A Small Sample Data Augmentation Method for Multivariate Degradation Equipment Based on TimeGAN and OCSVM

SUN Chen-feng; LÜ Wei-min; DAI Hong-de; ZHANG Hao-chen

doi:10.12263/DZXB.20220079

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A Small Sample Data Augmentation Method for Multivariate Degradation Equipment Based on TimeGAN and OCSVM

PAPERS | 更新时间：2025-12-08

- A Small Sample Data Augmentation Method for Multivariate Degradation Equipment Based on TimeGAN and OCSVM
- ACTA ELECTRONICA SINICA Vol. 50, Issue 11, Pages: 2678-2687(2022)
- 作者机构：
  
  1.海军航空大学岸防兵学院, 山东烟台 264000
  2.西北工业大学机电学院, 陕西西安 710000
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20220079
  CLC： TP206+.3
- Received：13 January 2022，
  
  Revised：2022-03-15，
  
  Published：25 November 2022
- 稿件说明：
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孙晨峰,吕卫民,戴洪德等.一种基于TimeGAN和OCSVM的多元退化设备小子样数据增广方法[J].电子学报,2022,50(11):2678-2687.

SUN Chen-feng,LÜ Wei-min,DAI Hong-de,et al.A Small Sample Data Augmentation Method for Multivariate Degradation Equipment Based on TimeGAN and OCSVM[J].ACTA ELECTRONICA SINICA,2022,50(11):2678-2687.
孙晨峰,吕卫民,戴洪德等.一种基于TimeGAN和OCSVM的多元退化设备小子样数据增广方法[J].电子学报,2022,50(11):2678-2687. DOI： 10.12263/DZXB.20220079.

SUN Chen-feng,LÜ Wei-min,DAI Hong-de,et al.A Small Sample Data Augmentation Method for Multivariate Degradation Equipment Based on TimeGAN and OCSVM[J].ACTA ELECTRONICA SINICA,2022,50(11):2678-2687. DOI： 10.12263/DZXB.20220079.

摘要

工作在复杂环境下的多元退化设备面临失效数据少、多源信息融合准确度低和监督学习数据不平衡等问题，对此本文提出一种基于时间序列生成对抗网络（Time-series Generative Adversarial Networks， TimeGAN）与单分类支持向量机（One-Class Support Vector Machine， OCSVM）组合模型的小子样数据增广方法.方法引入了TimeGAN模型拟合真实数据时间序列相关性，从而生成新的多元退化设备数据.本文提出了一种基于最大均值差异改进方法的可信度判据，避免强相关特征对生成数据质量评价的影响，通过使用T-分布随机邻近嵌入（T-distributed Stochastic Neighbor Embedding， T-SNE）和全局最大均值差异（Global Maximum Mean Discrepancy， GMMD）的组合方法，定性定量地评价生成数据的质量水平.基于训练后的OCSVM模型，对生成数据进行异常检测与剔除，进一步提高生成数据的质量.以航空发动机数据集C-MAPSS为例进行方法验证分析，通过与其他数据增强模型对比验证了所提方法的可行性和有效性.

Abstract

The multivariate degradation equipment working in complex environments faces the problems of small amount of failure data

the low accuracy of multi-source information fusion and imbalanced supervised learning dataset. For these problems

a small sample data augmentation method based on the combination model of time-series generative adversarial networks(TimeGAN) and one class support vector machine(OCSVM) is designed. TimeGAN is introduced to fit the time series correlation and generate the new degradation data. A new credibility criterion based on improved maximum mean discrepancy is proposed to avoid the strong correlation influence for the data quality evaluation. The combination method of t-distributed stochastic neighbor embedding(T-SNE) and global maximum mean discrepancy(GMMD) is applied to evaluate the quality of generation dataset qualitatively and quantitatively. The trained OCSVM is used to detect and remove the novelty data to further improve dataset quality. The comparison of the method and other data generation models on aircraft engine dataset C-MAPSS verifies its feasibility and effectiveness.

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Keywords

references

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