Multivariate performance reliability prediction in real-time

doi:10.1016/S0951-8320(00)00102-2

Multivariate performance reliability prediction in real-time

2001-04-01

130

PRE

AI

DOI：10.1016/S0951-8320(00)00102-2

原文链接

原文求助

摘要

En 中文

This paper presents a technique for predicting system performance reliability in real-time considering multiple failure modes. The technique includes on-line multivariate monitoring and forecasting of selected performance measures and conditional performance reliability estimates. The performance measures across time are treated as a multivariate time series. A state-space approach is used to model the multivariate time series. Recursive forecasting is performed by adopting Kalman filtering. The predicted mean vectors and covariance matrix of performance measures are used for the assessment of system survival/reliability with respect to the conditional performance reliability. The technique and modeling protocol discussed in this paper provide a means to forecast and evaluate the performance of an individual system in a dynamic environment in real-time. The paper also presents an example to demonstrate the technique. (C) 2001 Elsevier Science Ltd. All rights reserved.

Keyword:

performance reliability

survival assessment

multivariate time series analysis

forecasting and prediction

Multivariate performance reliability prediction in real-time

摘要

AI总结

期刊

机构

引用论文