基于TimeGAN数据增强的复杂过程故障分类方法

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中文摘要：针对传统基于重构的故障分类方法在故障样本稀疏或失衡情况下效果不佳、故障子空间区分能力弱的问题，提出基于TimeGAN数据增强的复杂过程故障分类方法。针对小子样故障，使用TimeGAN对历史故障数据进行数据增强，生成与历史数据分布相似的虚拟故障样本;采用马氏距离评估虚拟样本的质量，剔除不可信样本，构造平衡的故障样本集。将故障样本映射到高维核空间，并在核空间中提取故障子空间。设计故障分类策略并定义 4 种故障分类性能评估指标以定量衡量算法的分类性能。Tennessee Eastman应用结果表明，所提数据增强方法可以有效扩充故障样本，进而提高故障重构率。与WGAN-GP和SMOTE方法进行对比，发现基于TimeGAN数据增强的故障分类方法具有更好的分类性能。

外文标题：TimeGAN data augmentation-based fault classification method for complex processes

外文摘要：Traditional reconstruction-based fault classification methods failed to function effectively when the fault samples were sparse or imbalanced,and the fault subspace was weakly discriminative.Aiming at the above problems,a TimeGAN data augmentation-based fault classification method for complex processes was proposed.Firstly,for less sub-sampled faults,the data enhancement of historical fault data was performed using TimeGAN to generate virtual fault samples with similar distribution to historical data.The Mahalanobis distance was used to evaluate the quality of the virtual samples,eliminating the unreliable samples and further constructing a balanced fault sample set.Secondly,the fault samples were mapped to a high-dimensional kernel space,from which the fault subspace was extracted.Finally,a fault classification strategy was designed,and four fault classification performance evaluation metrics were defined to quantify the classification performance.Results of the Tennessee Eastman showed that the proposed TimeGAN can effectively enlarge the fault samples and improve the fault reconstruction rate.Compared to the WGAN-GP and SMOTE,the fault classification method based on TimeGAN data augmentation had better classification performance.

外文关键词：

fault classificationimbalanced sampledata augmentationfault subspacetime-series generative adversarial network

作者：

杨磊、何鹏举、丑幸幸

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作者单位：

山西大同大学物理与电子科学学院,山西大同 037009

西北工业大学自动化学院,陕西西安 710072

西北工业大学深圳研究院,广东深圳 518057

陕西科技大学电气与控制工程学院,陕西西安 710054

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关键词：

故障分类样本不平衡数据增强故障子空间时间序列生成对抗网络

基金：

山西省高等学校科技创新资助项目西安市科技计划资助项目深圳市科技计划资助项目山西大同大学科研基金资助项目

项目编号：

2022L4362017086CG/RC049JCYJ201703061546114152020CXZ2

出版年：

2024

DOI：

10.3785/j.issn.1008-973X.2024.09.002

浙江大学学报(工学版)

浙江大学

浙江大学学报(工学版)

CSTPCD北大核心

影响因子：0.625

ISSN：1008-973X

年,卷(期)：2024.58(9)

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