[1]牟建鹏,刘文韬,熊伟丽.基于双向生成对抗网络的工业过程故障检测[J].智能系统学报,2024,19(5):1199-1208.[doi:10.11992/tis.202306011]
MOU Jianpeng,LIU Wentao,XIONG Weili.Industrial process fault detection based on bidirectional generative adversarial networks[J].CAAI Transactions on Intelligent Systems,2024,19(5):1199-1208.[doi:10.11992/tis.202306011]
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MOU Jianpeng,LIU Wentao,XIONG Weili.Industrial process fault detection based on bidirectional generative adversarial networks[J].CAAI Transactions on Intelligent Systems,2024,19(5):1199-1208.[doi:10.11992/tis.202306011]
基于双向生成对抗网络的工业过程故障检测
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第5期
页码:
1199-1208
栏目:
学术论文—智能系统
出版日期:
2024-09-05
- Title:
- Industrial process fault detection based on bidirectional generative adversarial networks
- Keywords:
- fault detection; generative adversarial networks; long short-term memory network; autoencoders; reconstruction error; time series; TE process; coal pulverization process
- 分类号:
- TP277
- 文献标志码:
- 2024-08-29
- 摘要:
- 标准双向生成对抗网络的模型结构由全连接层构成，在进行故障检测时仅使用单个样本的过程特征进行统计量构建。因此，提出了一种改进双向生成对抗网络的工业过程故障检测方法。该方法用降噪自编码器对样本进行预处理，构建重构误差作为双向生成对抗网络的输入，以减少异常样本中正常信息对异常信息的淹没，增强模型对微小故障的检测能力；并将长短时编解码器引入双向生成对抗网络模型中，使得生成器在生成虚假样本的同时还可以关注当前时刻样本的过程特征和历史时刻样本间的关联性，增强了模型对时间序列数据的检测能力。将所提故障检测方法应用于田纳西伊斯曼过程和实际磨煤机工业过程，其在保证低误报率的同时，提升了报警率，并且具有良好的泛化性能。
- Abstract:
- The standard bidirectional generative adversarial network (BiGAN) model structure consists of a fully connected layer and only considers the process characteristics of a single sample for statistical construction during fault detection. Therefore, an improved bidirectional generative adversarial network for industrial process fault detection is proposed. The method preprocesses the samples using a denoising autoencoder and constructs the reconstruction error as the input for the BiGAN. This approach reduces the masking of abnormal information by normal information in abnormal samples and enhances the ability of the model to detect small faults. Additionally, a long short-term codec is introduced into the BiGAN model, which enables the generator to focus on both the current process characteristics and the correlation between historical samples while generating false samples. This improves the ability of the model to detect time-series data. The proposed fault detection method was applied to the Tennessee Eastman process and an actual coal mill industrial process. This method improved the alarm rate while maintaining a low false alarm rate, which demonstrates good generalization performance.
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备注/Memo
收稿日期:2023-6-7。
基金项目:国家自然科学基金项目(61773182).
作者简介:牟建鹏，硕士研究生，主要研究方向为复杂工业过程建模和故障诊断。E-mail：moujianpeng123@163.com;刘文韬，博士研究生, 主要研究方向为系统辨识、复杂工业过程建模。E-mail：wtliu@126.com;熊伟丽，教授，博士生导师，主要研究方向为复杂工业过程建模与监控、智能软测量技术。获得江苏省科学技术二等奖1 项，授权发明专利30项，发表学术论文近百篇。E-mail：greenpre@163.com。
通讯作者:熊伟丽. E-mail：greenpre@163.com
更新日期/Last Update:
2024-09-05