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Evaluating covariance in prognostic and system health management applications

机译：评估预后和系统健康管理应用程序中的协方差

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摘要

Developing a diagnostic and prognostic health management system involves analyzing system parameters monitored during the lifetime of the system. This data analysis may involve multiple steps, including data reduction, feature extraction, clustering and classification, building control charts, identification of anomalies, and modeling and predicting parameter degradation in order to evaluate the state of health for the system under investigation. Evaluating the covariance between the monitored system parameters allows for better understanding of the trends in monitored system data, and therefore it is an integral part of the data analysis. Typically, a sample covariance matrix is used to evaluate the covariance between monitored system parameters. The monitored system data are often sensor data, which are inherently noisy. The noise in sensor data can lead to inaccurate evaluation of the covariance in data using a sample covariance matrix. This paper examines approaches to evaluate covariance, including the minimum volume ellipsoid, the minimum covariance determinant, and the nearest neighbor variance estimation. When the performance of these approaches was evaluated on datasets with increasing percentage of Gaussian noise, it was observed that the nearest neighbor variance estimation exhibited the most stable estimates of covariance. To improve the accuracy of covariance estimates using nearest neighbor-based methodology, a modified approach for the nearest neighbor variance estimation technique is developed in this paper. Case studies based on data analysis steps involved in prognostic solutions are developed in order to compare the performance of the covariance estimation methodologies discussed in the paper.
机译：开发诊断和预后健康管理系统涉及分析在系统生命周期内监视的系统参数。此数据分析可能涉及多个步骤，包括数据缩减，特征提取，聚类和分类，构建控制图，异常识别以及建模和预测参数降级，以便评估所研究系统的健康状况。评估受监视系统参数之间的协方差可以更好地了解受监视系统数据中的趋势，因此，它是数据分析的组成部分。通常，样本协方差矩阵用于评估受监视系统参数之间的协方差。监视的系统数据通常是传感器数据，其固有噪声。传感器数据中的噪声会导致使用样本协方差矩阵对数据的协方差进行不正确的评估。本文研究了评估协方差的方法，包括最小体积椭球，最小协方差行列式和最近邻方差估计。当在高斯噪声百分比增加的数据集上评估这些方法的性能时，可以观察到最近邻方差估计值显示出最稳定的协方差估计值。为了提高基于最近邻的方法的协方差估计的准确性，本文针对近邻方差估计技术提出了一种改进的方法。为了比较本文讨论的协方差估计方法的性能，开发了基于预后解决方案中涉及的数据分析步骤的案例研究。

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