Recent developments in big data analysis, machine learning, Industry 4.0, and IoT applications have enabled the monitoring and processing of multi-sensor data collected from systems, allowing for the prediction of the "Remaining Useful Life" (RUL) of system components. Particularly in the aviation industry, Prognostic Health Management (PHM) has become one of the most important practices for ensuring reliability and safety. Not only is the accuracy of RUL prediction important, but the implementability of techniques, domain adaptability, and interpretability of system degradation behaviors have also become essential. In this paper, the data collected from the multi-sensor environment of complex systems are processed using a Functional Data Analysis (FDA) approach to predict when the systems will fail and to understand and interpret the systems' life cycles. The approach is applied to the C-MAPSS datasets shared by National Aeronautics and Space Administration, and the behaviors of the sensors in aircraft engine failures are adaptively modeled with Multivariate Functional Principal Component Analysis (MFPCA). While the results indicate that the proposed method predicts the RUL competitively compared to other methods in the literature, it also demonstrates how multivariate Functional Data Analysis is useful for interpretability in prognostic studies within multi-sensor environments.

翻译：近年来，大数据分析、机器学习、工业4.0和物联网应用的发展使得对系统采集的多传感器数据进行监测与处理成为可能，从而能够预测系统组件的"剩余使用寿命"。特别是在航空工业中，预测性健康管理已成为确保可靠性与安全性的最重要实践之一。剩余使用寿命预测的准确性固然重要，但技术的可实施性、领域适应性以及系统退化行为的可解释性也变得至关重要。本文采用函数数据分析方法处理从复杂系统多传感器环境采集的数据，以预测系统故障时间并理解与解释系统生命周期。该方法应用于美国国家航空航天局共享的C-MAPSS数据集，通过多元函数主成分分析对航空发动机故障中传感器的行为进行自适应建模。结果表明，与文献中的其他方法相比，所提出的方法在剩余使用寿命预测方面具有竞争力，同时证明了多元函数数据分析在多传感器环境下的预测性研究中如何有助于实现可解释性。