Reliability prediction plays a major role in many reliability programs across government and industry. Standards based reliability prediction relies on defining failure rates for the components of a system based on predefined standards, depending on the types of components, the use environment, the way the components are connected and the reliability prediction standard. These component failure rates are then used to obtain an overall system failure rate.

Over the past few decades, several standards have been introduced by various governments and industry organizations to assist in conducting this type of analysis. The standards define models for different component types based on test data. The models assume a constant failure rate (i.e. no wearout or early failures problem), which generally describes the useful life of a component where failures are considered random events. This article presents an overview of the common standards that are available in Lambda Predict as different modules.

Utilizing Residual Plots in Accelerated Life Testing Data Analysis

When analyzing accelerated life testing data, it is important to assess model assumptions, discover inadequacies in the model, note extreme observations and assess the possibility that the test did not account for important factors. One way to perform such verifications is through the use of residual analysis for reliability, which consists of analyzing the results of a regression analysis by assigning residual values to each point in the data set and plotting these residuals. Three types of residual plots are available in ALTA 6: the Standardized Residuals plot, the Cox-Snell Residuals plot and the Standardized vs. Fitted Value plot. This article presents descriptions of the plot types and their benefits.