An Adaptive Modeling for Robust Prognostics on a Reconfigurable Platform
| Main Author: | |
|---|---|
| Language: | English |
| Published: |
University of Cincinnati / OhioLINK
2010
|
| Subjects: | |
| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=ucin1276535854 |
| id |
ndltd-OhioLink-oai-etd.ohiolink.edu-ucin1276535854 |
|---|---|
| record_format |
oai_dc |
| spelling |
ndltd-OhioLink-oai-etd.ohiolink.edu-ucin12765358542021-08-03T06:14:05Z An Adaptive Modeling for Robust Prognostics on a Reconfigurable Platform Liao, Linxia Industrial Engineering Prognostics Adaptive Modeling Reconfigurable Platform Prognostics focus on failure prediction in order to prevent unexpected machine downtime; which can have major impact on costs in industry. Despite progress made in recent decades, many prognostics techniques have had limited success due to the reliance on ad hoc approaches. Thenovel adaptive prognostics framework presented in this dissertation can provide robust prognostic information and is capable of being reconfigured for diverse applications. The proposed framework uses a self-organizing map (SOM) based method to quantitatively assess degradation statuses, based on which a reinforcement learning agent is trained to provide guidance for dynamically selecting the appropriate prediction models under various degradation statuses. Anew density estimation method, which utilizes a boosting Gaussian mixture model (GMM), is proposed to improve the prediction accuracy, after the most appropriate prediction model is determined, by taking into consideration prediction uncertainties. Case studies show theproposed method achieves the highest accuracy compared to traditional F-test, as well as several auto-regressive moving average (ARMA) models with different orders. In order to address the issue of deploying the right prognostics tools for the right applications, a methodology fordesigning the architecture of a reconfigurable prognostics platform (RPP) is also proposed. This methodology is validated in two industrial case studies, which demonstrate that the RPP is bothfeasible and effective. With a reconfigurable architecture, the proposed adaptive prognostics framework can automate the prediction model selection procedure, which enables it to be appliedto many rotary machine component applications. With an appropriate definition of the“degradation state”, the proposed methodology can be used for general time series prediction in many other areas as well. 2010-08-05 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1276535854 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1276535854 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
| collection |
NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Industrial Engineering Prognostics Adaptive Modeling Reconfigurable Platform |
| spellingShingle |
Industrial Engineering Prognostics Adaptive Modeling Reconfigurable Platform Liao, Linxia An Adaptive Modeling for Robust Prognostics on a Reconfigurable Platform |
| author |
Liao, Linxia |
| author_facet |
Liao, Linxia |
| author_sort |
Liao, Linxia |
| title |
An Adaptive Modeling for Robust Prognostics on a Reconfigurable Platform |
| title_short |
An Adaptive Modeling for Robust Prognostics on a Reconfigurable Platform |
| title_full |
An Adaptive Modeling for Robust Prognostics on a Reconfigurable Platform |
| title_fullStr |
An Adaptive Modeling for Robust Prognostics on a Reconfigurable Platform |
| title_full_unstemmed |
An Adaptive Modeling for Robust Prognostics on a Reconfigurable Platform |
| title_sort |
adaptive modeling for robust prognostics on a reconfigurable platform |
| publisher |
University of Cincinnati / OhioLINK |
| publishDate |
2010 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1276535854 |
| work_keys_str_mv |
AT liaolinxia anadaptivemodelingforrobustprognosticsonareconfigurableplatform AT liaolinxia adaptivemodelingforrobustprognosticsonareconfigurableplatform |
| _version_ |
1719433168591781888 |