The reliability prediction module enables you to predict failure rates for a set of components under given conditions. The data may be drawn from a selection of industry-standard handbooks such as MIl-HDBK-217, 217 Plus, Telecordia, IEC TR 62380, FIDES, SN 29500, IEC 61709 and NSWC.
This definition allows the solution mapping to bind a variable in a basic graph pattern, BGP, to a blank node in G. Since SPARQL treats blank node identifiers in a SPARQL Query Results XML Format document as scoped to the document, they cannot be understood as identifying nodes in the active graph of the dataset. If DS is the dataset of a query, pattern solutions are therefore understood to be not from the active graph of DS itself, but from an RDF graph, called the scoping graph, which is graph-equivalent to the active graph of DS but shares no blank nodes with DS or with BGP. The same scoping graph is used for all solutions to a single query. The scoping graph is purely a theoretical construct; in practice, the effect is obtained simply by the document scope conventions for blank node identifiers.
First, we will discuss empirical prediction methods, which are based on the experiences of engineers and on historical data. Several standards, such as MIL-HDBK-217, Bellcore/Telcordia, RDF 2000 and China 299B, are widely used for reliability prediction of electronic products. Next, we will discuss physics of failure methods, which are based on root-cause analysis of failure mechanisms, failure modes and stresses. This approach is based upon an understanding of the physical properties of the materials, operation processes and technologies used in the design. 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.
RDF 2000 is a reliability data handbook developed by the French telecommunications industry. This standard provides reliability prediction models for a range of electronic components using cycling profiles and applicable phases as a basis for failure rate calculations [4]. RDF 2000 provides a unique approach to handle mission profiles in the failure rate prediction. Component failure is defined in terms of an empirical expression containing a base failure rate that is multiplied by factors influenced by mission profiles. These mission profiles contain information about how the component failure rate may be affected by operational cycling, ambient temperature variation and/or equipment switch on/off temperature variations. RDF 2000 disregards the wearout period and the infant mortality stage of product life based on the assumption that, for most electronic components, the wearout period is never reached because new products will replace the old ones before the wearout occurs. For components whose wearout period is not very far in the future, the normal life period has to be determined. The infant mortality stage failure rate is caused by a wide range of factors, such as manufacturing processes and material weakness, but can be eliminated by improving the design and production processes (e.g. by performing burn-in).
In contrast to empirical reliability prediction methods, which are based on the statistical analysis of historical failure data, a physics of failure approach is based on the understanding of the failure mechanism and applying the physics of failure model to the data. Several popularly used models are discussed next.
As mentioned above, time-to-failure data from life testing may be incorporated into some of the empirical prediction standards (i.e., Bellcore/Telcordia Method II) and may also be necessary to estimate the parameters for some of the physics of failure models. However, in this section of the article, we are using the term life testing method to refer specifically to a third type of approach for predicting the reliability of electronic products. With this method, a test is conducted on a sufficiently large sample of units operating under normal usage conditions. Times-to-failure are recorded and then analyzed with an appropriate statistical distribution in order to estimate reliability metrics such as the B10 life. This type of analysis is often referred to as Life Data Analysis or Weibull Analysis.
In this article, we discussed three approaches for electronic reliability prediction. The empirical (or standards based) methods can be used in the design stage to quickly obtain a rough estimation of product reliability. The physics of failure and life testing methods can be used in both design and production stages. In physics of failure approaches, the model parameters can be determined from design specs or from test data. On the other hand, with the life testing method, since the failure data from your own particular products are obtained, the prediction results usually are more accurate than those from a general standard or model.
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. These methods tend to present good estimates of reliability for similar or slightly modified parts. Some parameters in the curve function can be modified by integrating engineering knowledge. The assumption is made that system or equipment failure causes are inherently linked to components whose failures are independent of each other. There are many different empirical methods that have been created for specific applications. Some have gained popularity within industry in the past three decades. The table below lists some of the available prediction standards and the following sections describe three of the most commonly used methods in a bit more detail.
RDF 2000 is a reliability data handbook developed by the French telecommunications industry. This standard provides reliability prediction models for a range of electronic components using cycling profiles and applicable phases as a basis for failure rate calculations 4. RDF 2000 provides a unique approach to handle mission profiles in the failure rate prediction. Component failure is defined in terms of an empirical expression containing a base failure rate that is multiplied by factors influenced by mission profiles. These mission profiles contain information about how the component failure rate may be affected by operational cycling, ambient temperature variation and/or equipment switch on/off temperature variations. RDF 2000 disregards the wearout period and the infant mortality stage of product life based on the assumption that, for most electronic components, the wearout period is never reached because new products will replace the old ones before the wearout occurs. For components whose wearout period is not very far in the future, the normal life period has to be determined. The infant mortality stage failure rate is caused by a wide range of factors, such as manufacturing processes and material weakness, but can be eliminated by improving the design and production processes (e.g. by performing burn-in).
We will give an example of using ALTA to analyze the Arrhenius model. For this example, the life of an electronic component is considered to be affected by temperature. The component is tested under temperatures of 406, 416 and 426 Kelvin. The usage temperature level is 400 Kelvin. The Arrhenius model and the Weibull distribution are used to analyze the failure data in ALTA. Figure 4 shows the data and calculated parameters. Figure 5 shows the reliability plot and the estimated B10 life at the usage temperature level.
d0d94e66b7