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.

# Category Archives: System modeling

# State Enumeration Tool MIL-STD-756 Example

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, Example 3, Comparison to Redundancy Equation Approach

This article compares the results obtained using the Discrete Event Simulation (DES) tool to those obtained deterministically by integrating the reliability function using this tool. Continue reading

# Discrete Event Simulation Tool, Example 2, Comparison to MIL-HDBK-338

In this example, we use the Discrete Event Simulation tool in the Reliability Analytics Toolkit to simulate system availability for a problem presented in MIL-HDBK-338, Reliability Design Handbook (page 10-42), as shown below. Continue reading

# Discrete Event Simulation Tool, Example 1, Single Unit Failure/Repair

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).

# Estimating MTBF Based on L10 Life

The Reliability Analytics Toolkit L10 to MTBF Conversion tool provides a quick and easy way to convert a quoted L_{10%} life to an average failure rate (or MTBF), provided that an educated guess can be made regarding a Weibull shape parameter (β). Continue reading

# Reliability Modeling: Combination of Series and Parallel

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