 # Reliability Prediction

DOI: 10.4018/978-1-4666-9429-3.ch003

## Abstract

It is important to have an expectation about useful life of a system before its construction or even its remaining useful life during its operation. Reliability prediction is a tool for this goal. Reliability is the probability of performing adequately to achieve the desired aim of the system. In this chapter, probability calculation is used to predict failure rate of the converter. The formulation of these calculations are based on the concepts of failure factors which were described in the previous chapter. Some detailed examples are presented to show the power of probability tool for analyzing the behavior of complex systems. This chapter covers the methods for reliability calculation from component to system level. Some standards of reliability are presented. One can use the information from a reliability prediction to guide design decisions throughout the development cycle. MIL-HDBK-217 is described in details as a well-known standard for reliability prediction at component level. Reliability modeling is introduced for calculating the reliability at system level. Difference between system block diagram and reliability model is presented. The reliability models of various static and rotary power converters are expressed. Some examples are presented to demonstrate the procedure of calculations for a simple converter with its auxiliary components. This chapter gives a quantitative view to reader about evaluation of reliability and it can be used in the next chapters for reliability improvement.
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## Introduction: Reliability Prediction

Now, we know the reasons for failure in power converters. In this chapter, we try to predict the chance of failure. The state of this chapter in the flowchart of the book is shown in Figure 1. Reliability predictions provide a quantitative basis for evaluating the power converters reliability.

The information obtained from a reliability prediction is used to guide design decisions throughout the development cycle. When an initial design concept is proposed, a reliability prediction can indicate the design feasibility from a reliability standpoint. For example, the designer might have a requirement of a 200,000 hr MTBF for a power converter. If the predicted value is 35,000 hr, the current design concept may not be feasible. With modifying the design concept or revising the requirement, a predicted value of 500,000 hr can give confidence in design concept. (Figure 2).

### Reliability Prediction Methodology

There are reliability prediction techniques depending on the knowledge about design. As more details of the design are known, more accurate methods become available. These methods use part failure rate models, which predict the failure rates of parts based on various part parameters, such as technology, complexity, package type, quality level, and stress levels 3.

Predictive methods attempt to predict the reliability of a part based on some model typically developed through empirical studies and/or testing. An attempt is made to identify critical variables such as materials, application environmental and mechanical stresses, application performance requirements, duty cycle and manufacturing techniques. Typically, a base failure rate for the component is assigned, and this is multiplied by factors for each critical variable identified. Some predictive models assume a constant failure rate over the lifetime of a product. This ignores higher failure rates typically seen at the beginning and end of component life, infant mortality, and wear-out, respectively. Predictive methods can provide a relatively accurate reliability estimate in cases where good studies have been done to analyze field failures.

### Reliability Comparison

Predictive methods are also useful in providing a relative ranking of reliability between alternative designs, but the absolute reliability numbers (or failure rates) obtained with these methods will rarely be indicative of real-life performance. This is the best way to demonstrate the real reliability level of a product. It requires a statistically significant population in the field, and a reasonably long time in the field (Javadian, & Kaboli, 2013).

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