Swarm intelligence based deep learning model via improved whale optimization algorithm and Bi-directional long short-term memory for fault diagnosis of chemical processes

被引：7
|
作者：
Ji, Chunlei [1 ]
Zhang, Chu [1 ,2 ]
Suo, Leiming [1 ]
Liu, Qianlong [1 ]
Peng, Tian [1 ,2 ]
机构：
[1] Huaiyin Inst Technol, Fac Automat, Huaian 223003, Peoples R China
[2] Huaiyin Inst Technol, Jiangsu Permanent Magnet Motor Engn Res Ctr, Huaian 223003, Peoples R China
来源：
ISA TRANSACTIONS | 2024年 / 147卷
基金：
中国国家自然科学基金;
关键词：
Fault diagnosis; Kernel principal component analysis; Whale optimization algorithm; Bi-directional long short-term memory; Tennessee eastman process; CONVOLUTIONAL NEURAL-NETWORK; DECOMPOSITION; TRANSFORMER;
D O I：
10.1016/j.isatra.2024.02.014
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The chemical production process typically possesses complexity and high risks. Effective fault diagnosis is a key technology for ensuring the reliability and safety of chemical production processes. In this study, a comprehensive fault diagnosis method based on time-varying filtering empirical mode decomposition (TVF-EMD), kernel principal component analysis (KPCA), and an improved whale optimization algorithm (WOA) to optimize bi-directional long short-term memory (BiLSTM) is proposed. This research utilizes TVF-EMD and KPCA to analyze and preprocess the raw data, eliminating noise and and reducing the dimensions of the fault data. Subsequently, BiLSTM is employed for fault data classification. To address the hyperparameters within BiLSTM, the enhanced WOA is used for optimization. Finally, the efficacy and superiority of this approach are validated through two fault diagnosis examples.
引用
收藏
页码：227 / 238
页数：12
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