Abstract

In fault prognosis, the individual heterogeneity among degradation processes of equipment is a critical problem that decreases the reliability and stability of prognostic models. The presence of the diversity of degradation mechanisms, along with the complex temporal nature of multivariate measurements of equipment, make the existing approaches difficult to forecast the trend of health status and predict the Remaining Useful Life (RUL) of equipment. To resolve this problem, this article proposes a dual-network approach for online RUL prediction. The proposed approach predicts the RUL by constructing a recurrent neural network (RNN) and a Feedforward Neural Network (FNN) from the degradation measurements and failure occurrence data of equipment. The RNN is used to predict the evolution of degradation measurements, whereas the FNN is used to determine the failure occurrence based on the predicted measurements. Considering the individual heterogeneity problem, a novel meta-learning procedure is proposed for network training. The main idea of the meta-learning approach is to train two network generators to capture the average behavior and variation of equipment degradation, and generate dual networks dynamically tailored to different equipment in the online RUL prediction process. Numerical studies on a simulation dataset and a real-world dataset are performed for performance evaluation.

摘要

在故障预测中，设备退化过程之间的个体异质性是降低预测模型可靠性和稳定性的关键问题。退化机制多样性的存在，以及设备多变量测量的复杂时间特性，使得现有方法难以预测健康状况的趋势和预测设备的剩余使用寿命（RUL）。为了解决这个问题，本文提出了一种用于在线 RUL 预测的双网络方法。所提出的方法通过根据设备的退化测量和故障发生数据构建循环神经网络（RNN）和前馈神经网络（FNN）来预测 RUL。RNN 用于预测退化测量的演变，而 FNN 用于根据预测的测量值确定故障发生。考虑到个体异质性问题，提出了一种用于网络训练的新元学习程序。元学习方法的主要思想是训练两个网络生成器来捕捉设备退化的平均行为和变化，并在在线 RUL 预测过程中生成针对不同设备动态定制的对偶网络。对模拟数据集和真实数据集进行数值研究以进行性能评估。元学习方法的主要思想是训练两个网络生成器来捕捉设备退化的平均行为和变化，并在在线 RUL 预测过程中生成针对不同设备动态定制的对偶网络。对模拟数据集和真实数据集进行数值研究以进行性能评估。元学习方法的主要思想是训练两个网络生成器来捕捉设备退化的平均行为和变化，并在在线 RUL 预测过程中生成针对不同设备动态定制的对偶网络。对模拟数据集和真实数据集进行数值研究以进行性能评估。