基于WGAN的多维数据生成方法及其在RUL预测中的应用

doi:10.3901/JME.2025.16.070

摘要/Abstract

摘要： 考虑到大数据背景下随机退化设备监测数据呈现出“碎片化、高维化、不完整”的特点，提出一种存在数据缺失情形下的随机退化设备剩余寿命(Remaining useful life, RUL)预测方法。面向非理想的传感器数据，基于Wasserstein生成对抗网络(Wasserstein generative adversarial network, WGAN)分析利用数据隐藏信息，扩充补全不连续的时间序列，从而改善数据质量。此外，考虑到多性能退化变量相互耦合、相互影响，采用Hausdorff距离分别从时间和空间属性上衡量数据之间的多维相似度，深度挖掘各变量之间的潜在关联。最后，融合多源传感器监测数据，构建设备性能退化模型，通过退化特征首达失效阈值的时间实现RUL预测。

关键词: 大数据, 数据缺失, Wasserstein生成对抗网络, 多维相似度, 剩余寿命预测

Abstract: Considering that the monitoring data of random degraded equipment under the background of big data presents the characteristics of “fragmented, high-dimensional, and incomplete”, a method for the remaining useful life(RUL) prediction of random degraded equipment in the presence of missing data is proposed. First, for non-ideal sensor data, based on Wasserstein generative adversarial network(WGAN) analysis the hide information of data, expand and complete discontinuous time series, thereby improving data quality. In addition, considering the mutual coupling and mutual influence of multiple performance degradation variables, the Hausdorff distance is used to measure the multi-dimensional similarity between the data from the time and space attributes, and the potential associations between the variables are deeply explored. Finally, the multi-source sensor monitoring data is merged to construct the equipment performance degradation model, and the RUL prediction is realized by the time when the degradation feature first reaches the failure threshold.

Key words: big data, data missing, Wasserstein generative adversarial network (WGAN), multi-dimensional similarity, remaining useful life (RUL) prediction

中图分类号:

TG156

张晟斐, 李天梅, 胡昌华, 司小胜, 张博玮. 基于WGAN的多维数据生成方法及其在RUL预测中的应用[J]. 机械工程学报, 2025, 61(16): 70-82.

ZHANG Shengfei, LI Tianmei, HU Changhua, SI Xiaosheng, ZHANG Bowei. Multidimensional Data Generation Method Based on WGAN and Its Application in RUL Prediction[J]. Journal of Mechanical Engineering, 2025, 61(16): 70-82.

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