Error Prediction Models and Field Data

Carnegie Mellon University
Spring 1998

Michael Collins


Lifecycle prediction is an important part of product design, as it provides estimates for a product's safe lifetime. The need for accurate prediction schemes has resulted in various reliability prediction models which are used to qualitatively estimate the lifetimes of mechanical and electronic systems. These models are effectively 'best guesses', and to work with any degree of accuracy, they must use empirically acquired field data. We discuss several prediction models, their associated field data, and the differences between these data models.

Related Topics:



In order to predict the failure rate of a system, the Military and other organizations have developed error prediction models. These models are systematically derived equations which, by modeling properties of a system, generate predictions as to the life of the system. While they are not necessarily a perfectly accurate estimate of the lifespan of a system, these models provide an adequate initial guess and can be valuable when used by a sufficiently cautious engineer.

In order to determine meaningful failure rates, prediction models depend on observed lifetime data. There are several means of acquiring lifetime data for a system such as artificially stressing a system, initial laboratory tests and the like. Most data sources have some sort of bias, and the historic military preference has been for field data: information acquired by observing the lifetime of components in their normal use.[EPRD-97]

Prediction models depend on this data for several reasons. First, the prediction formulae are themselves derived from field data, without some idea of the natural lifespans of the components, talking about their estimated lifespans is meaningless. Second, engineers building new components plug existing field data into their derived prediction models to make an estimate. Field data is one of a group of datatypes that can be used, but it is usually the most thorough.

The standard for prediction models is the MIL-HDBK-217, an extensive handbook on various error prediction models for different systems. The MIL-217 specifications are extensive, but are also suited for a specific mode of behavior. The safety standards used by the military are not necessarily appropriate for other industries, where models of use and the consequences of failure are radically different. While the MIL-217 specification serves as the basis for almost every other prediction model, different industries have built different models to accommodate their specific needs.

Acquiring field data is a relatively onerous task: by definition, field data is gathered by observing parts fail in situ. A well designed part is less likely to have a long life time, leading to extended waiting time for any useful information. Because the task is so time consuming, there are relatively few sources, usually from the manufacturers themselves. The largest repositories of field data are the NPRD-95 and EPRD-97 produced by the military and these were the of years of observation, repair records, and other activities. Given the amount of time it takes to build such a record, these reliability tables are likely to remain the standard for years to come.

In the past twenty years, the military has become a less important client for engineering companies, and consumer products have come into a higher demand. A consequence of this change in demand has been a lessening of the MIL-217's role in reliability prediction. In general, the 217 model is considered to be considered somewhat 'pessimistic' - that the lifetime estimates are too conservative or too general. In response to these demands, industries are developing alternatives such as Bellcore TR-332 error prediction model [Relex].

Key Concepts

Acquiring and using field data is an empirical science. Most of the key concepts associated with analyzing this data are shared by any experimental discipline. A knowledge of statistical interpretation and an understanding of systematic error sources are a basic requirements.

Available tools, techniques, and metrics

Formalized error prediction began with the MIL-217 and the bulk of failure prediction tools are based around making the MIL-217 more tractable. Examples include:

As the MIL-217 becomes less important, many of these tools include the TR-332 as an alternative system. There are a number of products which handle both models, and include conversion facilities.

The other major tool are the data sources: the NPRD-95 and EPRD-97 error rate repositories. These books are published by the U.S. Reliability Analysis Center and are nothing more than giant compendia of failure rates. The EPRD and NPRD are extremely detailed work; as noted above, their failure data may be too detailed for a particular industry. Certain industries have developed smaller compendia for their own purposes.

Relationship to other topics


While basically a mathematically based guess, formal error prediction is a necessary part of safety-centered design. Error prediction models provide estimates on the lifetime of systems, and serve as a starting point for determining the realistic lifespan of a system.

Error prediction drives the need for field data on failure rates. Field data can be gathered from a variety of sources, each of whom may introduce systematic error into the prediction model. Some knowledge of experimental methods, such as statistical analysis and sources of systematic error, can help turn the raw data into meaningful values.

The basis for error prediction models is the MIL-HDBK-217, a military compendium developed in the late 1940's and periodically updated since. The MIL-217 is an extensive and generalized source and consequently may not be appropriate for all industries. As with many other disciplines the MIL-217 prediction model is suffering from the peace dividend. As the military becomes a less important customer, alternative models which place more of an emphasis on consumer needs become more attractive.

Annotated Reference List

Loose Ends

I wasn't really able to say that much about the Bellcore model for financial reasons: actually purchasing a copy of the Bellcore model is outside of my means right now.

One interesting point is that the Bellcore model's rise is a result of the peace dividend: as the military becomes a less important customer, the military reliability standards become less important. It would be interesting to provide exact specifications for how the Bellcore model differs as a consequence. I've touched on some of this by discussing the SAE's emphasis on generic parts - legal issues require them to use generic parts, rather than finger individual customers. Bellcore apparently extends these kind of concerns in other directions - using field testing data, for example.

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