Determining the Reuse Potential of Components Based on Life Cycle Data

Reuse of components is one of the most efficient strategies for product recovery, which requires reliable methods for assessing the quality and the remaining life of used components. A new methodology, presented in this paper, is based on the trend analysis of lifetime monitoring data. Data with s...

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Main Authors: Kara, S., Mazhar, Ilyas, Kaebernick, H., Ahmed, A.
Format: Journal Article
Published: CIRP 2005
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/22813
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author Kara, S.
Mazhar, Ilyas
Kaebernick, H.
Ahmed, A.
author_facet Kara, S.
Mazhar, Ilyas
Kaebernick, H.
Ahmed, A.
author_sort Kara, S.
building Curtin Institutional Repository
collection Online Access
description Reuse of components is one of the most efficient strategies for product recovery, which requires reliable methods for assessing the quality and the remaining life of used components. A new methodology, presented in this paper, is based on the trend analysis of lifetime monitoring data. Data with similar trends were grouped and a number of analysis techniques such as Linear Multiple Regression, Dynamic Ordinary Kriging, Universal Kriging and Neural Networks were applied in order to find the most suitable methodology for each group. The methodology was validated by using lifetime monitoring data from a consumer product.
first_indexed 2025-11-14T07:45:23Z
format Journal Article
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T07:45:23Z
publishDate 2005
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spelling curtin-20.500.11937-228132017-02-28T01:36:31Z Determining the Reuse Potential of Components Based on Life Cycle Data Kara, S. Mazhar, Ilyas Kaebernick, H. Ahmed, A. Condition Monitoring Life Cycle Reuse Reuse of components is one of the most efficient strategies for product recovery, which requires reliable methods for assessing the quality and the remaining life of used components. A new methodology, presented in this paper, is based on the trend analysis of lifetime monitoring data. Data with similar trends were grouped and a number of analysis techniques such as Linear Multiple Regression, Dynamic Ordinary Kriging, Universal Kriging and Neural Networks were applied in order to find the most suitable methodology for each group. The methodology was validated by using lifetime monitoring data from a consumer product. 2005 Journal Article http://hdl.handle.net/20.500.11937/22813 CIRP restricted
spellingShingle Condition Monitoring
Life Cycle
Reuse
Kara, S.
Mazhar, Ilyas
Kaebernick, H.
Ahmed, A.
Determining the Reuse Potential of Components Based on Life Cycle Data
title Determining the Reuse Potential of Components Based on Life Cycle Data
title_full Determining the Reuse Potential of Components Based on Life Cycle Data
title_fullStr Determining the Reuse Potential of Components Based on Life Cycle Data
title_full_unstemmed Determining the Reuse Potential of Components Based on Life Cycle Data
title_short Determining the Reuse Potential of Components Based on Life Cycle Data
title_sort determining the reuse potential of components based on life cycle data
topic Condition Monitoring
Life Cycle
Reuse
url http://hdl.handle.net/20.500.11937/22813