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The goal of an OEM electrical/electronics (E/E) platform organization is to release reliable E/E systems that achieve high levels of customer satisfaction with minimum investment and system cost. Achieving this goal is made more challenging by rapid advances in E/E technology and features which impact the vehicle development business environment. This paper discusses the evolution of an OEM platform organization striving to achieve E/E system integrity in an ever-changing world and eventually achieved the world class electrical quality as measured by J. D. Power. The organizational evolution progresses through a series of philosophies and methodologies, adapting new initiatives and enablers seeking continuous improvement. The result is an OEM organization with: knowledge based on lessons learned, an understanding of E/E system architecture, and enabled by models and tools to provide high levels of customer satisfaction.

The reliability of an electronic sensor in the automotive applications is assessed using data from Fleet Test and proving ground Vehicle Endurance test. These nonfailure data are multiply censored at different mileage. Reliability analysis of data with no failure is rarely discussed in most reliability literature. This paper applies the Weibull maximum likelihood analysis based on known values of the Weibull shape parameter to extract useful reliability information. The well-known Weibayes and Weibest methods are subsets of the discussed approach. The sensitivity of the change of reliability levels over a range of Weibull shape parameter values is also examined in our case. The Huang-Porter (1991) approach of obtaining a reliability lower bound regardless of the Weibull shape parameter values is also applied and its potential of practical application is discussed. Practical limitations of all methods are discussed.

A probabilistic model proposed by Wang (1990) is summarized and extended to quantify the approximate cranking probability at a specified confidence level. The criterion for acceptance is the ability of a randomly selected starter/battery set which delivers acceptable cold cranking performance on a specified engine application above a minimum targeted speed required to start. The model serves as a decision-making tool for engineers to (1) verify existing cranking system performance, (2) assess new combinations of existing cranking components, (3) evaluate the performance of newly developed cranking components, and (4) provide functional requirements of starter/battery sets for component sizing purposes. The model can be applied to perform trade-off analyses of cranking system performance with test results or computer simulation conclusions. An example is discussed and engineering usage is illustrated.