Prognostics and health management enables the prediction of future degradation and remaining useful life (RUL) for in-service systems based on historical and contemporary data, showing promise for many practical applications. One major challenge for prognostics is the common occurrence of missing values in time-series data, often caused by disruptions in sensor communication or hardware/software failures. Another major concern is that the sufficient prior knowledge of critical component degradation with a clear failure threshold is often not readily available in practice. These issues can significantly hinder the application of advanced signal and data analysis methods and consequently degrade the health management performance. In this article, we propose a novel data-driven framework that is capable of providing accurate and reliable predictions of degradation and RUL. In this approach, one-hot health state indicators are appended to the historical time series so that the model learns end-of-life automatically. A modified gate recurrent unit based variational autoencoder is employed in generative adversarial networks to model the temporal irregularity of the incomplete time series. Experiments on multivariate time-series datasets collected from real-world aeroengines verify that significant performance improvement can be achieved using the proposed model for robust long-term prognostics.

中文翻译：

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在缺失数据的情况下进行可靠的机器预测健康管理

预测和健康管理能够基于历史和当代数据预测在役系统的未来退化和剩余使用寿命 (RUL)，显示出许多实际应用的前景。预测的一个主要挑战是时间序列数据中经常出现缺失值，这通常是由传感器通信中断或硬件/软件故障引起的。另一个主要问题是，在实践中通常不容易获得具有明确故障阈值的关键部件退化的充分先验知识。这些问题会严重阻碍先进信号和数据分析方法的应用，从而降低健康管理绩效。在本文中，我们提出了一种新颖的数据驱动框架，能够提供准确可靠的退化和 RUL 预测。在这种方法中，将单热健康状态指标附加到历史时间序列中，以便模型自动学习寿命终止。在生成对抗网络中采用了一种基于改进门循环单元的变分自动编码器来模拟不完整时间序列的时间不规则性。对从现实世界航空发动机收集的多元时间序列数据集进行的实验证实，使用所提出的模型可以实现显着的性能改进，以实现稳健的长期预测。在生成对抗网络中采用了一种基于改进门循环单元的变分自动编码器来模拟不完整时间序列的时间不规则性。对从现实世界航空发动机收集的多元时间序列数据集进行的实验证实，使用所提出的模型可以实现显着的性能改进，以实现稳健的长期预测。在生成对抗网络中采用了一种基于改进门循环单元的变分自动编码器来模拟不完整时间序列的时间不规则性。对从现实世界航空发动机收集的多元时间序列数据集进行的实验证实，使用所提出的模型可以实现显着的性能改进，以实现稳健的长期预测。