Research on <scp>TE</scp> process fault diagnosis method based on <scp>DBN</scp> and dropout

Wei, Yuqin; Weng, Zhengxin

doi:10.1002/cjce.23750

Cited by 32 publications

(14 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In addition, dropout is used to avoid the overfitting problem. As it is similar in most previous research, [26,45,46] no detailed introduction will be given here. one, whose maximum accuracy reaches 91.7% for a single test.…”

Section: Model Optimization 431 | Model Parametersmentioning

confidence: 90%

Chemical process fault diagnosis based on a combined deep learning method

Bao

Guo

et al. 2021

Can J Chem Eng

View full text Add to dashboard Cite

The study on fault detection and diagnosis (FDD) of chemical processes has always been the top priority of the chemical process safety. In this paper, a fault diagnosis method combining the deep convolutional with the recurrent neural network (DCRNN) is proposed. In this method, the data from chemical processes are input to the deep convolutional neural network (DCNN) to extract features in spatial domains, and then, the features are fused into the bidirectional recurrent neural network (BRNN). Due to the powerful capabilities of DCNN to extract features in spatial domains and the sensitivity to time series of RNN, the combined method can adaptively learn the dynamic information of the raw data in both spatial and temporal domains and has unique advantages in multivariate chemical processes. The application of the DCRNN model in the Tennessee Eastman (TE) process demonstrates the high accuracy of this proposal in identifying abnormal conditions for the chemical process, compared with the traditional fault identification algorithms of deep learning.

show abstract

Section: Model Optimization 431 | Model Parametersmentioning

confidence: 90%

Chemical process fault diagnosis based on a combined deep learning method

Bao

Guo

et al. 2021

Can J Chem Eng

View full text Add to dashboard Cite

show abstract

“…With the increasing complexity of industrial systems, data-based methods have been paid more and more attention in multivariate statistical process monitoring (MSPM). [10][11][12][13] Data-based methods have been proved more appropriate for complicated industrial processes. [14] The high-dimensional multivariate data often collected from different sensors.…”

Section: Introductionmentioning

confidence: 99%

Uncorrelated discriminant graph embedding for fault classification

Peng

Zhao

2021

Can J Chem Eng

View full text Add to dashboard Cite

Recently, graph embedding methods have been successfully used in process monitoring. To improve the discriminant power, a novel supervised graph embedding method, called uncorrelated discriminant graph embedding (UDGE), is proposed. Different from the unsupervised design of locality preserving projection (LPP), UDGE utilizes both the local geometrical structure and label information to construct the similarity between different data points.The "local geometrical structure" means that each data point can be represented as a combination of its neighbours. Due to add the uncorrelated constraint, the extracted features of UDGE are statistically uncorrelated. Uncorrelated attributes are essential for dimension reduction since they contain minimum redundancy. The application of UDGE is evaluated on the Tennessee Eastman process (TEP) benchmark. Experimental results show UDGE can better separate different types of faults and provide more promising fault diagnosis performance. The code of UDGE is released in https://github.com/htz-ecust/ Uncorrelated-Discriminant-Graph-Embedding-for-Fault-Classification.

show abstract

“…Compared with the shallow model, DBN can use the initial stacked restricted Boltzmann machine to unsupervise feature extraction and then use the classifier to fine-tune [14], which can improve the classification effect in the case of semisupervised and weak labeling. Further, Sun et al [15] used signal processing to extract the fault characteristics of the monitoring signal and used deep learning to diagnose the type of mechanical failure and the degree of damage.…”

Section: Introductionmentioning

confidence: 99%

Design and Application of Deep Belief Network Based on Stochastic Adaptive Particle Swarm Optimization

Yang

Chang

Wang

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Due to the problem of poor recognition of data with deep fault attribute in the case of traditional superficial network under semisupervised and weak labeling, a deep belief network (DBN) was proposed for deep fault detection. Due to the problems of deep belief network (DBN) network structure and training parameter selection, a stochastic adaptive particle swarm optimization (RSAPSO) algorithm was proposed in this study to optimize the DBN. A stochastic criterion was proposed in this method to make the particles jump out of the original position search with a certain probability and reduce the probability of falling into the local optimum. The RSAPSO-DBN method used sample data to train the DBN and used the final diagnostic error rate to construct the fitness value function of the particle swarm algorithm. By comparing the minimum fitness value of each particle to determine the advantages and disadvantages of the model, the corresponding minimum fitness value was selected. Using the number of network nodes, learning rate, and momentum parameters, the optimal DBN classifier was generated for fault diagnosis. Finally, the validity of the method was verified by bearing data from Case Western Reserve University in the United States and data collected in the laboratory. Comparing BP (BP neural network), support vector machine, and heterogeneous particle swarm optimization DBN methods, the proposed method demonstrated the highest recognition rates of 87.75% and 93.75%. This proves that the proposed method possesses universality in fault diagnosis and provides new ideas for data identification with different fault depth attributes.

show abstract

Research on TE process fault diagnosis method based on DBN and dropout

Cited by 32 publications

References 31 publications

Chemical process fault diagnosis based on a combined deep learning method

Chemical process fault diagnosis based on a combined deep learning method

Uncorrelated discriminant graph embedding for fault classification

Design and Application of Deep Belief Network Based on Stochastic Adaptive Particle Swarm Optimization

Contact Info

Product

Resources

About