Degradation modeling is a critical and challenging problem as it serves as the basis for system prognostics and evolution mechanism analysis. In practice, multiple sensors are used to monitor the status of a system. Thus, multisensor data fusion techniques have been proposed to capture comprehensive information for prognostic modeling and analysis, which aims at developing a composite health index (HI) through the fusion of multiple sensor signals. In the literature, most existing methods use a linear data-fusion model for integration of multisensor data to construct the HI, which is insufficient to model nonlinear relations between sensing signals and HI in a complicated system. This article proposes a novel data fusion method based on deep learning for HI construction for prognostic analysis. A pair of adversarial networks is proposed to enable the training procedure of neural networks. To guarantee the stability of the algorithm, we propose a root mean square propagation (i.e., RMSprop)-based sampling algorithm to estimate model parameters. A set of simulation studies and a case study on a set of degradation signals of aircraft engines are conducted. The results demonstrate that the proposed method has a significant improvement on remaining useful life prediction compared to existing data fusion methods.

中文翻译：

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一种基于深度学习的退化建模和预测数据融合方法

退化建模是一个关键且具有挑战性的问题，因为它是系统预测和演化机制分析的基础。实际上，使用多个传感器来监控系统的状态。因此，已经提出了多传感器数据融合技术来捕获用于预后建模和分析的综合信息，其目的是通过融合多个传感器信号来开发复合健康指数 (HI)。在文献中，大多数现有方法使用线性数据融合模型来集成多传感器数据来构建 HI，这不足以模拟复杂系统中传感信号与 HI 之间的非线性关系。本文提出了一种基于深度学习的新型数据融合方法，用于构建 HI 以进行预后分析。提出了一对对抗网络来实现神经网络的训练过程。为了保证算法的稳定性，我们提出了一种基于均方根传播（即RMSprop）的采样算法来估计模型参数。对飞机发动机的一组退化信号进行了一组模拟研究和一个案例研究。结果表明，与现有数据融合方法相比，所提出的方法在剩余使用寿命预测方面具有显着改进。