Bayesian Hierarchical Models for aerospace gas turbine engine prognostics

Prognostics of gas turbine engine: An integrated approach

“…However, there are some issues in gas 100 turbine engine degradation that need to be considered further. This 101 paper is a continuation of our previous paper (Zaidan et al, 2015) 102 that aims to address some unsolved problems in gas turbine engine 103 prognostics. Several events may affect health index and the degrada-104 tion pattern of a complex system such as a gas turbine engine.…”

“…7a), BR-3 demon-478 strates similar performance with Int-BR-3. This is because BR-3 itself 479 already has a common prior which is able to share the information 480 across the engines to provides appropriate prediction for this sce-481 nario as described in Zaidan et al (2015). In Fig.…”

“…In this paper, a Bayesian Hierarchical Model will be 92 used as a main prognostic algorithm. The brief concept of BHM for 93 prognostics will be discussed briefly in Section 3.1, whereas the de-94 tails of this algorithm can be found in Zaidan et al (2015). 95 2.2.…”

Residual life prediction for highly reliable products with prior accelerated degradation data

“…To resolve the problem, we adopted a Bayesian method to obtain more credible posterior estimates of µ σ , ,rˆˆ by making full use of historical accelerated degradation data. Although the application of Wiener processes in Bayesian inference has been widely studied in literature, most works assume that the random parameters of a Wiener process obey the following conjugate prior [25,34].…”

Remaining Useful Life Prediction of Gas Turbine Engine using Autoregressive Model

“…These include support vector machines (SVM) [1,[7][8][9], Bayesian forecasting [10][11][12], Kalman Filters [10], state-space models [12], artificial neural networks [13][14][15][16], independent component analysis [17], regression techniques [18], Dempster-Shafer regression [1] and one parameter double exponential smoothing [10]. These techniques can be classified into two types: (i) physics based modelling approaches, and (ii) data driven models [5,[19][20][21].…”