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).
A system consisting of n components or subsystems, of which only k need to be functioning for system success, is called a “k-out-of-n” configuration. For such a system, k is less than n. An example of such a system might be an air traffic control system with n displays of which k must operate to meet the system reliability requirement.
Most practical equipments and systems are combinations of series and parallel components as shown below
To solve this network, one merely uses series and parallel relationships to decompose and recombine the network step by step. Continue reading
A commonly occurring configuration encountered in reliability mathematical modeling is the parallel configuration as shown in the reliability block diagram below
For this case, for the system to fail, all of the components would have to fail. Continue reading
The reliability functions of some simple, well known structures will be derived. These functions are based upon the exponential distribution of time to failure.
The simplest and perhaps most commonly occurring configuration in reliability mathematical modeling is the series configuration. The successful operation of the system depends on the proper functioning of all the system components. A component failure represents total system failure. A series reliability configuration is represented by the block diagram as shown below with n components.
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.