Prognostic modelling for residual useful life prediction

In the context of this thesis, prognosis aims at predicting the residual useful life ofcomponents using condition monitoring information. It enables a projection ofcomponent condition from the past and present into the future, providing significantassistance to maintenance decision making and asset management. An inaccurate ordelayed prognosis might result in unexpected failure of critical assets, thus leading toenormous economic or casualty losses. In order to increase the accuracy and efficiencyof prognosis, this thesis studies new approaches for prognostic modelling of residualuseful life prediction using condition monitoring information.First, stochastic filtering models are applied for residual useful life prediction, andboth failure and censored data are utilized for model parameterization. Then, three typesof threshold based models are developed, namely an adaptive Brownian motion basedmodel, an adaptive gamma based model and an adaptive inverse Gaussian based model.The degradation processes of these models are adapted to the history of monitoredinformation, thus providing more realistic models and more accurate prognosis.In addition to these newly developed prognosis models, two developments, namely athreshold zone approach and a multiple failure modes approach, are also presented tocomplement existing models in order to accommodate more complex situations. Finally,a new proposal of model fusion is presented to combine physics of failure models anddata driven models. This type of model fusion is a new trend for condition based prognostics, and possesses the advantages of both combined models.This thesis provides several new methodologies for prognosis modelling of residualuseful life. Through comparisons with previously published models, we demonstratethat the proposed models perform reasonably well and generate more accuratepredictions. However, more real data are required to evaluate further the prognosiscapabilities of the improved models.

Level dc:type.qualificationlevel

Doctoral (Level 8)

Year dc:date.issued

2012

Author dc:creator

• Xu, W

Language dc:language

en

Identifier

oai:salford-repository.worktribe.com:1337463

OAI identifier oai:identifier

oai:salford-repository.worktribe.com:1337463