A Modular Approach for The Diagnostic Analysis of Dynamic Systems Using Time Series Models

Abstract

In many dynamic systems such as aircrafts, space vehicles, nuclear power plants and magnetohydrodynamic generators, it is often necessary to determine the cause of fluctuations in a primary variable as a function of dynamic inputs to the system and other process variables. The timely detection of the source of anomaly in the system is useful in preventing damage to the system and restoring it to normal operational mode. In this paper multivariate empirical time series models have been developed in various forms and applied to system diagnostics. Proper combination of multiple-input single-output models and multivariate feedback models are shown to give useful cause-effect relationships among dynamic variables. Recursive model parameter estimation methods, and causal flow maps are developed and applied to the diagnostics of fluctuations in the output voltage of an open-cycle magnetohydrodynamic generator.