Certified Reliability Engineer (CRE)

• Basic concepts
• Statistical terms 
• Define and use terms such as population, parameter, statistic, sample, the central limit theorem, etc., and compute their values. (Apply)
• Basic probability concepts 
• Use basic probability concepts (e.g., independence, mutually exclusive, conditional probability) and compute expected values. (Apply)
• Discrete and continuous probability distributions
• Compare and contrast various distributions (binomial, Poisson, exponential, Weibull, normal, log-normal, etc.) and their functions (e.g., cumulative distribution functions (CDFs), probability density functions (PDFs), hazard functions), and relate them to the bathtub curve. (Analyze)
• Poisson process models 
• Define and describe homogeneous and non-homogeneous Poisson process models (HPP and NHPP). (Understand)
• Non-parametric statistical methods 
• Apply non-parametric statistical methods, including median, Kaplan-Meier, Mann-Whitney, etc., in various situations. (Apply)
• Sample size determination
• Use various theories, tables, and formulas to determine appropriate sample sizes for statistical and reliability testing. (Apply)
• Statistical process control (SPC) and process capability
• Define and describe SPC and process capability studies (Cp, Cpk, etc.), their control charts, and how they are all related to reliability. (Understand)
• Statistical inference
• Point estimates of parameters
• Obtain point estimates of model parameters using probability plots, maximum likelihood methods, etc. Analyze the efficiency and bias of the estimators. (Evaluate)
• Statistical interval estimates 
• Compute confidence intervals, tolerance intervals, etc., and draw conclusions from the results. (Evaluate)
• Hypothesis testing (parametric and non-parametric)
• Apply hypothesis testing for parameters such as means, variance, proportions, and distribution parameters. Interpret significance levels and Type I and Type II errors for accepting/rejecting the null hypothesis. (Evaluate)

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• Reliability design techniques
• Environmental and use factors 
• Identify environmental and use factors (e.g., temperature, humidity, vibration) and stresses (e.g., severity of service, electrostatic discharge (ESD), throughput) to which a product may be subjected. (Apply)
• Stress-strength analysis 
• Apply stress-strength analysis method of computing probability of failure, and interpret the results. (Evaluate)
• FMEA and FMECA 
• Define and distinguish between failure mode and effects analysis and failure mode, effects, and criticality analysis and apply these techniques in products, processes, and designs. (Analyze)
• Common mode failure analysis 
• Describe this type of failure (also known as common cause mode failure) and how it affects design for reliability. (Understand)
• Fault tree analysis (FTA) and success tree analysis (STA) 
• Apply these techniques to develop models that can be used to evaluate undesirable (FTA) and desirable (STA) events. (Analyze)
• Tolerance and worst-case analyses 
• Describe how tolerance and worst-case analyses (e.g., root of sum of squares, extreme value) can be used to characterize variation that affects reliability. (Understand)
• Design of experiments
• Plan and conduct standard design of experiments (DOE) (e.g., full-factorial, fractional factorial, Latin square design). Implement robust-design approaches (e.g., Taguchi design, parametric design, DOE incorporating noise factors) to improve or optimize design. (Analyze)
• Fault tolerance 
• Define and describe fault tolerance and the reliability methods used to maintain system functionality. (Understand)
• Reliability optimization 
• Use various approaches, including redundancy, derating, trade studies, etc., to optimize reliability within the constraints of cost, schedule, weight, design requirements, etc. (Apply)
• Human factors 
• Describe the relationship between human factors and reliability engineering. (Understand)
• Design for X (DFX) 
• Apply DFX techniques such as design for assembly, testability, maintainability environment (recycling and disposal), etc., to enhance a product’s producibility and serviceability. (Apply)
• Reliability apportionment (allocation) techniques 
• Use these techniques to specify subsystem and component reliability requirements. (Analyze)
• Parts and systems management
• Selection, standardization, and reuse 
• Apply techniques for materials selection, parts standardization and reduction, parallel modeling, software reuse, including commercial off-the-shelf (COTS) software, etc. (Apply)
• Derating methods and principles
• Use methods such as S-N diagram, stress-life relationship, etc., to determine the relationship between applied stress and rated value, and to improve design. (Analyze)
• Parts obsolescence management 
• Explain the implications of parts obsolescence and requirements for parts or system requalification. Develop risk mitigation plans such as lifetime buy, backwards compatibility, etc. (Apply)
• Establishing specifications
• Develop metrics for reliability, maintainability, and serviceability (e.g., MTBF, MTBR, MTBUMA, service interval) for product specifications. (Create)

