Reliability Growth Analysis

Learn applications for Crow (AMSAA), power law and other RGA models using ReliaSoft RGA

Course overview - 3 days

Reliability Growth Analysis provides an overview of reliability growth analysis models and their application for data captured from developmental (reliability growth) testing and also from fielded (repairable) systems. Reliability growth training enables professionals to plan, execute and evaluate the reliability of a system during its development stage. And fielded system analysis training enables asset management professionals to plan, execute and evaluate preventive and corrective maintenance activities.

Software used

You will learn how to apply reliability growth analysis using ReliaSoft RGA software in hands-on exercises.
Learning objectives

  • Analyze data from developmental tests to quantify reliability growth and determine the feasibility of achieving reliability goals with a given test/fix strategy
  • Analyze data from fielded systems to calculate optimum overhaul times and other results without the detailed data sets that would normally be required for repairable system analysis

Reliability Growth Models in Developmental Testing and Fielded Systems Analysis
SMRP Approved Provider

Topics included

  • Understanding reliability growth models and their application
  • Reliability growth management and planning
  • Estimating system growth potential
  • How to manage multiple failure modes in a growth program
  • Repairable system analysis

  • Methods for warranty analysis of repairable systems
  • Analyzing the overall reliability trend for a fleet or system
  • Optimum replacement and overhaul analysis
  • Evaluation of proposed upgrades, improvements or corrective actions for fielded systems

Who should attend

The Reliability Growth Analysis course is for product development and maintenance professionals that are analyzing complex system reliability data.

Required prerequisite / foundational course(s)

Fundamentals of Reliability or RAM for Asset Management