<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">TACS</journal-id><journal-title-group><journal-title>Technology and Application of Computer Science</journal-title></journal-title-group><issn>2998-8926</issn><eissn>2998-8934</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/TACS.2026020040</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>基于深度学习的反应器过程故障诊断方法研究</title><url>https://artdesignp.com/journal/TACS/3/2/10.61369/TACS.2026020040</url><author>唐琪,郭武,刘怀海,刘建平,陆相印</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>3</volume><issue>2</issue><history><date date-type="pub"><published-time>2026-01-28</published-time></date></history><abstract>在工业反应器运行过程中，高维过程信号的特征提取效果与噪声抑制水平，会直接影响故障检测与诊断的整体性能。为解决这一问题，本文设计一种新型多通道时序双向长短期记忆- 门控循环单元神经网络（MTBiLSTM-GRU），用于高维过程信号的深度特征学习。首先采用小波变换对高维过程信号进行多频带特征提取，分离不同频率分量信息；其次通过快速傅里叶变换实现时域信号到频域信号的转换，简化信号特征呈现形式；最后利用 MTBiLSTM-GRU 网络从多尺度过程信号中学习具有区分度的时频联合特征。在 Tennessee Eastman 数据集上开展多组故障分类方法对比实验，结果表明：小波变换降噪条件下模型分类准确率达到 90.71%，当选用 haar 小波基且降噪等级设置为 3 时，模型分类准确率可提升至 96.1%。此外，在行业内公认难以分类的第 3 类、第 9 类与第 15 类故障数据上，本文模型分别实现 99.50%、98.63%、97.38% 的高分类精度。</abstract><keywords>故障检测,时间序列提取,信息融合</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1]Faradisa, I. S., et al. (2016). Identification of phonocardiogram signal based on STFT and Marquart Lavenberg Backpropagation. 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA).[2]Daubechies, I. (1990). "The wavelet transform, time-frequency localization and signal analysis." IEEE Transactions on Information Theory 36(5): 961-1005.[3]Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classifcation with deep convolutional neural networks. Commun ACM 60(6):84&amp;ndash;90[4]Shahnazari, H. (2020). "Fault diagnosis of nonlinear systems using recurrent neural networks." Chemical Engineering Research and Design 153: 233-245.[5]Sun, W., et al. (2020). "Fault detection and identification using Bayesian recurrent neural networks." Computers &amp;amp; Chemical Engineering 141: 106991.[6]Kang, J.-L. (2020). "Visualization analysis for fault diagnosis in chemical processes using recurrent neural networks." Journal of the Taiwan Institute of Chemical Engineers 112: 137-151.[7]Han, Y., et al. (2020). "An optimized long short-term memory network based fault diagnosis model for chemical processes." Journal of Process Control 92: 161-168.[8]Zhang, R. and Z. Xiong (2019). Recurrent Neural Network Model with Self-Attention Mechanism for Fault Detection and Diagnosis. 2019 Chinese Automation Congress (CAC).[9]Mirzaei, S., et al. (2022). "A comparative study on long short-term memory and gated recurrent unit neural networks in fault diagnosis for chemical processes using visualization." Journal of the Taiwan Institute of Chemical Engineers 130.[10]Yu, J., et al. (2020). "Multichannel one-dimensional convolutional neural network-based feature learning for fault diagnosis of industrial processes." Neural Computing and Applications 33(8): 3085-3104.[11]P&amp;ouml;ppelbaum, J., et al. (2022). "Contrastive learning based self-supervised time-series analysis." Applied Soft Computing 117.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
