LSTMED: An uneven dynamic process monitoring method based on LSTM and Autoencoder neural network

Deng, Wangqun; Li, Yuxuan; Huang, Keke; Wu, Dehao; Yang, Chunhua; Gui, Weihua

doi:10.1016/j.neunet.2022.11.001

Cited by 32 publications

(10 citation statements)

References 42 publications

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“…Step 4: Finally, the spatial and temporal monitoring statistics and thresholds of all units are used to calculate a comprehensive monitoring index BIC calculated by Equations ( 20)- (23). Whether there is a fault in a relevant unit is determined according to the decision logic in Equation ( 24).…”

Section: Online Monitoringmentioning

confidence: 99%

“…Due to its significant advantages of nonlinear feature extraction, it has received increasing attentions in the field of data-driven process monitoring. [22][23][24] Recently, several deep structural models, including deep belief networks (DBN), [25] stack autoencoder (SAE), and variational autoencoder (VAE), have been developed for industrial process monitoring based on the deep learning theories for unsupervised feature extraction. Deep learning technologies use a complex hierarchical structure to obtain the essential characteristics of data through feature transformation at each layer, thus capturing the rich patterns in data.…”

Section: Introductionmentioning

confidence: 99%

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Distributed temporal–spatial neighbourhood enhanced variational autoencoder for multiunit industrial plant‐wide process monitoring

Yao,

Jiang,

et al. 2024

Can J Chem Eng

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Process monitoring plays an essential role in ensuring the safe and efficient operation of multi‐unit plantwide industrial processes. However, complicated nonlinear dynamics of these processes pose enormous challenges to the plantwide process monitoring approaches. In this paper, a novel distributed temporal–spatial neighbourhood enhanced variational autoencoder (DTS‐VAE) monitoring scheme is proposed. The scheme selects the temporal and spatial neighbourhood sets of a current sample from a local unit. Time‐series correlation is utilized to construct temporal representative samples to obtain process dynamic characteristics. Likewise, spatial similarity is employed to construct spatial representative samples to obtain spatial patterns in the data. Then the reconstructed and current samples serve as the inputs for a variational autoencoder to extract features from the current sample and its neighbourhoods. Subsequently, a distributed monitor that considers the temporal–spatial characteristics is established for each unit. Finally, a comprehensive evaluation index is developed using Bayesian fusion strategy to improve monitoring performance. The proposed DTS‐VAE monitoring scheme was effectively verified on the Tennessee Eastman benchmark process and a wastewater treatment plant.

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Section: Online Monitoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed temporal–spatial neighbourhood enhanced variational autoencoder for multiunit industrial plant‐wide process monitoring

Yao,

Jiang,

et al. 2024

Can J Chem Eng

View full text Add to dashboard Cite

show abstract

“…Recent developments in the feld of process mining and machine learning have given rise to the technique of predictive process monitoring. Multiple machine learning algorithms have been applied for predictive process monitoring [26]. Tere are two main use cases concerned with the application of predictive process monitoring, such as predicting process outcomes and proactive process monitoring.…”

Section: Literature Reviewmentioning

confidence: 99%

Decision-Making Based on Predictive Process Monitoring of Patient Treatment Processes: A Case Study of Emergency Patients

Aravazhi,

Helgheim,

Aadahl

2023

Advances in Operations Research

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This paper investigates predictive process monitoring problems in emergency treatment by combining the fields of process management and artificial intelligence. The objective is to predict the next activity and its timestamp in the treatment of emergency patients who have undergone surgery at the gastroenterology or urology surgery units in a hospital in Norway. To achieve this goal, three models were developed using different algorithms, and the best performing model was identified using various performance metrics. The results demonstrate the potential of predictive process monitoring to accurately forecast the outcome of patient treatments. By leveraging the insights gained from predictive process monitoring, hospitals can make more informed decisions. The findings of this study suggest that predictive process monitoring holds significant promise as a tool for improving the efficiency and effectiveness of emergency patient treatment processes. This research has significant implications for the field of decision sciences, particularly regarding resource allocation, reducing waiting times, and improving patient outcomes. The ability to predict the outcomes of patient treatment processes has important implications for hospitals, allowing the streamlining and acceleration of the treatment process. Overall, this study provides a promising framework for predicting patient treatment processes by using the predictive process monitoring method. This could be expanded upon in future research, ultimately leading to improved patient outcomes and better decision-making in healthcare.

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“…Up to now, many DL methods, like stacked auto-encoder (SAE), deep belief network (DBN), convolutional neural network (CNN), and long shortterm memory (LSTM), have been designed and applied in industrial production. [12][13][14][15] Specifically, SAE and its derivative models are widely used and developed due to its strong abilities in extracting nonlinear features and revealing low-dimensional essence structure of highdimensional data. To improve the anti-interference ability of SAE, stacked denoising auto-encoder (SDAE) was developed.…”

Section: Introductionmentioning

confidence: 99%

Operating performance assessment based on stacked performance‐relevant enhanced denoising auto‐encoder for industrial processes

Liu,

Ma,

Wang

et al. 2023

Can J Chem Eng

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As an effective way to ensure the economic benefits of enterprises, process operating performance assessment has attracted more and more attention from industry and academia in recent years. In this paper, a stacked performance‐relevant enhanced denoising autoencoder (SPEDAE) network is designed for the operating performance assessment of industrial processes. Compared to the original denoising auto‐encoder (DAE), each performance‐relevant enhanced denoising auto‐encoder (PEDAE) not only reconstructs the input features in the output layer, but also strives to reconstruct the original input data and the performance grade labels simultaneously. Then the SPEDAE is formed by stacking multiple PEDAEs layer by layer. Through this improved training strategy, SPEDAE can avoid accumulated information loss during the deep feature extraction process, improve the robustness of the network, and extract features closely related to the operating performance, thereby better completing the assessment task. The effectiveness of the proposed assessment method is validated on the case of gold cyanide leaching process. Compared with several methods, the proposed SPEDAE has the highest accuracy and reaches 99.85%, which demonstrates its superiority in operating performance assessment.

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LSTMED: An uneven dynamic process monitoring method based on LSTM and Autoencoder neural network

Cited by 32 publications

References 42 publications

Distributed temporal–spatial neighbourhood enhanced variational autoencoder for multiunit industrial plant‐wide process monitoring

Distributed temporal–spatial neighbourhood enhanced variational autoencoder for multiunit industrial plant‐wide process monitoring

Decision-Making Based on Predictive Process Monitoring of Patient Treatment Processes: A Case Study of Emergency Patients

Operating performance assessment based on stacked performance‐relevant enhanced denoising auto‐encoder for industrial processes

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