Life Data Analysis using ReliaSoft Weibull++

Life Data Analysis (also called “Weibull analysis”) is a statistical technique that is used to understand the lifetime of products. Product life can be measured in hours, miles, cycles or any other metric that applies to the period of successful operation. This technique is used across engineering sectors to quantify lifetimes for all manner of things, as well as to understand how aging will influence performance. The models created can then be used to estimate important life characteristics of the product

Description

This course covers the introductory theory of Life Data Analysis and gives practice with ReliaSoft Weibull++ software, with the following aims:

  • To provide understanding of the data requirements to perform analysis
  • To explain the process of performing a Life Data Analysis
  • To enable delegates to perform their own simple Life Data Analysis using Weibull++
  • To discuss the meaning of the results obtained from this type of analysis

Who Should Attend?

This course is intended as an in introduction to Life Data Analysis for people that are interesting in better understanding the life characteristics of their products, with a focus on performing the analysis with ReliaSoft Weibull++.

Prerequisites

This course does not require prior experience with either the analysis method or the tool, but delegates should be familiar with basic statistical concepts such as averages, percentages and charts.

For people that are experienced with Life Data Analysis, separate training on advanced topics is also available.

Agenda

Course Topics Include:

  • Overview of Life Data Analysis
  • Overview of ReliaSoft Weibull++
  • Quantifying Reliability (Counts, MTBF and Failure Rate)
  • Life Data Analysis Method
    • Exercise – Weibull Hand Plot
  • Data Collection and Management
    • Exercise – Complete Data
    • Exercise – Censored Data
    • Exercise – Bearing Analysis
  • Confidence Intervals
  • Interpreting Results and Comparing Datasets
    • Exercise – Comparing Datasets
  • Multiple Failure Modes
    • Exercise – Competing Failure Modes