Fault diagnosis for distillation process based on CNN–DAE

Li, Chuankun; Zhao, Dixian; Mu, Shanjun; Zhang, Weihua; Shi, Ning; Li, Lening

doi:10.1016/j.cjche.2018.12.021

Cited by 38 publications

(25 citation statements)

References 24 publications

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“…The existing fault identification methods are mainly divided into: Qualitative methods [2], quantitative methods [3,4], and data-driven methods [5,6]. Among all of the data-driven fault identification methods, the supervised machine learning technique provides impressive fault identification results for the chemical process [7,8].…”

Section: Background and Significancementioning

confidence: 99%

“…(4) The labeled data and unlabeled data in M are used to train DAS4VM. (5) The PCA-DAS4VM model will be built if the pseudo label confidence is higher than 80%.…”

Section: Fault Identification Frameworkmentioning

confidence: 99%

“…The process includes 22 continuous measured variables (Table 2) and 20 preset fault modes (Table 3). 3E Feed CMV (14) Separator underflow CMV 4A and C Feed CMV (15) Stripper level CMV (5) Recycle flow CMV (16) Stripper pressure CMV (6) Reactor feed CMV (17) Stripper underflow CMV 7Reactor pressure CMV (18) Stripper temperature CMV (8) Reactor level CMV (19) Stripper steam flow 5Recycle flow CMV (16) Stripper pressure CMV (6) Reactor feed CMV (17) Stripper underflow CMV 7Reactor pressure CMV (18) Stripper temperature CMV (8) Reactor level CMV (19) Stripper steam flow CMV (9) Reactor temperature CMV (20) Compressor work CMV (10) Purge flow CMV (21) Reactor cooling water outlet temperature CMV (11) Separator temperature CMV (22) Condenser cooling water outlet temperature Figure 4 shows that the total variance contribution rate of the first 12 principal components has reached 83.15% (more than 80%), so the first 12 principal components can reflect information about all variables. Figure 5 shows the eigenvalues of the first 12 principal components as well.…”

Section: Process Descriptionmentioning

confidence: 99%

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A Dynamic Active Safe Semi-Supervised Learning Framework for Fault Identification in Labeled Expensive Chemical Processes

Jia

Tian

et al. 2020

Processes

Self Cite

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A novel active semi-supervised learning framework using unlabeled data is proposed for fault identification in labeled expensive chemical processes. A principal component analysis (PCA) feature selection strategy is first given to calculate the weight of the variables. Secondly, the identification model is trained based on the obtained key process variables. Thirdly, the pseudo label confidence of identification model is dynamically optimized with an historical, current, and future pseudo label confidence mean. To increase the upper limit of the identification model that is self-learning with high entropy process data, active learning is used to identify process data and diagnosis fault causes by ontology. Finally, a PCA-dynamic active safe semi-supervised support vector machine (PCA-DAS4VM) for fault identification in labeled expensive chemical processes is built. The application in the Tennessee Eastman (TE) process shows that this hybrid technology is able to: (i) eliminate chemical process noise and redundant process variables simultaneously, (ii) combine historical pseudo label confidence with future pseudo label confidence to improve the identification accuracy of abnormal working conditions, (iii) efficiently select and diagnose high entropy unlabeled process data, and (iv) fully utilize unlabeled data to enhance the identification performance.

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Section: Background and Significancementioning

confidence: 99%

“…(4) The labeled data and unlabeled data in M are used to train DAS4VM. (5) The PCA-DAS4VM model will be built if the pseudo label confidence is higher than 80%.…”

Section: Fault Identification Frameworkmentioning

confidence: 99%

Section: Process Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

A Dynamic Active Safe Semi-Supervised Learning Framework for Fault Identification in Labeled Expensive Chemical Processes

Jia

Tian

et al. 2020

Processes

Self Cite

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“…This learning task is similar to the training in natural language processing (NLP) and image recognition [20]. That is why a DAE-CNN ( Figure 2) was specifically designed as the subnetwork to process the harmonic features, and output a vector [21][22][23]. The output vector, together with the environmental conditions, was received by the fullyconnected network, which then output the predicted changes of measuring errors.…”

Section: Model Constructionmentioning

confidence: 99%

A Bifurcation Deep Neural Network for Electricity Meter Error Prediction under Actual Conditions

Kou¹,

Fang²,

Bleszinski³

2019

EJEE

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The environmental conditions have a great impact on the measuring accuracy of electricity meters, once they are installed. This paper aims to find a way to accurately evaluate the measuring errors of electricity meters under actual conditions. Specifically, a novel bifurcation deep neural network (BDNN) model was designed and tested. The BDNN consists of a subnetwork and a fully-connected network. The subnetwork is a deep autoencoderconvolutional neural network (DAE-CNN) dedicated to processing harmonic features. The fully-connected network takes the subnetwork output and the environmental conditions as its inputs, and generates the output of the entire model by softmax. Then, the BDNN was trained on a dataset generated by real experiments with electricity meters. Three hyperparameters, namely, the activation function, the number of hidden layers and the autoencoder structure, were optimized through several experiments. Through the optimization, the rectified linear unit (ReLu) was adopted as the activation function, the number of hidden layers was set to 4, and the autoencoder structure was determined as 256-128-64-32. Each numerical figure refers to the number of nodes in the corresponding hidden layer. Finally, the BDNN was compared with the least squares support vector machine (LS-SVM), the fully-connected MLP (FCP) and the original CNN, and found to outshine the contrastive methods in prediction error and computing cost. The research results shed important new light on the field calibration and error prediction of electricity meters.

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“…Other works with real cases containing fault detection and diagnosis implementation were presented in [30][31][32]. Furthermore, some representative simulated cases were presented in [33][34][35][36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Development and Application of a Data-Driven System for Sensor Fault Diagnosis in an Oil Processing Plant

et al. 2019

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Predictive analytics is usually cited as one of the most important pillars of the digital transformation. For the oil industry, specifically, it is a common belief that issues like integrity and maintenance could benefit from predictive analytics. This paper presents the development and the application of a process-monitoring tool in a real process facility. The PMA (Predictive Maintenance Application) system is a data-driven application that uses a multivariate analysis in order to predict the system behavior. Results show that the use of a multivariate approach for process monitoring could not only detect an early failure at a metering system days before the operation crew, but could also successfully identify, among hundreds of variables, the root cause of the abnormal situation. By applying such an approach, a better performance of the monitored equipment is expected, decreasing its downtime.

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Fault diagnosis for distillation process based on CNN–DAE

Cited by 38 publications

References 24 publications

A Dynamic Active Safe Semi-Supervised Learning Framework for Fault Identification in Labeled Expensive Chemical Processes

A Dynamic Active Safe Semi-Supervised Learning Framework for Fault Identification in Labeled Expensive Chemical Processes

A Bifurcation Deep Neural Network for Electricity Meter Error Prediction under Actual Conditions

Development and Application of a Data-Driven System for Sensor Fault Diagnosis in an Oil Processing Plant

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