|This is just an Excerpt from a larger document, click here to view the entire document.
2. Methodology Overview|
The 217Plus™ methodology is illustrated in Figure 2.
The methodology is structured to allow the user the ability to estimate the reliability of a system in the early design stages when little is known about the system. For example, early in the development phase of a system, a reliability estimate can be made based on a generic parts list, using default values for operational profiles and stresses. When additional information becomes available, the model allows the incremental addition of data.
The purpose of 217Plus™ is to provide an engineering tool to assess the reliability of electronic systems. It is not intended to be the "standard" prediction methodology, and it can be misused if applied carelessly. It does not consider the effect of redundancy or perform FMEAs. The intent of 217Plus™ is to provide the data necessary to feed these analyses.
The methodology allows modifying a base reliability estimate with process grading factors for the following failure causes: parts, design, manufacturing, system management, wearout, induced and no defect found. These process grades correspond to the degree to which actions have been taken to mitigate the occurrence of system failure due to these failure categories. Once the base estimate is modified with the process grades, the reliability estimate is further modified by empirical data taken throughout system development and testing. This modification is accomplished using Bayesian techniques which apply the appropriate weights for the different data elements.
Advantages of this new methodology are that it uses all available information to form the best estimate of field reliability, is tailorable, has quantifiable confidence bounds, and has sensitivity to the predominant system reliability drivers.
The new model adopts a broader scope to predicting reliability. It factors in all available reliability data as it becomes available on the program. It thus integrates test and analysis data, which provides a better prediction foundation and a means of estimating variances from different reliability measures.
Figure 2: 217Plus™ Methodology (Click to Zoom)