Reliability Engineering Course

Lesson 1 • Defining Reliability and Its Metrics

Introduces reliability as a probability-based engineering discipline with measurable outcomes. Connects metric definitions to real-world performance expectations.

Lesson 2 • Probability Theory for Reliability

Covers probability rules, conditional probability, and independence as applied to failure events. Provides the mathematical foundation for all subsequent reliability calculations.

Lesson 3 • Failure Rate and the Bathtub Curve

Explains how failure rate changes over a product's life and the three-phase bathtub model. Links each phase to engineering actions such as burn-in and preventive maintenance.

Lesson 4 • Data Collection and Reliability Databases

Identifies sources of field and test data needed to estimate reliability parameters. Establishes data quality standards that underpin accurate analysis throughout the course.

Lesson 1 • Goodness-of-Fit and Distribution Selection

Applies statistical tests and graphical methods to validate distribution choices against data. Ensures analysts select the most defensible model before making reliability predictions.

Lesson 2 • Bayesian Reliability Estimation

Introduces Bayesian updating to incorporate prior knowledge with observed failure data. Provides a framework for small-sample estimation common in high-reliability systems.

Lesson 3 • Weibull Distribution

Introduces the two- and three-parameter Weibull model as the most versatile reliability distribution. Connects shape parameter values to the three bathtub curve phases.

Lesson 4 • Lognormal and Normal Distributions

Examines lognormal and normal models suited for fatigue, repair time, and wear-out failures. Contrasts their hazard functions with the Weibull to guide distribution selection.

Lesson 1 • Failure Mode and Effects Analysis Fundamentals

Defines FMEA scope, team roles, and the step-by-step worksheet process. Establishes FMEA as the primary proactive tool for capturing failure risk early in design.

Lesson 2 • FMEA Integration with Design Reviews

Embeds FMEA outputs into formal design review gates and change management workflows. Ensures failure mode knowledge is preserved and updated across the product lifecycle.

Lesson 3 • FMECA and Criticality Analysis

Extends FMEA with quantitative criticality numbers using failure rate data. Produces a criticality matrix that ranks failure modes by probability and severity.

Lesson 4 • Process FMEA Application

Adapts FMEA methodology to manufacturing and service processes rather than hardware design. Demonstrates how process controls replace detection mechanisms in the rating scales.

Lesson 1 • Fault Tree Analysis Fundamentals

Introduces deductive top-down logic modeling using AND/OR gates and basic events. Establishes FTA as the complement to FMEA for system-level failure causation.

Lesson 2 • Event Tree Analysis

Models accident sequences using inductive event trees that branch on system success or failure. Links initiating events to consequence categories for risk quantification.

Lesson 3 • Quantitative Fault Tree Evaluation

Calculates top-event probability using component failure rates and cut set probabilities. Connects quantitative results to reliability targets and design change decisions.

Lesson 4 • Minimal Cut Set Determination

Applies Boolean algebra and the MOCUS algorithm to extract minimal cut sets from fault trees. Minimal cut sets reveal the smallest combinations of failures that cause the top event.

Lesson 1 • Accelerated Life Testing

Applies elevated stress conditions to compress failure timelines and extrapolate to use conditions. Covers Arrhenius, inverse power law, and Eyring acceleration models.

Lesson 2 • Reliability Demonstration Testing

Designs pass/fail tests that demonstrate a reliability requirement with stated confidence. Applies the chi-squared and binomial methods to zero-failure and failure-allowed plans.

Lesson 3 • Highly Accelerated Life Testing

Introduces HALT as a discovery tool to expose design weaknesses beyond normal operating limits. Distinguishes HALT from quantitative ALT in purpose, method, and output.

Lesson 4 • Reliability Test Planning

Defines test objectives, sample sizes, and duration based on reliability and confidence targets. Balances cost and schedule constraints against statistical rigor in test design.

Lesson 1 • Spare Parts and Logistics Support

Models spare parts demand using Poisson and negative binomial distributions to set stock levels. Links sparing decisions to operational availability and life-cycle cost targets.

Lesson 2 • Reliability-Centered Maintenance

Applies the RCM decision logic to select the most appropriate maintenance task for each failure mode. Produces a maintenance program justified by failure consequences and detectability.

Lesson 3 • Availability Models

Derives inherent, achieved, and operational availability formulas for different system contexts. Demonstrates how each availability type reflects different maintenance and logistics assumptions.

Lesson 4 • Preventive Maintenance Optimization

Determines optimal preventive maintenance intervals using age-replacement and block-replacement models. Balances maintenance cost against failure cost to maximize cost-effective uptime.

Lesson 1 • Reliability Growth Planning

Constructs a reliability growth plan with milestones, resource allocation, and fix effectiveness assumptions. Aligns the growth plan with program schedule and reliability requirements.

Lesson 2 • Duane and AMSAA Growth Models

Fits the Duane postulate and AMSAA-Crow model to cumulative failure data to project future reliability. Compares model assumptions and selects the appropriate model for given data.

Lesson 3 • Reliability Requirements Allocation

Decomposes system-level reliability requirements to subsystem and component targets using allocation methods. Ensures each design team has a quantitative reliability goal to design toward.

Lesson 4 • Reliability Growth Concepts

Explains how iterative test-analyze-fix cycles drive measurable reliability improvement over time. Establishes the theoretical basis for growth models used in subsequent sections.

Lesson 1 • Quantitative Risk Assessment Methods

Applies probabilistic risk assessment techniques to estimate frequency and consequence of hazardous events. Combines FTA, ETA, and consequence models into an integrated risk picture.

Lesson 2 • System Safety Fundamentals

Defines hazard, risk, and safety as distinct but related concepts within a system safety program. Positions reliability analysis as a quantitative input to hazard risk assessment.

Lesson 3 • Reliability and Safety Case Development

Structures a safety case argument that uses reliability evidence to support safety claims. Produces a claims-arguments-evidence framework accepted by independent safety assessors.

Lesson 4 • Risk Reduction and ALARP Principle

Applies the as-low-as-reasonably-practicable principle to justify risk reduction measures. Demonstrates cost-benefit analysis for safety investments using risk reduction factors.