航空学报 > 2022, Vol. 43 Issue (8): 225708-225708   doi: 10.7527/S1000-6893.2021.25708

基于深度卷积生成对抗网络的缺失数据生成方法及其在剩余寿命预测中的应用

张晟斐, 李天梅, 胡昌华, 杜党波, 司小胜   

  1. 火箭军工程大学 导弹工程学院, 西安 710025
  • 收稿日期:2021-04-23 修回日期:2021-06-29 出版日期:2022-08-15 发布日期:2021-06-29
  • 通讯作者: 司小胜,E-mail:sxs09@mails.tsinghua.edu.cn E-mail:sxs09@mails.tsinghua.edu.cn
  • 基金资助:
    国家自然科学基金(61833016,62073336,61922089)

Missing data generation method and its application in remaining useful life prediction based on deep convolutional generative adversarial network

ZHANG Shengfei, LI Tianmei, HU Changhua, DU Dangbo, SI Xiaosheng   

  1. College of Missile Engineering, Rocket Force University of Engineering, Xi'an 710025, China
  • Received:2021-04-23 Revised:2021-06-29 Online:2022-08-15 Published:2021-06-29
  • Supported by:
    National Natural Science Foundation of China (61833016,62073336,61922089)

摘要: 数据驱动的设备剩余寿命(RUL)预测方法旨在通过建模分析设备运行监测数据,以确定设备剩余可用时间,因此数据的质量直接关系到预测结果的精度。随着传感器技术的发展与物联网的普及,数据规模呈井喷式增长,但海量数据往往存在数据缺失问题,这类问题将会对建模的准确性与决策的可靠性产生关键性影响。针对数据缺失下的剩余寿命预测问题,论文提出了一种基于深度卷积生成对抗网络(DCGAN)的缺失数据生成方法。该方法利用生成对抗网络(GAN)强大的学习能力,采用深度卷积网络学习真实分布并对缺失数据进行填充处理,并结合Kolmogorov-Smirnov (K-S)非参数检验的思想以改善生成对抗网络训练不稳定、建模过于自由的缺点。在此基础上,利用双向长短时记忆网络(Bi-LSTM)建立了设备退化趋势预测模型,通过预测退化量超过失效阈值的时间实现了剩余寿命的预测。最后,基于锂电池退化数据对所提方法进行了应用验证,并且通过对比完整数据、生成数据以及缺失数据下剩余寿命预测结果进一步表现本文方法在解决数据缺失问题方面的优越性,同时可以提升剩余寿命预测对数据缺失的鲁棒性。

关键词: 大数据, 数据缺失, 深度卷积生成对抗网络(DCGAN), K-S非参数检验, 剩余寿命预测

Abstract: Data-driven Remaining Useful Life (RUL) prediction methods are designed to determine the remaining useful time of the equipment through analyzing and modeling the equipment operation monitoring data; hence, data quality is directly related to prediction accuracy. The development of sensor technology and popularization of the Internet of Things induce the exponential growth of the data scale; however, massive data often face the data missing problem, imposing critical impact on the accuracy of modeling and the reliability of decision-making. Aiming at the problem of RUL prediction with missing data, this paper proposes a generation method for missing data based on the Deep Convolutional Generative Adversarial Network (DCGAN). This method uses a deep convolutional network to learn the true distribution, fills in the missing data based on the powerful learning ability of the Generative Adversarial Network (GAN), and improves the network defects of unstable training and too free modeling in combination with the idea of the Kolmogorov-Smirnov (K-S) non-parametric test. A Bidirectional Long Short-Term Memory (Bi-LSTM) network is further adopted to establish a device degradation trend prediction model, and the RUL prediction is realized by prediction of the time for the degradation to exceed the failure threshold. The proposed method is validated based on the lithium battery degradation data. The RUL prediction results with the complete data, the generated data, as well as the missing data, respectively, are compared to further demonstrate the superiority of this method in solving the missing data problem. The robustness of the RUL prediction towards missing data can also be improved.

Key words: big data, data missing, Deep Convolutional Generative Adversarial Network (DCGAN), Kolmogorov-Smirnov non-parametric test, remaining useful life prediction

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