Generalized transformer in fault diagnosis of Tennessee Eastman process

Zhang, Lei; Song, Zhihuan; Zhang, Qinghua; Peng, Zhiping

doi:10.1007/s00521-021-06711-2

Cited by 15 publications

(5 citation statements)

References 27 publications

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“…Transformer is a neural network based on a pure attention mechanism to reflect the global dependence between input and output and has good identification performance in chemical process fault diagnosis. 38 Transformer’s network architecture and hyperparametric references 39 take the enhanced dynamic data as the input, and the size of the input subsequence is 20. Similarly, all experiments are repeated 10 times under the same conditions, and the average accuracy is taken as the experimental result.…”

Section: Experimental Verificationmentioning

confidence: 99%

“…The kernel function of SVM is RBF. Transformer is a neural network based on a pure attention mechanism to reflect the global dependence between input and output and has good identification performance in chemical process fault diagnosis . Transformer’s network architecture and hyperparametric references take the enhanced dynamic data as the input, and the size of the input subsequence is 20.…”

Section: Experimental Verificationmentioning

confidence: 99%

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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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Section: Experimental Verificationmentioning

confidence: 99%

Section: Experimental Verificationmentioning

confidence: 99%

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

et al. 2022

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“…For instance, Wei et al [ 23 ] proposed an improved Transformer for fault diagnosis in chemical processes, demonstrating its performance through evaluations of the Tennessee Eastman (TE) process. Zhang et al [ 24 ] introduced a novel method called the generalized transformer, employing the attention mechanism to explore the relationships and non‐linearities between inputs for fault diagnosis tasks, and their experiments validated its effectiveness. These advancements highlight the transformative impact of deep learning technologies in diagnosing and managing faults in complex systems, showcasing their potential for broader applications in various industrial processes.…”

Section: Introductionmentioning

confidence: 99%

Enhanced process monitoring for industrial coking furnace using a dual‐channel pooling and homologous bilinear model‐based convolutional neural network

Hua,

Cui,

et al. 2024

Can J Chem Eng

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This paper introduces a process monitoring method using a convolutional neural network (CNN) with dual‐channel pooling and homologous bilinear models (DCP‐HBM). This method aims to enhance the stability of industrial coking furnaces and reduce maintenance costs. It is significantly relevant for addressing challenges such as complexity, high dimensionality, and strong coupling, which are difficult to manage with traditional fault diagnosis methods. In this study, a deep feature extraction module is employed to gather feature information. To effectively reduce the impact of noise and strengthen the correlation between features, DCP is incorporated after deep feature extraction. Concurrently, a HBM is introduced for feature fusion, further refining the characteristics of each state. Faults are then accurately classified through a classification module. The method utilizes DCP to filter and focus input data, thus making the model more attentive to task‐relevant information. This enhances the model's understanding and representation of the input data. The introduction of the HBM further refines the features extracted by the network and increases the precision of feature extraction. This leads to improved recognition of faults with high similarity, thereby enhancing the accuracy of the overall process monitoring. Experimental results demonstrate that this method exhibits strong performance in the monitoring of industrial coking furnaces, indicating its wide‐ranging application prospects.

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“…Bidirectional structures are introduced into LSTM, which include both forward and backward dependencies [42]. This improvement effectively avoids problems such as gradient disappearance and explosion caused by time dependence, and has been widely used in many fields [43]. However, it still has some shortcomings in the depth and complexity of feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis in asynchronous motors based on an optimal deep bidirectional long short-term memory networks

Xu,

Li,

Liu

et al. 2023

Meas. Sci. Technol.

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Fault diagnosis of asynchronous motors has become a pressing need in the metallurgical industry. Due to the complex structure of asynchronous motors, fault types and fault characteristics are diverse, with strong nonlinear relationships between them, which leads to the difficulty of fault diagnosis. To efficiently and accurately diagnose various motor faults, we propose a fault diagnosis method based on an optimal deep bidirectional long short-term memory neural network. First, the three-phase current, multidimensional vibrational signal, and acoustic signal of the asynchronous motor are collected and construct diverse and robust data sample set to enhance the generalization ability of the model. Next, a modified 3D logistics-sine complex chaotic map (3D LSCCM) is constructed to improve the global and local search capabilities of the pigeon swarm optimization algorithm (PIO). Then, we construct a deep bidirectional long short-term memory network (Bid-LSTM) with attention mechanism to mine high-value fault characteristic information. Meanwhile, the optimal hyper-parameters of the deep ABid-LSTM are explored using the modified PIO to improve the training performance of the model. Finally, the fault data samples of asynchronous motor are induced to train and test the proposed framework. By fusing diverse data samples, the proposed method outperforms conventional deep Bid-LSTM and achieves fault diagnosis accuracy of 99.13%. It provides a novel diagnostic strategy for motor fault diagnosis.

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Generalized transformer in fault diagnosis of Tennessee Eastman process

Cited by 15 publications

References 27 publications

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

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

Enhanced process monitoring for industrial coking furnace using a dual‐channel pooling and homologous bilinear model‐based convolutional neural network

Fault diagnosis in asynchronous motors based on an optimal deep bidirectional long short-term memory networks

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