Discrete event simulation (DES) is a powerful technique that can be used to to solve more complex system reliability modeling and supportability problems. This article discusses using the Discrete Event Simulation tool in the Reliability Analytics Toolkit for spare parts planning purposes.
A Sequential Reliability Testing Calculator was recently added to the Reliability Analytics Toolkit. Sequential testing often provides a more efficient method to verify equipment reliability achievement. Really “good” equipment will be accepted much quicker and really “bad” equipment will be rejected much sooner, often resulting in fewer test hours needed than using a military handbook 781 fixed length reliability test. The tool provides the ability to plan a sequential reliability demonstration test for verification of equipment mean time between failure (MTBF) if it can be assumed that the equipment follows an exponential failure distribution (i.e., constant failure rate). Continue reading
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
Discrete event simulation is a powerful technique that can be used to to solve more complex system reliability modeling problems. This article introduces the some of the capabilities of the Discrete Event Simulation tool in the Reliability Analytics Toolkit.
The Discrete Event Simulation tool can be used for:
1. Estimating system mean time between critical failure (MTBCF) for a system consisting of units with different failure and repair scenarios.
2. Estimating system operational availability (Ao).
3. Providing graphical visualizations of the overall failure and repair process for individual units, as well as a system of units operating together.
4. Estimating spare part requirements and the impact of different policies, such as local versus remote spare parts, on Ao and MTBCF.
5. Other custom user studies (by exporting the simulation results to Excel).
This is an example of a recently published in the Reliability Analytics Toolkit called Weibull Prediction of Future Failures. This tool is based on work described in references 1 and 2. For a population of N items placed on test, this tool calculates the expected number of failures for some future time interval based on the following two inputs:
1. the estimated Weibull shape parameter and
2. some number of failures (X>=1) during the initial time interval (t1).
In reliability, one is concerned with designing an item to last as long as possible without failure; in maintainability, the emphasis is on designing an item so that a failure can be corrected as quickly as possible. The combination of high reliability and high maintainability results in high system availability. Maintainability, then, is a measure of the ease and rapidity with which a system or equipment can be restored to operational status following a failure. It is a function of the equipment design and installation, personnel availability in the required skill levels, adequacy of maintenance procedures and test equipment, and the physical environment under which maintenance is performed. As with reliability, maintainability parameters are also probabilistic and are analyzed by the use of continuous and discrete random variables, probabilistic parameters, and statistical distributions. An example of a discrete maintainability parameter is the number of maintenance actions completed in some time t, whereas an example of a continuous maintainability parameter is the time to complete a maintenance action.