UNSPECIFIED (1999) A comparison of electronic-reliability prediction models. IEEE TRANSACTIONS ON RELIABILITY, 48 (2). pp. 127-134. ISSN 0018-9529

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Abstract

One of the most controversial procedures in reliability is the use of reliability prediction techniques based on component failure data to estimate system failure rates. The International Electronics Reliability Institute (IERI) at Loughborough University is in a unique position. Over many years, much reliability information has been collected from leading British & Danish electronic manufacturing companies. These data are of such high quality that IERI can perform the comparison exercise with many circuit boards (CB) of different types. Several CB were selected from the IERI field-failure database and their reliability was predicted and compared with the observed field-performance. The prediction techniques were based on the: M217E [US Mil-Hdbk-217E], HRD4, Siemens (SN29500), CNET, Bellcore (TR-TSY-000332) models. For each model, the associated published failure rates were used. Hence, parts count analyses were performed on several CB from the database; these analyses were compared with the field failure rate. The prediction values differ greatly from the observed field behavior and from each other. Further analysis showed that each prediction model was sensitive to widely different physical parameters. The results are summarized in table 1. Some of the models are more sensitive to a pi factor that varies according to an Arrhenius model, such as temperature and electrical stress, while others are more sensitive to the discrete pi factors used to model environment & quality.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering
Journal or Publication Title:	IEEE TRANSACTIONS ON RELIABILITY
Publisher:	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
ISSN:	0018-9529
Date:	June 1999
Volume:	48
Number:	2
Number of Pages:	8
Page Range:	pp. 127-134
Publication Status:	Published
URI:	http://wrap.warwick.ac.uk/id/eprint/14281

Data sourced from Thomson Reuters' Web of Knowledge

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