Two-view LSTM variational auto-encoder for fault detection and diagnosis in multivariable manufacturing processes

Qi, Li; Ren, Yuwei; Fang, Yixian; Zhou, Jinglin

doi:10.1007/s00521-023-08949-4

Neural Comput & Applic

2023

DOI: 10.1007/s00521-023-08949-4

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Two-view LSTM variational auto-encoder for fault detection and diagnosis in multivariable manufacturing processes

Li Qi,

Yuwei Ren,

Yixian Fang

et al.

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Cited by 5 publications

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Three‐layer deep learning network random trees for fault detection in chemical production process

Lu,

Gao,

Zou

et al. 2024

Can J Chem Eng

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With the development of technology, the chemical production process is becoming increasingly complex and large‐scale, making fault detection particularly important. However, current detection methods struggle to address the complexities of large‐scale production processes. In this paper, we integrate the strengths of deep learning and machine learning technologies, combining the advantages of bidirectional long‐ and short‐term memory neural networks, fully connected neural networks, and the extra trees algorithm to propose a novel fault detection model named three‐layer deep learning network random trees (TDLN‐trees). First, the deep learning component extracts temporal features from industrial data, combining and transforming them into a higher‐level data representation. Second, the machine learning component processes and classifies the features extracted in the first step. An experimental analysis based on the Tennessee Eastman process verifies the superiority of the proposed method.

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Three‐layer deep learning network random trees for fault detection in chemical production process

Lu,

Gao,

Zou

et al. 2024

Can J Chem Eng

View full text Add to dashboard Cite

show abstract

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Two-view LSTM variational auto-encoder for fault detection and diagnosis in multivariable manufacturing processes

Cited by 5 publications

References 32 publications

Three‐layer deep learning network random trees for fault detection in chemical production process

Three‐layer deep learning network random trees for fault detection in chemical production process

Multi-fault diagnosis and fault degree identification in hydraulic systems based on fully convolutional networks and deep feature fusion

Hybrid physics-infused 1D-CNN based deep learning framework for diesel engine fault diagnostics

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