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Advanced Reliability Engineering

Overview

This program aims to develop advanced level competencies in professionals to resolve reliability issues in their respective organizations. After attending the programme the participant will be able to establish and improve upon reliability measures like B10 life, MTTF and MTBF for critical components and systems, prioritize areas for improvement in reliability, predict warranty cost based on warranty data analysis and will be able to execute reliability growth for existing and upcoming products.

Objectives

To enhance Advanced Reliability Engineering knowledge and build skills in reliability tools
To provide exposure to good reliability engineering practices and systems
To enable better warranty data analysis and take permanent actions
To develop methodology for reliability growth of products (existing and new products)

Course Contents

Introduction to Reliability: Product Life cycle, Data collection and use (Complete and Censored data), Life data analysis (Simple and with failure modes, weibull distribution, paper clip Exercise and life testing in minitab), Warranty Data Analysis (Importance, exercise in minitab).
Reliability tools and their application: Accelerated Life testing (Concepts, use and exercise in minitab), Monte carlo Simulation (Concepts, use and exercise in minitab), DFMEA + FMECA (Concept, scope, use and exercise).
Reliability models for Current & New Products and Case Studies : Reliability Block Diagrams (Importance, Series/parallel systems and it's use), Reliability models (DUANE model, AMSAA, FRACAS, FORD Models - Concept, use and example), Stress Screening (ESS, HASS, Burn in Tests - Use and examples), Reliability problem solving and Root cause analysis, Case Studies from AFS.

  • Eligibility

    Engineers/Managers having 5 years to 15 years of experience in Product Development, Component development, Reliability Labs, QA, Maintenance etc.

  • Duration
    3 days
  • Venue
    Mahindra Institute of Quality (MIQ), Nashik
Methodology
  • Classroom learning
  • Self learning teams
  • Individual and team exercises
  • Case studies and experience sharing
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