Raw field reliability data has been a very popular source of data on which to base
reliability estimates. This “similar data” can be based on a specific company’s own field experience on previous products or systems, or it can be a pooled set of data based on a variety of companies and organizations. As an example of the latter, one of the RIAC’s most popular documents has been the “Nonelectronic Parts Reliability Data, (NPRD)” publication. NPRD is a compilation of observed field reliability data on a wide variety of components. A summary of NPRD is provided in Section 7.4, to provide the reader with a guide to the interpretation of this type of data.
For the most part, methodologies such as EPRD (Electronic Parts Reliability Data), NPRD, MIL-HDBK-217, and 217Plus rely on field data from similar products or systems in order to make reliability estimates. The manner in which they do this differs, but they all share the same fundamental type of data as their basis.
2.5.1.2.4. Models
The use of models derived from empirical data to estimate the reliability of a product or system is just one option for estimating reliability. Empirical models can be developed and used by the analyst, or he/she can use empirical models developed by others. Models developed by others include the industry standards or methodologies that many reliability analysts are familiar with.
This section of the book deals with such models that are derived from the analysis of empirical field data. Modeling is the means by which mathematical equations are
developed for the purpose of estimating the reliability of a specific item used and applied in a specific manner. There are many ways in which models can be derived, and there is no single “correct” way to develop these models. There are many such models in
existence. These models are generally easy to use, in that they are of a closed form and simply require the analyst to identify the appropriate values of the input variables. The developers of each of these models had their own perspective in terms of the user community to be served, the variables that were to be modeled, the data that was
available, etc. It is not the intent of this book to review the specifics of these models, or to compare them in detail. It is the intent, however, to discuss the rationale and options for development of the models, and to provide some examples.
The analyst must first decide what variables are to be modeled. Factors that should be considered as indicators of reliability include:
• Environmental stresses • Operational stresses • Reliability growth • Time dependency o Infant mortality o Wearout • Engineering practices • Technology o Feature sizes o Materials • Defect rates • Yields
Which ones are actually included depend on whether the data is available to support the quantification of a factor, if a valid theoretical basis exists for its inclusion, and whether the factor can be empirically shown to be an indicator of reliability.
There are always many more potential factors influencing reliability that can realistically be included in a model. The analyst must choose which ones are considered to be the predominant reliability drivers, and include them in the model. The next step of model development is to theorize a model form. This is generally accomplished by attempting to establish a model consistent with the fundamental physics of reliability. Examples of the development of several empirically-based models are provided in Chapter 7.
To compare various empirical methodologies, Table 2.5-11 contains the predicted failure rate of various empirical methodologies for a digital circuit board. The failure rates in this table were calculated for each combination of environment, temperature and stress. As can be seen from the data, there can be significant differences between the predicted failure rate values, depending on the method used. Differences are expected because each methodology is based on unique assumptions and data. The RIAC data in the last row of the table is based on observed component failure rates in a ground benign application.
Table 2.5-11: Digital Circuit Board Failure Rates (in Failures per Million Part Hours)
Environment Ground Benign Ground Fixed Temperature 10 Deg. C 70 Deg. C 10 Deg. C 70 Deg. C
Stress 10% 50% 10% 50% 10% 50% 10% 50% ALCATEL 6.59 10.18 13.30 19.89 22.08 29.79 32.51 47.27 Bellcore Issue 4 5.72 7.09 31.64 35.43 8.56 10.63 47.46 53.14 Bellcore Issue 5 8.47 9.25 134.45 137.85 16.94 18.49 268.90 275.70 British Telecom HDR4 6.72 6.72 6.72 6.72 9.84 9.84 9.84 9.84 British Telecom HDR5 2.59 2.59 2.59 2.59 2.59 2.59 2.59 2.59 MIL-HDBK-217 E Notice 1 10.92 20.20 94.37 111.36 36.38 56.04 128.98 165.91 MIL-HDBK-217 F Notice 1 9.32 18.38 20.15 35.40 28.31 48.78 45.44 79.46 MIL-HDBK-217 F Notice 2 6.41 9.83 18.31 26.76 24.74 40.15 73.63 119.21 217Plus Version 2.0 0.28 4.89 0.51 6.04 RIAC data 3.3
For electronic systems, generic handbook models such as MIL-HDBK-217 or Telcordia SR-332 can be separated into two basic approaches, Parts Count and Parts Stress. When the models for these handbooks were developed, researchers performed statistical analyses on collected test and field data to determine major influencing factors for the
class of components being considered. For example, for most all electronic components, the predicted failure rate is found to be a function of operating temperature and applied electrical stress. In general, the lower the operating temperature and applied electrical stress, the lower the predicted failure rate will be. Therefore, the parts stress method includes model factors for these specific stresses. However, if specific stress values cannot be determined, it is still possible to perform a prediction using the more general parts count methodology. For the parts count method, model stress levels have been set to typical default levels to allow a failure rate estimate simply by knowing the generic type of component (such as chip resistor) and its intended use environment (such as ground mobile). It should be noted that these reliability prediction handbook approaches are, by necessity, generic in nature. Actual test or field data from other similar items is always more desirable, given sufficient similarity, as was discussed previously.