Practical Applications of Reliability Engineering

Explore the basics of reliability analysis in section two and how product qualities change over time. Identify the definition, main methods, and boundaries of reliability analysis.

Examine how product quality, a characteristic and ability to satisfy customer requirements, changes over time as internal wear, use, and environmental conditions erode satisfaction, while balancing reliability and cost.

Define reliability analysis and show how to predict future performance from historical data using a statistical model, employing randomization, probability, and statistics to identify failures and guide maintenance.

Identify four boundaries of reliability analysis to reduce variability in data: normal operating conditions, measurable parameters, proper use, and applicable time limits, illustrated by the fridge example.

Explore how reliability and unreliability are measured and defined in mathematical terms, using randomization, probability, and statistics, and examine the failure data set with its common graphical representations.

Explore the reliability equation, defining n0 as the total sample, f as failed units, and S as serviceable units; learn how reliability equals one minus unreliability and its time-based nature.

Explain reliability properties with radios, detailing the average time of failure and the range between first and last failures. Show how randomized samples reduce skewness and improve prediction.

Analyze a failure dataset to derive reliability parameters from times-to-failure data, using an example of 100 light switches and an Excel-based analysis to sort, interpret, and plan spares.

Explore how column charts, histograms, and Pareto charts visualize failure data, reveal the most frequent failure around five months, and show distribution and cumulative impact.

Learn to interpret the probability density function and its cumulative distribution for failures, compute reliability and unreliability functions, and visualize results with Pareto charts and spreadsheets.

Analyze the failure rate, measured as failures per unit time, and the bathtub curve showing infant mortality, useful life, and wear out, noting real curves vary with reliability and quality.

Explore the four moments of the failure distribution, focusing on the first moment—mean time between failure—while illustrating how distribution shape influences MTBF usefulness for safety and warranty.

Explore the second moment as variance, and measure dispersion with standard deviation, comparing distributions of mean time between failures to assess certainty and risk.

Examine kurtosis, fourth moment, and how distribution shape reflects failure randomness. Positive kurtosis means heavier tails and flatter curves; negative kurtosis implies a peaked, predictable failure pattern for reliability engineers.

Review the four moments of distribution—mean (mtbf), variance, skewness, and kurtosis—to assess center, spread, asymmetry, and tail risk, guiding actions to improve product reliability.

Quantify reliability and unreliability (sum to one) from failure data, build a failure dataset, and use the bathtub curve to reveal distributions and their four moments: mean, variance, skewness, kurtosis.

Examine two reliability functions: the pdf, showing failure probability at each moment, and the cdf, showing cumulative failure over time.

examine discrete distributions like binomial and poison, and continuous distributions such as exponential, normal, log normal variable, and gamma.

Explore the binomial distribution as a discrete model with two mutually exclusive outcomes, and learn to compute its probability function, mean, and standard deviation using n, k, p, and q.

Explore the Poisson distribution as a discrete model of independent events in a time interval with a constant rate, illustrated by a spreadsheet example of 120 attempts and 70% success.

Explore continuous distributions where a monitoring parameter takes any value in a time interval, and identify the mean, variance, and other moments with examples like time to failure and temperature.

Explore the Weibull distribution, its shape, scale, and threshold parameters, and see how adjusting them models reliability for quality control and warranty analyses, with Excel-based demonstrations.

Analyze the exponential distribution as a Weibull special case with a single lambda, modeling constant failure rate and independent failures; relate mtbf to reliability and standard deviation equals the mean.

learn how to fit the exponential distribution for reliability analysis when the failure rate is constant, using two testing methods to estimate k, total operating time, mtbf, and lambda.

Learn the normal distribution, its mean and sigma, and convert any distribution to the standard normal form using z values to compute probabilities in Excel for reliability analysis.

Explore the lognormal distribution, a continuous, left-skewed model used to analyze downtime, cow milk production, and time to failure, with shape sigma and mu location parameters.

Examine the limitations of probability and statistical analysis that rely on historical data and may not guarantee future performance, and emphasize a data set and model verification when conditions change.

Explore probability density and cumulative density functions for reliability analysis, distinguish discrete from continuous distributions, and fit data to models to compute mean and standard deviation.

Explore factors shaping reliability requirements and how to set achievable targets within product requirements, covering design roles, material selection, human factors, and three common reliability analyses with practical guidance.

Set realistic reliability requirements by balancing design feasibility, material quality and cost, workforce skills, and supplier and facility constraints to meet life-cycle and quality goals.

Define usage and environmental conditions to form reliability requirements, analyzing operating, maintaining, manufacturing, storage, and transportation contexts across thermal, mechanical, chemical, and electrical factors.

Explore how human factors affect product reliability across design, manufacturing, use, and maintenance. Understand how human errors and complexity lower reliability, and how simplification or familiarity improves it.

Explore inductive reliability analysis from component to system using failure mode effects and criticality analysis and reliability block diagrams, and deductive fault tree analysis for root-cause investigation.

Identify failure modes and effects across components and systems using FMECA, assess criticality, and drive design changes, maintenance tasks, and risk mitigation to improve product reliability.

Assess how reliability factors influence product design, set reliability requirements considering resources and cost-benefit constraints, and apply inductive or deductive analysis with Mecca fault tree analysis and reliability block diagrams.