Prognostic modelling for residual useful life prediction

Xu, W

Abstract

In the context of this thesis, prognosis aims at predicting the residual useful life of
components using condition monitoring information. It enables a projection of
component condition from the past and present into the future, providing significant
assistance to maintenance decision making and asset management. An inaccurate or
delayed prognosis might result in unexpected failure of critical assets, thus leading to
enormous economic or casualty losses. In order to increase the accuracy and efficiency
of prognosis, this thesis studies new approaches for prognostic modelling of residual
useful life prediction using condition monitoring information.
First, stochastic filtering models are applied for residual useful life prediction, and
both failure and censored data are utilized for model parameterization. Then, three types
of threshold based models are developed, namely an adaptive Brownian motion based
model, an adaptive gamma based model and an adaptive inverse Gaussian based model.
The degradation processes of these models are adapted to the history of monitored
information, thus providing more realistic models and more accurate prognosis.
In addition to these newly developed prognosis models, two developments, namely a
threshold zone approach and a multiple failure modes approach, are also presented to
complement existing models in order to accommodate more complex situations. Finally,
a new proposal of model fusion is presented to combine physics of failure models and
data 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 residual
useful life. Through comparisons with previously published models, we demonstrate
that the proposed models perform reasonably well and generate more accurate
predictions. However, more real data are required to evaluate further the prognosis
capabilities of the improved models.

Citation

Xu, W. Prognostic modelling for residual useful life prediction. (Thesis). University of Salford

• Thesis Type: Thesis
• Deposit Date: Aug 13, 2021
• Award Date: Jun 1, 2012