Program Overview
This intensive program provides engineers and technical managers with a deep, application-focused understanding of reliability engineering. Blending core concepts such as failure rate behaviour, life data analysis, reliability distributions, block diagrams, and accelerated testing with real industrial case studies, the course equips participants to diagnose failures, analyse warranty and field data, and implement reliability improvement actions. Hands-on simulations reinforce how to select distributions, build reliability models, assess system-level performance, and plan effective reliability testing. This program is designed to help organisations reduce failures, optimise design, lower warranty costs, and enhance safety and customer trust—reflecting the principles described in the reference document
Features
- Analyse failure patterns and apply appropriate reliability distributions to real-world data
- Build system reliability models and interpret risk using reliability block diagrams
- Apply life data, warranty analysis, and accelerated testing concepts to predict failures
- Implement reliability improvement actions based on test results, modelling, and field insights
Target audiences
- Operations, maintenance & electrical/mechanical engineering professionals
- Quality, reliability, warranty & failure analysis engineers
- R&D, product development & design engineering teams
- Projects, plant engineering & utilities professionals
- Risk, safety & asset management engineers
Curriculum
- 6 Sections
- 49 Lessons
- 1 Day
- Foundations of Reliability Engineering10
- 1.1What is Reliability? (MTBF, MTTR, failure probability, bathtub curve)
- 1.2Failure rate behaviour & life cycle reliability
- 1.3Key reliability metrics: MTTF, reliability function, hazard rate
- 1.4Why reliability matters: warranties, safety, brand trust, competitive advantage
- 1.5Common failure modes in automotive, manufacturing, aerospace, electronics
- 1.6Cost of unreliability: recalls, downtime, customer dissatisfaction
- 1.7Reliability vs maintainability vs availability
- 1.8Industry examples of design failures due to reliability gaps
- 1.9Warranty failures & prediction errors from real OEM cases
- 1.10Exercise: Identify critical components in your product/process and estimate potential failure modes
- Reliability Statistics & Distributions8
- 2.1Failure distributions: Weibull, Exponential, Lognormal, Normal
- 2.2Statistical parameters: scale, shape, characteristic life
- 2.3Probability plots & interpretation
- 2.4Situational Awareness: Selecting the right distribution for real-world behaviour
- 2.5Common mistakes: poor data, incorrect model, sample size issues
- 2.6Example: Defect prediction from field return data
- 2.7Example: Reliability trends from warranty databases
- 2.8Exercise: Manually fit a Weibull distribution using provided sample data
- Life Data Analysis & Warranty Analytics9
- 3.1Life Data Analysis using Minitab-style workflow
- 3.2Right/left-censored data
- 3.3Reliability estimation from field returns
- 3.4Warranty curve fitting
- 3.5Situational Awareness: How companies misuse or misinterpret warranty data
- 3.6Detecting early-life failures vs wear-out failures
- 3.7Example: OEM case study: warranty spikes due to supplier variation
- 3.8Example: Cost-saving achieved by proactive reliability screening
- 3.9Exercise: Build a basic failure prediction curve using provided warranty dataset
- Reliability Block Diagrams (RBD) & System Reliability9
- 4.1Block diagram modelling (series, parallel, k-out-of-n, standby redundancy)
- 4.2Calculating overall system reliability
- 4.3How architecture impacts reliability
- 4.4Situational Awareness: Trade-offs in system design
- 4.5Cost vs reliability: optimal engineering decisions
- 4.6Design-for-reliability principles
- 4.7Example: Automotive subsystem reliability
- 4.8Example: Electronics system reliability improvement using redundancy
- 4.9Exercise: Create an RBD for your own subsystem & calculate system reliability
- Reliability Testing Methods & Sample Size Determination8
- 5.1ALT, HALT, HASS, ESS testing
- 5.2Stress screening & accelerated failure modelling
- 5.3Reliability test planning & acceptable quality levels
- 5.4Situational Awareness: How poor test planning leads to costly field failures
- 5.5Understanding test trade-offs: time, cost, confidence
- 5.6Example: Case: Accelerated life testing revealing design flaw
- 5.7Example: Case: Early-life failure detection via HALT
- 5.8Exercise: Determine required test sample size for 90% reliability at 95% confidence
- Applications, Case Studies & Final Simulation5
- 6.1Case Study: Warranty spike root-cause analysis
- 6.2Reliability improvement roadmap from a manufacturing plant
- 6.3Component failure due to incorrect distribution modelling
- 6.4Exercise: Use all concepts from the day to solve a real-world reliability challenge
- 6.5Interpret failure data, select distribution, estimate system reliability, recommend design/testing improvements, Present findings as an engineering recommendation report



