Part I: Reliability Factors

Note: This is the first part of a three-part article. It identifies the major factors in reliability prediction models that contribute to predicting component failure. Part II explores the various reliability prediction standards that are available, including MIL-HDBK-217, Telcordia, NSWC-98/LE1, PRISM, RDF 2000, CNET 93, HRD5, and GJB/z 299B. Part III provides guidelines for making a sound judgment when deciding which standard to apply to a particular analysis.

Introduction

Reliability prediction stands at the center of many reliability programs across government and industry. The basic principle of reliability prediction is to define a rate of failure for all key components in a system and then add them together to obtain an overall system failure rate. This process explicitly considers all components to be in series, which means that if one component fails, the entire system goes down. The result gives a conservative estimate of when a system will most likely fail.

Over the past few decades, several standards have been developed to assist in conducting this type of analysis. The standards define models for different component types based on test data. With few exceptions, the models assume a failure rate that is constant with time, addressing the useful life of a component where failures are regarded as random.

Major Factors Affecting Reliability Predictions

Part I of this three-part article identifies the major factors in reliability prediction models that contribute to predicting component failure. They consist of:

• the type of prediction method (parts count versus parts stress)
• the types of parts in the system
• the quality of the parts
• the environment in which the system operates
• the availability of life data

Parts Count Versus Parts Stress

The first decision you must make before choosing a reliability prediction model is whether to use a parts count prediction or a parts stress prediction. The parts count prediction is generally used for the early design stage of a project when parts and part parameters have not been exactly identified. It uses generic failure rates for various part types given an operating environment and temperature, multiplies them by a quality factor, and then adds them up to obtain a system failure rate. This methodology is specifically defined in MIL-HDBK-217, Telcordia, and GJB/z 299B.

Parts stress prediction is normally used later in the development stage when most of the components and operating conditions have been identified. In a parts stress prediction, temperature and electrical stress become important factors in predicting the part failure rate. Temperature can be set at the system level, the assembly level, and the component level. A junction temperature rise per component may also be considered, depending on the depth of the parts stress prediction. The electrical stress usually assumes the form of a ratio of operating value to rated value. For instance, the defining stress factor for capacitors is voltage. Consequently, operating voltage and rated voltage are used in the failure calculation model. These factors are generally consistent across the different reliability prediction standards.

Table 1. Comparison of Device Inputs for Models of Two Part Types

Model	MIL-HDBK-217	Telcordia
Microprocessor	* Technology Type * Number of Gates * Pins * Package Type * Years in Production * Operating Power * Thermal Resistance	* Technology Type * Number of Gates * Package Type
Si FET	* One or Two Sided * Application * Power Rating * Type * Thermal Resistance	* Application * Package Type * Power Rating

Part Types

Part type identification in any prediction model is the major factor affecting failure rates as well as the model inputs that must be considered. As an example, Table 1 compares the device parameters required for microprocessor and silicon field effects transistor parts stress models in MIL-HDBK-217 and Telcordia. Each of these parameters has a dramatic effect on the failure rate prediction of the device. It is also very important to consider the different part types supported by a particular standard when deciding which one to choose. For example, MIL-HDBK-217 includes a model for laser diodes and Telcordia includes a model for batteries, but the reverse is not true. For a comprehensive matrix of model coverage for the four most widely used standards, go to www.relex.com/resources/art/art_predmodels.asp.

Quality Level

Part quality level is a measure of a manufacturer's production and test procedures and the quality controls in place. Quality level scales vary significantly from standard to standard and from part type to part type for some standards. For example, Telcordia defines a single quality scale for all part types while MIL-HDBK-217 has different scales for different part types. When assessing which model to use, you may want to consider which quality rating you use in your company and see which model closely matches it.

Environment

The environment in which the system operates will have a considerable effect on reliability prediction results. The operating environment considers the type of surroundings in which the system operates, such as ground-fixed, ground-mobile, naval, air, space, etc. Environment choices vary from standard to standard, and the environments covered should be scrutinized before deciding which standard to use. For example, Telcordia does not have a model for naval environments, so it would not be the standard of choice if your system operates on a naval ship.

Availability of Life Data

Sometimes, you may have data obtained from actual fielded units or laboratory tested units. This information can be very useful in adjusting the predicted failure rates to more accurately reflect what has been experienced. Some models specifically support this feature, including Telcordia and PRISM. Though this may limit your selection if you are doing prediction calculations manually, software prediction packages such as Relex Reliability Prediction extend this capability to all prediction models.

Summary

There are several major factors in reliability prediction models that contribute to predicting component failure. These include the type of prediction (parts count versus parts stress), the types of parts in the system, the quality of parts, the environment in which the system operates, and the availability of life data. Part II of this three-part article explores, in more detail, the various standards that are available, including MIL-HDBK-217, Telcordia, NSWC-98/LE1, PRISM, RDF 2000, CNET 93, HRD5, and GJB/z 299. Additional information about Relex Reliability Prediction, supported models, and predictive modeling in general can be found on the Relex web site. Visit www.relex.com to learn more about the use of reliability prediction models.