Introduction to Reliability Engineering

Explore reliability engineering for design and manufacturing, covering stress-strength analysis, life testing, exponential distribution, and reliability block diagrams to evaluate quality over time.

Define reliability as quality plus time, the probability that an item will perform a required function without failure under stated conditions for a period of time.

Reliability engineers estimate design reliability, run tests, identify failure modes, and use field data to guide improvements and reliability growth via predictive maintenance and design changes.

Explore why things fail by comparing the load on a product with its inherent strength, using real-life examples and showing how load and strength vary over time.

Analyze strength and its variation as a product's ability to resist tensile loads, using chains, tires, and swing sets to show how size, materials, design, and manufacturing affect strength.

Investigate how variation in wire chemistry, diameter, tooling wear, welding energy, and weld material drives chain strength, and use sampling of chains to estimate population strength range for reliability engineering.

Demonstrates destructive testing with a hydraulic tensile tester to break a chain while recording load and elongation, then uses sampling to infer properties about the population.

Apply Excel to analyze reliability test data, compute max, min, range, and mean, create histograms, and assess dispersion and normal distribution to estimate population strength.

Link the normal distribution, histogram, probability distribution, and sigma to average and standard deviation, and connect failure data to population inferences through sampling and data analysis.

Explore the normal distribution and sampling, using a minimum of 30 observations to estimate mean (X bar) and standard deviation (S). Above 30, the normal and student’s t distributions align.

Explore the normal distribution curve, its mean and sigma. See how 68.3%, 95.4%, and 99.7% within 1–3 sigma define the six-sigma process range and relate to Pp, Ppk, Cpk.

Apply the normal distribution to load-to-failure data to estimate population strength using mean and standard deviation. Use ±3 sigma to bound 99.7% of values and norm inverse for percentile estimates.

Explore how load, driven by how products are used, varies widely and compares to strength; reliability engineers balance field research and design ratings to meet the bulk of known applications.

Apply field research to identify real-world loads on products, collect load data, and analyze with statistics and histograms to assess maximum loads and margins of safety and safety factors.

Plot strength and load histograms on the same scale to compare their distributions and identify overlap. Assess margin of safety and reserve strength to gauge reliability and probability of failure.

Learn how safety factor and margin of safety compare mean strength to mean load, using mu and reserve strength to guide design ratings and safe working loads.

Use the z-score to relate a value to the mean in standard deviations for reliability and quality calculations, using both the z-score table and normdist for normal distributions.

Analyze the overlap of load and strength distributions using z-scores to estimate probability of failure, beyond safety factor, with mu and sigma, and apply the method in Excel.

Explore load-strength analysis in excel by building a data model, calculating z scores, and evaluating probability of failure using normsdist.

Reliability engineers analyze how load and strength shift over time due to corrosion, thermal cycling, wear, and fatigue, estimate starting conditions, assess the environment, and guide strategies to slow degradation.

Learn how life testing estimates product life in reliability engineering by sampling bulbs, testing to 1200 hours, and using results to infer durability for 800-hour guarantees in high-volume manufacturing.

Analyze life data to estimate mean time to failure and failure rate from total test time and failure counts, accounting for censored data and MTBF vs MTTF.

Introduce the exponential distribution as a reliability model, define lambda as the constant hazard rate, and use R_t = e^{-lambda t}, illustrated with a light bulb example.

Compute reliability using the exponential distribution by applying the failure rate in Excel, evaluating survival probabilities at 1,500 and 2,000 hours, and deriving conditional survival.

Explore accelerated life testing (ALT) to shorten lifespan assessments by compressing timelines, raising stress, or intensifying environmental stresses, without changing failure modes. Examples include Ikea chair and a Motorola radio.

Explore accelerated life testing concepts using high, medium, and low stresses to estimate product life at design stress through mean life and regression, with models like Arrhenius and Eyring.

Explore the Weibull curve, aka the bathtub curve, showcasing infant mortality, useful life, and wear-out phases in reliability engineering, with its three-parameter equation and location, scale, and shape parameters.

Model system reliability with reliability block diagrams, showing components in series, parallel, and combinations, including actively redundant paths. Use Excel to calculate overall reliability from component reliabilities.

Explore reliability block diagrams by modeling series and parallel systems in Excel, compute overall reliability with series (product) and parallel (one minus product) formulas, and run what-if scenario experiments.

Explore HALT and HASS to improve product reliability by pushing designs beyond limits in accelerated testing, identifying failure modes, and screening manufactured parts for defects.

Explore redundancy as a reliability strategy by duplicating critical components with active, passive, and standby configurations to increase system reliability.

Derating improves reliability by operating components below maximum strength, increasing reserve strength and safety factors, while strategic life testing targets upgrades on the system's weakest links.

Explore preventive maintenance, including lubrication, oil changes, inspections, and overhauls, to minimize unplanned failures in large equipment. Learn how predictive maintenance uses condition monitoring to predict faults and extend reliability.

Explore OEE, a composite metric that combines availability, efficiency, and quality to measure factory equipment performance. Baseline by machine or department, track trends, identify outliers, and drive improvements.

Conclude by reinforcing reliability engineering skills and inviting learners to leave reviews, while highlighting lifetime access, future updates, and HALT versus HASS concepts.

Access a bonus overview of related manufacturing and reliability courses—from Lean Six Sigma and root cause analysis to reliability engineering and analytics—with a downloadable PDF catalog and sale price.