Abstract
For (electrical) infrastructure assets a high reliability is required. When assets cannot be monitored and no condition information is available, statistical methods can be used to assess their condition. This paper discusses statistical methods to assess the condition of LV cables, which is a typical example of electric components with a very low failure rate. Two survival methods and three classification methods are compared. The Cox Proportional Hazard model proved to be the most suitable. The influence of variables on the failure probability provides insight in failure mechanisms and asset groups which have a high failure probability. In this paper it is proved that a great amount of information can be gained using survival analysis for ageing infrastructure assets.
| Original language | English |
|---|---|
| Title of host publication | 2019 IEEE Milan PowerTech, PowerTech 2019 |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-5386-4722-6 |
| DOIs | |
| Publication status | Published - 1 Jun 2019 |
| Event | 2019 IEEE Milan PowerTech, PowerTech 2019 - Milan, Italy Duration: 23 Jun 2019 → 27 Jun 2019 |
Conference
| Conference | 2019 IEEE Milan PowerTech, PowerTech 2019 |
|---|---|
| Country | Italy |
| City | Milan |
| Period | 23/06/19 → 27/06/19 |
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Keywords
- Asset management
- Big data applications
- Power distribution
- Power grids
- Regression analysis
Cite this
}
Statistical methods for condition assessment of low-failure assets. / Klerx, Maikel; Morren, Johan; Slootweg, Han.
2019 IEEE Milan PowerTech, PowerTech 2019. Piscataway : Institute of Electrical and Electronics Engineers, 2019. 8810966.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Statistical methods for condition assessment of low-failure assets
AU - Klerx, Maikel
AU - Morren, Johan
AU - Slootweg, Han
PY - 2019/6/1
Y1 - 2019/6/1
N2 - For (electrical) infrastructure assets a high reliability is required. When assets cannot be monitored and no condition information is available, statistical methods can be used to assess their condition. This paper discusses statistical methods to assess the condition of LV cables, which is a typical example of electric components with a very low failure rate. Two survival methods and three classification methods are compared. The Cox Proportional Hazard model proved to be the most suitable. The influence of variables on the failure probability provides insight in failure mechanisms and asset groups which have a high failure probability. In this paper it is proved that a great amount of information can be gained using survival analysis for ageing infrastructure assets.
AB - For (electrical) infrastructure assets a high reliability is required. When assets cannot be monitored and no condition information is available, statistical methods can be used to assess their condition. This paper discusses statistical methods to assess the condition of LV cables, which is a typical example of electric components with a very low failure rate. Two survival methods and three classification methods are compared. The Cox Proportional Hazard model proved to be the most suitable. The influence of variables on the failure probability provides insight in failure mechanisms and asset groups which have a high failure probability. In this paper it is proved that a great amount of information can be gained using survival analysis for ageing infrastructure assets.
KW - Asset management
KW - Big data applications
KW - Power distribution
KW - Power grids
KW - Regression analysis
UR - http://www.scopus.com/inward/record.url?scp=85072344569&partnerID=8YFLogxK
U2 - 10.1109/PTC.2019.8810966
DO - 10.1109/PTC.2019.8810966
M3 - Conference contribution
AN - SCOPUS:85072344569
BT - 2019 IEEE Milan PowerTech, PowerTech 2019
PB - Institute of Electrical and Electronics Engineers
CY - Piscataway
ER -