A Prognostic Modeling Methodology for Multistream Degradation-based Signals

制造业和服务业中使用的高价值工程资产越来越多地配备有数百个传感器，用于状态监测和预测剩余寿命，即，弹道学。 故障学的基本思想是传感器数据通常包含可用于预测的独特的基于故障的特征。 如今，多个传感器越来越多地用于监测单个机器中降解过程的不同方面，因此，利用嵌入在这些传感器中的组合信息非常重要。 然而，迄今为止开发的大多数现有预测模型都集中在单传感器应用上，并且没有考虑任何数据质量问题。 该奖项支持基础研究，为数据观测可能稀疏和/或缺失的多流传感器信号提供预测模型;这一方面一直在挑战现实世界应用中的预测学。 这项研究将使制造业和服务业的许多行业能够提高设备可用性，防止灾难性故障，并降低维护成本。 研究结果也将用于推进工程教育，将这项工作的结果纳入研究生课程。几乎所有的退化建模方法都是基于一个在现实中很少得到满足的关键假设;传感器数据的质量很高，状态是连续观察的。该项目将通过放宽传统上作为发展预测模型基础的简化假设，弥合预测学领域理论与实践之间的差距。 这将通过结合简约估计方法和功能数据分析来实现，以模拟具有缺失和稀疏观测的退化信号。 具体而言，功能主成分分析将用于对不同传感器记录的多变量信号的同时变化进行建模。通过条件期望进行的主成分分析将用于解决缺失观测值带来的挑战。 将使用保序回归方法确保所得模型的单调性，这将是估计剩余寿命的关键。

High-valued engineering assets used in the manufacturing and service sectors are increasingly being instrumented with hundreds of sensors that are used for condition monitoring and predicting remaining lifetime, i.e., prognostics. The underlying idea of prognostics is that sensor data often contain unique fault-based features that can be utilized for prediction. Today, multiple sensors are increasingly being used to monitor different aspects of a degradation process in a single machine, thus, it is important to leverage the combined information embedded in these sensors. However, most of the existing prognostic models developed to date focus on single-sensor applications and do not account for any data quality issues. This award supports fundamental research to provide prognostic models for multistream sensor signals where data observations may be sparse and/or missing; an aspect that has consistently challenged the implementation of prognostics in real-world applications. This research will enable numerous industries in the manufacturing and service sectors to increase equipment availability, prevent catastrophic failures, and reduce maintenance costs. Research findings will also be used to advance engineering education by incorporating the findings of this work in graduate coursework. Almost all approaches available for degradation modeling are based on a key assumption that is rarely satisfied in reality; the quality of sensor data is high and states are observed on a continuous basis. This project will bridge the gap between theory and practice in the area of prognostics by relaxing the simplifying assumptions that have traditionally been the basis for developing prognostic models. This will be accomplished by incorporating parsimonious estimation methods and functional data analysis to model degradation signals with missing and sparse observations. Specifically, functional Principal Component Analysis will be used to model simultaneous variations of multivariate signal recorded by different sensors. Principal Components Analysis through Conditional Expectation will be used to address the challenges arising from the missing observations. Isotonic regression methods will be used to ensure monotonicity of the resulting models, which will be key in estimating remaining lifetime.