Fundamentals of Reliability

Learn how to analyze and model reliability data using ReliaSoft Weibull++ and BlockSim

Course overview - 4 days

Reliability Engineering in the development process is the discipline of quantifying a product’s reliability requirements, while ensuring that practices are in place to design in reliability, and verifying that reliability requirements are met through design analysis and testing. This course will introduce and familiarize you with the basic concepts and skills of estimation, prevention and management of product lifecycle reliability engineering.

Software used

This course helps you understand how life data analysis methodologies can be applied using ReliaSoft Weibull++ and ReliaSoft BlockSim as well as other tools with practical hands-on exercises and case study examples.

Learning objectives

  • Defining meaningful reliability requirements, demonstrating if an item meets the specification and effectively communicating performance estimates to the engineering team and management
  • Analyzing product test and field performance to make predictions about the useful life and warranty periods
  • Evaluating suppliers and/or comparing designs based on reliability
  • Become familiar with the applications of other essential reliability data analysis methods and tools such as: Accelerated Life TestReliability Block Diagrams and Reliability Growth Analysis
Reliability Engineering Data Analysis and Modeling for Product Development
SMRP Approved Provider

Topics included

  • Reliability metrics
  • Weibull and life distribution analysis
  • Degradation analysis
  • Warranty analysis
  • Reliability statistical concepts
  • Reliability data types

Who should attend

The Fundamentals of Reliability course is for managers and engineers involved with the reliability aspects of product development that need to understand the concepts and develop skills in reliability engineering analysis for their product life data (product failures, usage, test results and more). Solid understanding of life data analysis is highly recommended.