Abstract
The warranty data plays a crucial role in the improvement of the manufactured product. Association rule analysis is an efficient methodology for eliciting useful information from warranty data by defining the relationships between production data and failure data within warranty period. We extract association rules from warranty data of heavy duty diesel engine in order to find significant patterns of failures along with manufacturing information. We also used Weibull regression to identify influential factors that affect the variation in mean time between failures which are identified from extracted association rules. The results from the empirical study for manufacturing firm "D" provide information as to the areas in which improvements should be made. Moreover, the result for specific failure is able to suggest a solution to overcome short-sighted improvement representatively. This study expects to contribute quality improvement to manufacturing industry which is under coarse warranty data with enormous unrevealed information to draw meaningful information with ease.
| Original language | English |
|---|---|
| Pages (from-to) | 176-183 |
| Number of pages | 8 |
| Journal | Reliability Engineering and System Safety |
| Volume | 133 |
| DOIs | |
| Publication status | Published - 2015 Jan |
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All Science Journal Classification (ASJC) codes
- Safety, Risk, Reliability and Quality
- Industrial and Manufacturing Engineering
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Product failure pattern analysis from warranty data using association rule and Weibull regression analysis : A case study. / Jeon, Jeasu; Sohn, So Young.
In: Reliability Engineering and System Safety, Vol. 133, 01.2015, p. 176-183.Research output: Contribution to journal › Article
TY - JOUR
T1 - Product failure pattern analysis from warranty data using association rule and Weibull regression analysis
T2 - A case study
AU - Jeon, Jeasu
AU - Sohn, So Young
PY - 2015/1
Y1 - 2015/1
N2 - The warranty data plays a crucial role in the improvement of the manufactured product. Association rule analysis is an efficient methodology for eliciting useful information from warranty data by defining the relationships between production data and failure data within warranty period. We extract association rules from warranty data of heavy duty diesel engine in order to find significant patterns of failures along with manufacturing information. We also used Weibull regression to identify influential factors that affect the variation in mean time between failures which are identified from extracted association rules. The results from the empirical study for manufacturing firm "D" provide information as to the areas in which improvements should be made. Moreover, the result for specific failure is able to suggest a solution to overcome short-sighted improvement representatively. This study expects to contribute quality improvement to manufacturing industry which is under coarse warranty data with enormous unrevealed information to draw meaningful information with ease.
AB - The warranty data plays a crucial role in the improvement of the manufactured product. Association rule analysis is an efficient methodology for eliciting useful information from warranty data by defining the relationships between production data and failure data within warranty period. We extract association rules from warranty data of heavy duty diesel engine in order to find significant patterns of failures along with manufacturing information. We also used Weibull regression to identify influential factors that affect the variation in mean time between failures which are identified from extracted association rules. The results from the empirical study for manufacturing firm "D" provide information as to the areas in which improvements should be made. Moreover, the result for specific failure is able to suggest a solution to overcome short-sighted improvement representatively. This study expects to contribute quality improvement to manufacturing industry which is under coarse warranty data with enormous unrevealed information to draw meaningful information with ease.
UR - http://www.scopus.com/inward/record.url?scp=84907524893&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84907524893&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2014.08.015
DO - 10.1016/j.ress.2014.08.015
M3 - Article
AN - SCOPUS:84907524893
VL - 133
SP - 176
EP - 183
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
SN - 0951-8320
ER -