Analyzing Field Failure Data of Complex Repairable Systems for Failure Predictions

被引：0
|
作者：
Yilmaz, Ozlem [1 ]
机构：
[1] ASELSAN Inc, Radar & Elect Warfare Syst Business Sect, Integrated Logist Support Directorate, TR-06830 Ankara, Turkey
来源：
2019 ANNUAL RELIABILITY AND MAINTAINABILITY SYMPOSIUM (RAMS 2019) - R & M IN THE SECOND MACHINE AGE - THE CHALLENGE OF CYBER PHYSICAL SYSTEMS | 2019年
关键词：
Crow-AMSAA; Repairable Systems; Failure Trend;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we consider the field failure data of a product that consists of a specific amount of complex repairable systems and use Crow-AMSAA method in order to observe the trend and predict the future failures. By using cumulative failures and cumulative time data we create Crow-AMSAA plot. We have observed that there are cusps (turning points) in the plot and we have partitioned the data into different regions in order to analyze it further. We create multiple linear fits to these regions, observe the changes in the number of failures through time and interpret the findings. Lastly, we comment on our findings, share experiences, and provide some possible ways to improve the study further.
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页数：5
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