Prognostic Modelling of Crack Growth in a Tensioned Steel Band

Forecasting turbine problems by means of the state space framework

“…The minimisation of maintenance costs when Condition Monitoring is applied have been analysed in the literature (AlNajjar & Alsyouf, 2003;Swanson et al, 2000). There are different ways to evaluate the benefit/costs of a Condition Monitoring programme.…”

“…In Banek and Batko (1997) an optimal filter for the elimination of disturbances in the harmonics of monitored diagnostic signals is designed. Swanson, Spencer, and Arzoumanian (2000) present a method for predicting the state of damage in predictive maintenance, that is, the assessment of the likelihood of a failure as a function of future time in terms of past and current conditions. Pan and Lin (2006) develop order-tracking techniques using the VoldeKalman filter with a weighting factor to distinguish and separate both close and crossorder components in signals measured from rotary machines.…”

Prognostic modelling options for remaining useful life estimation by industry

“…Specific examples of applying Kalman filters to Bayesian networks for the purposes of residual life estimation of engineering assets can be found in [17,[85][86][87][88].…”

Novel RUL prediction of assets based on the integration of auto-regressive models and an RUSBoost classifier

“…In this way the filter can adapt to changes in the environment [9]. In [10], the Kalman Filter is utilized to predict the evolution of a crack in a tensioned steel band. A generalization of the Kalman Filter, namely the Particle Filter, is used in cases where the noise statistics are not Gaussian.…”