A Multichannel LSTM‐CNN Method for Fault Diagnosis of Chemical Process

Shao, Bilin; Hu, Xiaoli; Bian, Genqing; Zhao, Yu

doi:10.1155/2019/1032480

Cited by 22 publications

(10 citation statements)

References 24 publications

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“…While CNN can capture the local features of data in a small window. Some studies of chemical anomaly warning add convolution layer to improve the performance of LSTM [118] . Figure 3 shows a network model combining CNN and Bi-LSTM for predicting abnormal conditions of FCCU [119] .…”

Section: Data-driven Methodsmentioning

confidence: 99%

Artificial Intelligence Methods Applied to Catalytic Cracking Processes

Yang

Lei

et al. 2023

Big Data Min. Anal.

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Fluidic Catalytic Cracking (FCC) is a complex petrochemical process affected by many highly non-linear and interrelated factors. Product yield analysis, flue gas desulfurization prediction, and abnormal condition warning are several key research directions in FCC. This paper will sort out the relevant research results of the existing Artificial Intelligence (AI) algorithms applied to the analysis and optimization of catalytic cracking processes, with a view to providing help for the follow-up research. Compared with the traditional mathematical mechanism method, the AI method can effectively solve the difficulties in FCC process modeling, such as high-dimensional, nonlinear, strong correlation, and large delay. AI methods applied in product yield analysis build models based on massive data. By fitting the functional relationship between operating variables and products, the excessive simplification of mechanism model can be avoided, resulting in high model accuracy. AI methods applied in flue gas desulfurization can be usually divided into two stages: modeling and optimization. In the modeling stage, data-driven methods are often used to build the system model or rule base; In the optimization stage, heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule base. AI methods, including data-driven and knowledge-driven algorithms, are widely used in the abnormal condition warning. Knowledge-driven methods have advantages in interpretability and generalization, but disadvantages in construction difficulty and prediction recall. While the data-driven methods are just the opposite. Thus, some studies combine these two methods to obtain better results.

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Section: Data-driven Methodsmentioning

confidence: 99%

Artificial Intelligence Methods Applied to Catalytic Cracking Processes

Yang

Lei

et al. 2023

Big Data Min. Anal.

View full text Add to dashboard Cite

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“…Early detection or even prediction of such situations can have a relevant impact on the Overall Equipment Effectiveness (OEE) of the plant and its productivity. Machine learning methods oriented to time series such as LTSM (Long short-term memory) networks can be used to address these types of challenges [ 102 , 103 ].…”

Section: Potential Of Data Intelligencementioning

confidence: 99%

Refinery 4.0, a Review of the Main Challenges of the Industry 4.0 Paradigm in Oil & Gas Downstream

Olaizola

Quartulli

Unzueta

et al. 2022

Sensors

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Industry 4.0 concept has become a worldwide revolution that has been mainly led by the manufacturing sector. Continuous Process Industry is part of this global trend where there are aspects of the “fourth industrial revolution” that must be adapted to the particular context and needs of big continuous processes such as oil refineries that have evolved to control paradigms supported by sector-specific technologies where big volumes of operation-driven data are continuously captured from a plethora of sensors. The introduction of Artificial Intelligence techniques can overcome the current limitations of Advanced Control Systems (mainly MPCs) by providing better performance on highly non-linear and complex systems and by operating with a broader scope in terms of signals/data and sub-systems. Moreover, the state of the art of traditional PID/MPC based solutions is showing an asymptotic improvement that requires a disruptive approach in order to reach relevant improvements in terms of efficiency, optimization, maintenance, etc. This paper shows the key aspects in oil refineries to successfully adopt Big Data and Machine Learning solutions that can significantly improve the efficiency and competitiveness of continuous processes.

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“…The fault diagnosis model based on the combination of CNN and LSTM has become a research hotspot because of its ability to extract spatial and temporal features of industrial data and improve diagnosis performance. Shao et al 25 used a multichannel LSTM-CNN (MCLSTM-CNN) fault diagnosis model. This method inputs a data set into LSTM and then uses multiple parallel convolution layers to mine the output features of the hidden layer at the same time.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of the Dynamic Chemical Process Based on the Optimized CNN-LSTM Network

et al. 2022

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Deep learning provides new ideas for chemical process fault diagnosis, reducing potential risks and ensuring safe process operation in recent years. To address the problem that existing methods have difficulty extracting the dynamic fault features of a chemical process, a fusion model (CS-IMLSTM) based on a convolutional neural network (CNN), squeeze-and-excitation (SE) attention mechanism, and improved long short-term memory network (IMLSTM) is developed for chemical process fault diagnosis in this paper. First, an extended sliding window is utilized to transform data into augmented dynamic data to enhance the dynamic features. Second, the SE is utilized to optimize the key fault features of augmented dynamic data extracted by CNN. Then, IMLSTM is used to balance fault information and further mine the dynamic features of time series data. Finally, the feasibility of the proposed method is verified in the Tennessee-Eastman process (TEP). The average accuracies of this method in two subdata sets of TEP are 98.29% and 97.74%, respectively. Compared with the traditional CNN-LSTM model, the proposed method improves the average accuracies by 5.18% and 2.10%, respectively. Experimental results confirm that the method developed in this paper is suitable for chemical process fault diagnosis.

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A Multichannel LSTM‐CNN Method for Fault Diagnosis of Chemical Process

Cited by 22 publications

References 24 publications

Artificial Intelligence Methods Applied to Catalytic Cracking Processes

Artificial Intelligence Methods Applied to Catalytic Cracking Processes

Refinery 4.0, a Review of the Main Challenges of the Industry 4.0 Paradigm in Oil & Gas Downstream

Fault Diagnosis of the Dynamic Chemical Process Based on the Optimized CNN-LSTM Network

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