the combination of the first order and second order terms. Since the delta parallel robot is a second order process, it is an excellent platform to prove the effectiveness of the two proposed hypothesis. INDEX TERMS Novel nonlinear hypothesis, second order nonlinear hypothesis, nonlinear hypothesis, delta parallel robot.

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Section: A Linear Hypothesismentioning

confidence: 99%

“…In [13], [14], authors use hypothesis containing Gaussian membership maps. In [15], [16], authors use hypothesis containing linear activation maps. In [17], [18], authors use hypothesis containing discontinuous activation maps.…”

Section: Introductionmentioning

confidence: 99%

Novel Nonlinear Hypothesis for the Delta Parallel Robot Modeling

et al. 2020

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“…In recent years, deep‐learning‐based architectures have been applied in data modeling of soft sensor . Based on quality prediction and process monitoring methods in process industries, deep quality‐related feature extraction with hybrid Variable‐Wise weighted stack auto encoder (VW‐SAE), hierarchical quality‐relevant feature representation and extended deep belief network have been developed for soft sensing modeling …”

Section: Introductionmentioning

confidence: 99%

“…36,37 Based on quality prediction and process monitoring methods in process industries, deep qualityrelated feature extraction with hybrid Variable-Wise weighted stack auto encoder (VW-SAE), hierarchical quality-relevant feature representation and extended deep belief network have been developed for soft sensing modeling. [38][39][40] Although the model-driven soft sensor is more effective for specific plants, in the previous research of acetylene hydrogenation reactor, a simple soft sensor calibration scheme based on output correction is proposed. 41 In addition, dynamic modeling of soft sensor is a significant problem that should be addressed.…”

Section: Introductionmentioning

confidence: 99%

Online estimation for catalyst activity of acetylene hydrogenation reactor

Xie

Zhang

et al. 2020

Asia-Pacific J Chem Eng

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In the ethylene industry, the high purity of the ethylene product depends on hydrogenation in acetylene hydrogenation reactor. Because the catalyst deactivation leads to the moving of the operating point, the operation scheme must be adjusted continually according to the catalyst activity. It is necessary to estimate the catalyst activity online. Based on the discrete dynamic model of the acetylene hydrogenation reactor, the extended Kalman filter (EKF) is used to build the soft sensor for catalyst activity. Considering that EKF involves the large computation costs, we propose a method that estimates the parameters of the time‐varying deactivation kinetics model for the tradeoff of accuracy and complexity. The method is effective to reduce computation complexity of estimation, and simultaneously, the accuracy satisfies the process requirement.

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“…In the last decade, the recently developed deep learning has received an increased amount of attention in process data modelling like process monitoring and soft sensor applications. Compared to traditional models, deep neural networks (DNN) with multiple hidden layers have a powerful ability to learn the essential features of data, thus ultimately improving the performance of various tasks.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for quality prediction of nonlinear dynamic processes with variable attention‐based long short‐term memory network

Yuan

Wang

et al. 2019

Can J Chem Eng

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Industrial processes are often characterized with high nonlinearities and dynamics.For soft sensor modelling, it is important to model the nonlinear and dynamic relationship between input and output data. Thus, long short-term memory (LSTM) networks are suitable for quality prediction of soft sensor modelling. However, they do not consider the relevance of different input variables with the quality variable. To address this issue, a variable attention-based long short-term memory (VA-LSTM) network is proposed for soft sensing in this paper. In VA-LSTM, variable attention is designed to identify important input variables according to their relevance with quality prediction. After that, different attention weights are calculated and assigned to further obtain a weighted input sample at each time step.Finally, the LSTM network is exploited to capture the long-term dependencies of the weighted input time series to predict the quality variable. The performance of the proposed modelling method is validated on an industrial debutanizer column and a hydrocracking process. K E Y W O R D Sattention, deep learning, long short-term memory, prediction, soft sensor

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A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network

Cited by 295 publications

References 33 publications

Novel Nonlinear Hypothesis for the Delta Parallel Robot Modeling

Novel Nonlinear Hypothesis for the Delta Parallel Robot Modeling

Online estimation for catalyst activity of acetylene hydrogenation reactor

Deep learning for quality prediction of nonlinear dynamic processes with variable attention‐based long short‐term memory network

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