An enhanced <scp>DPCA</scp> fault diagnosis method based on hierarchical cluster analysis

Chen, Youqiang; Bai, Jianjun; Zou, Hongbo

doi:10.1002/cjce.25058

Cited by 5 publications

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

Chemical process fault detection and trend analysis based on KESN

Cao,

Cheng,

Deng

et al. 2024

Can J Chem Eng

View full text Add to dashboard Cite

Fault detection has great significance for chemical process safety with the development of science and technology. The conventional echo state network‐based fault detection method does not highlight key fault features, and cannot forecast future fault trend after the occurrence of faults. For the above problems, a chemical process fault detection and trend analysis strategy based on key feature enhanced echo state network (KESN) is proposed. First, dynamic features are extracted by a detecting echo state network. Then, a weighting strategy is designed to enhance key features and increase fault detection rates. After detecting a fault, independent component analysis is utilized to extract independent key features. Future fault trend is forecasted based on the forecasting multi‐KESN. Simulation results on the Tennessee Eastman process demonstrate the effectiveness of the proposed method.

show abstract

An Industrial Fault Diagnosis Method Based on Graph Attention Network

Hou,

Sun,

Liu

et al. 2024

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

In the field of industrial production, the precise and timely implementation of fault diagnosis methods is crucial for improving product quality, enhancing operational safety, reducing downtime, and minimizing losses. Recent studies have shown that most CNN-based fault diagnosis models are more suitable for handling Euclidean data such as images or videos but are not suitable for dealing with non-Euclidean sensor data. In practical industrial scenarios, chemical process data with imbalanced fault patterns may lead data-driven models to assign different attentions to fault patterns. The SMOTE algorithm is commonly used to generate new data, but it often tends to overfit when there are very few nearest neighbor samples. To address these issues, we designed an efficient fault diagnosis model named KRGAT. To fully utilize the spatial structural information on sensor data, we employed graph attention networks (GATs), which are well-suited for handling non-Euclidean data. Additionally, we introduced the top-k loss method to select hard samples, thereby increasing the weight of these samples. Furthermore, we improved DropMessage to enhance the model's accuracy and robustness. Experimental results demonstrate that our model outperforms the baseline model under both balanced and imbalanced conditions.

show abstract

An enhanced DPCA fault diagnosis method based on hierarchical cluster analysis

Cited by 5 publications

References 27 publications

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

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

Chemical process fault detection and trend analysis based on KESN

An Industrial Fault Diagnosis Method Based on Graph Attention Network

Contact Info

Product

Resources

About