LAUSR

One of the main concerns associated with diagnosis, prognosis, and health management (DPHM) of engineering systems is the accuracy of estimates that are derived from Bayesian tracking methods. Estimating the exiting degradation based on stochastic models and evaluating the remaining useful life (RUL) of the system is inherently associated with variances that characterize the inaccuracy of estimation techniques. Furthermore, there are scenarios where a single measurement does not necessarily generate sufficient information regarding the system states, leading one to require multiple readings (and, hence, multidimensional analysis) to deduce diagnostic and/or prognostic decisions. This article introduces a novel approach for solving complex nonlinear multivariable Bayesian models that are utilized for estimation and prediction problems that would be, otherwise, challenging or impractical to solve through available methods, such as particle filters (PFs). Theoretical derivation and strategies that are developed in this article are verified through numerical case study simulations for electrohydraulic servo valves (EHSVs) that constitute a core component of many hydraulic actuators, such as multifunctional spoilers (MFSs), which are widely utilized in aircraft flight control systems. Our developed results are compared with those that are derived through PF in order to illustrate and demonstrate the advantages, benefits, and improvements that are accomplished by applying our proposed methodologies.