Some have gained popularity within industry in the past three decades. There are many different empirical methods that have been created for specific applications. The assumption is made that system or equipment failure causes are inherently linked to components whose failures are independent of each other. Some parameters in the curve function can be modified by integrating engineering knowledge.

These methods tend to present good estimates of reliability for similar or slightly modified parts. Empirical (or Standards Based) Prediction Methods Empirical prediction methods are based on models developed from statistical curve fitting of historical failure data, which may have been collected in the field, in-house or from manufacturers. Finally, we will discuss life testing methods, which are used to determine reliability by testing a relatively large number of samples at their specified operation stresses or higher stresses and using statistical models to analyze the data.

This approach is based upon an understanding of the physical properties of the materials, operation processes and technologies used in the design. Next, we will discuss physics of failure methods, which are based on root-cause analysis of failure mechanisms, failure modes and stresses. Several standards, such as MIL-HDBK-217, Bellcore/Telcordia, RDF 2000 and China 299B, are widely used for reliability prediction of electronic products. First, we will discuss empirical prediction methods, which are based on the experiences of engineers and on historical data. In this article, we will provide an overview of all three approaches. Among these approaches, three main categories are often used within government and industry: empirical (standards based), physics of failure and life testing. Several different approaches have been developed to achieve the reliability prediction of electronic systems and components.Įach approach has its unique advantages and disadvantages. Accurate prediction of the reliability of electronic products requires knowledge of the components, the design, the manufacturing process and the expected operating conditions. Once the prototype of a product is available, lab tests can be utilized to obtain more accurate reliability predictions. Aiding in business decisions such as budget allocation and scheduling. Establishing goals for reliability tests. Providing models for system reliability/availability analysis. Evaluating the feasibility of a design.Ĭomparing different designs and life-cycle costs. However, the objective of reliability prediction is not limited to predicting whether reliability goals, such as MTBF, can be reached. Historically, this term has been used to denote the process of applying mathematical models and component data for the purpose of estimating the field reliability of a system before failure data are available for the system. This leads to the concept of reliability prediction. To obtain high product reliability, consideration of reliability issues should be integrated from the very beginning of the design phase. Reliability Prediction Methods for Electronic Products ReliaSoft June 2008 In today’s competitive electronic products market, having higher reliability than competitors is one of the key factors for success. Reliasoft Weibull Crack Rating: 5,8/10 872 reviews

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