Improved product reliability quantification methodology making use of physics of failure based prognostics

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Jul 5, 2016
Abdellatif Bey-Temsamani Stijn Helsen Steven Kauffmann Anke Van Campen

Abstract

Quantifying accurate reliability at (sub-)system level is not an easy task. Despite the availability of different tools allowing reliability estimation, e.g. reliability handbooks as MIL217-F, the accuracy of the obtained results is not guaranteed. For instance, the data used in these handbooks are outdated, referring to old technologies and assuming stresses that are not always realistic. Other methods exist which should allow a more accurate reliability estimation e.g. the physics of failure prognostics. However, for an industrial end user, following such an approach at (sub) system level is too expensive. Typical steps to obtain reliability data of one component following physics of failure prognostic approach would require (i) understanding a given failure mechanism and developing its corresponding physics of failure model, (ii) identifying stress accelerators of this failure mechanism, and (iii) planning and implementing an accelerated life test to collect failure data in order to validate the model. A typical accelerated life test would require failures of components collected during the test time (in the order of months) at different stress levels. Another approach to get more accurate reliability at (sub-)system level is collecting and analyzing field data. However, this would require a complete process within an organization, by tracking the products in the field and collecting failure information for many years.
In order to overcome these limitations for companies, we propose a methodology allowing to obtain quick and accurate estimations of the (sub-) system reliability by combining
component’s reliability information from different sources, e.g. using physics-of-failure models for some critical components where test data are historically available, and / or using reliability prediction handbook for proven in use components, and / or using field data if available.

How to Cite

Bey-Temsamani, A., Helsen, S., Kauffmann, S., & Campen, A. V. (2016). Improved product reliability quantification methodology making use of physics of failure based prognostics. PHM Society European Conference, 3(1). https://doi.org/10.36001/phme.2016.v3i1.1669
Abstract 107 | PDF Downloads 529

##plugins.themes.bootstrap3.article.details##

Keywords

physics of failure, prognostics, reliability, diagnostics

References
Abernethy. (2006). The new Weibull handbook.
Ashburn. (2008). High accelerated testing of capacitors for medical applications. Proceedings of the 5th SMTA Medical Electronics Symposium.
Barnard. (2008). Integration of reliability engineering into product development. 2nd SAIAS symposium, (pp. 14-16). Stellenbosch.
Bayle. (2010). Temperature acceleration models in reliability predictions: justification and improvements. Reliability and Maintainability symposium (RAMS).
Charpenel. (2003). The right way to assess electronic system reliability: FIDES. Microelectronics reliability Vol. 43, 1041-1404.
Coit. (2005). Method for correlating field life degradation with reliability prediction for electronic modules. Quality and reliability engineering international.
FIDES. (2004). FIDES guide: reliability methodology for electronic systems. FIDES.
Guo. (2012). Design of experiments and data analysis. AR & MS.
IEC. (2004). IEC TR 62380: reliability data handbook - universal model for reliability prediction of electronics components, PCBs and equipment. IEC.
Jiang. (2011). A study of Weibull shape parameter: properties and significance. Reliability engineering and system safety.
Lu. (2000). Accelerated stress testing in a time-driven product development process. International journal of production economics.
Marin. (2005). Experinec report on the FIDES reliability prediction method. IEEE Annual reliability and maintainability symposium, (pp. 8-13).
McLean. (2009). HALT, HASS and HASA explained.
McLean. (2010). From HALT results to an accurate field MTBF estimate. Reliability and maintainability symposium (RAMS). IEEE.
MIL. (1995). Military Handbook reliability prediction for electronic components MIL HDBK217F notice 2.
Porter. (2001). Using accelerated testing methods to improve electronics design. Compliance Engineering.
Reliasoft. (2007). Life data analysis reference. Tucson: Reliasoft Corporation.
Ryu. (2005). Novel concepts for reliqbility technology. Microelectronics reliability.
Shi. (2009). Research on Pre-HALT analysis and the application of test data in MTBF evaluation . ICRMS.
Wang. (2004). Reliability block diagram simulation techniques applied to the IEEE std 493 standard network. IEEE transactions on indsutry applications , 887-895.
Winkelbauer. (2006). Developments in Risk-based Approaches to Safety. Proceedings of the Fourteenth Safety-citical Systems Symposium. Bristol.
Wohlgemuth. (2011). Using accelerated testing to predict Photovoltaic module reliability . 37th IEEE Photovoltaic Specialists Conference (PVSC 37). Seattle.
Section
Technical Papers