A new reliability engineering search tool was recently added to the Reliability Analytics Toolkit. This tool indexes, on a page level basis, approximately 30,000 pages from various reliability engineering “standards” (government standards, handbooks, guides and reports related to reliability, maintainability, availability, safety, etc.). The tool provides a more comprehensive search capability than the Google Custom Search box at the top of each page, which only outputs pages ranked high by Google, but not necessarily all pages that contain a particular set of words. Continue reading
The Reliability Analytics Toolkit System States tool provides the equivalent functionality as the Method 1002 procedure described in MIL-STD-756, Reliability Modeling and Prediction. While the approach described in MIL-STD-756 is very tedious, the System States tool makes the analysis process far easier. Continue reading
Figure 1 shows a typical time versus failure rate curve for equipment. This is the well known “bathtub curve,” which, over the years, has become widely accepted by the reliability community.
It has proven to be particularly appropriate for electronic equipment and systems. Note that it displays the three failure rate patterns, a decreasing failure rate (DFR), constant failure rate (CFR), and an increasing failure rate (IFR).
Failure modeling is a key to reliability engineering. Validated failure rate models are essential to the development of prediction techniques, allocation procedures, design and analysis methodologies, test and demonstration procedures, control procedures, etc. In other words, all of the elements needed as inputs for sound decisions to insure that an item can be designed and manufactured so that it will perform satisfactorily and economically over its useful life.
Inputs to failure rate models are operational field data, test data, engineering judgment, and physical failure information. These inputs are used by the reliability engineer to construct and validate statistical failure rate models (usually having one of the distributional forms described previously) and to estimate their parameters.
Most modern engineering disciplines are based on applied mathematics. An engineer or scientist observes a particular event and formulates a hypothesis (or conceptual model) which describes a relationship between the observed facts and the event being studied. In the physical sciences, conceptual models are, for the most part, mathematical in nature. Mathematical models represent an efficient, shorthand method of describing an event and the more significant factors which may cause, or affect, the occurrence of the event. Such models are useful to engineers since they provide the theoretical foundation for the development of an engineering discipline and a set of engineering design principles which can be applied to cause or prevent the occurrence of an event.