• Reliability modeling
• Sources and uses of reliability data
• Describe sources of reliability data (prototype, development, test, field, warranty, published, etc.), their advantages and limitations, and how the data can be used to measure and enhance product reliability. (Apply)
• Reliability block diagrams and models 
• Generate and analyze various types of block diagrams and models, including series, parallel, partial redundancy, time-dependent, etc. (Create)
• Physics of failure models 
• Identify various failure mechanisms (e.g., fracture, corrosion, memory corruption) and select appropriate theoretical models (e.g., Arrhenius, S-N curve) to assess their impact. (Apply)
• Simulation techniques
• Describe the advantages and limitations of the Monte Carlo and Markov models. (Apply)
• Dynamic reliability
• Describe dynamic reliability as it relates to failure criteria that change over time or under different conditions. (Understand)
• Reliability predictions
• Part count predictions and part stress analysis
• Use parts failure rate data to estimate system- and subsystem-level reliability. (Apply)
• Reliability prediction methods 
• Use various reliability prediction methods for both repairable and non-repairable components and systems, incorporating test and field reliability data when available (Apply)

Case Study/Exercise: Data analytics and advanced topics (Markov chains and Monte Carlo simulation)

• Reliability test planning
• Reliability test strategies
• Create and apply the appropriate test strategies (e.g., truncation, test–to-failure, degradation) for various product development phases. (Create)
• Test environment
• Evaluate the environment in terms of system location and operational conditions to determine the most appropriate reliability test. (Evaluate)
• Testing during development
• Describe the purpose, advantages, and limitations of each of the following types of tests, and use common models to develop test plans, evaluate risks, and interpret test results. (Evaluate)
• Accelerated life tests (e.g., single-stress, multiple-stress, sequential stress, step-stress)
• Discovery testing (e.g., HALT, margin tests, sample size of 1),
• Reliability growth testing (e.g., test, analyze, and fix (TAAF), Duane)
• Software testing (e.g., white-box, black-box, operational profile, and  fault-injection)
• Product testing
• Describe the purpose, advantages, and limitations of each of the following types of tests, and use common models to develop product test plans, evaluate risks, and interpret test results. (Evaluate)
• Qualification/demonstration testing (e.g., sequential tests, fixed-length tests)
• Product reliability acceptance testing (PRAT)
• Ongoing reliability testing (e.g., sequential probability ratio test [SPRT])
• Stress screening (e.g., ESS, HASS, burn-in tests)
• Attribute testing (e.g., binomial, hypergeometric)
• Degradation (wear–to-failure) testing

Case Study/Exercise: Working through a system of their choosing to study failure degradation mechanisms (physics of failure) as well as predict using statistical methods the optimal maintenance periodicity. 

• Management strategies
• Planning 
• Develop plans for maintainability and availability that support reliability goals and objectives. (Create)
• Maintenance strategies 
• Identify the advantages and limitations of various maintenance strategies (e.g., reliability-centered maintenance (RCM), predictive maintenance, repair or replace decision making), and determine which strategy to use in specific situations. (Apply).
• Availability tradeoffs 
• Describe various types of availability (e.g., inherent, operational), and the tradeoffs in reliability and maintainability that might be required to achieve availability goals. (Apply)
• Maintenance and testing analysis
• Preventive maintenance (PM) analysis 
• Define and use PM tasks, optimum PM intervals, and other elements of this analysis, and identify situations in which PM analysis is not appropriate. (Apply)
• Corrective maintenance analysis 
• Describe the elements of corrective maintenance analysis (e.g., fault-isolation time, repair/replace time, skill level, crew hours) and apply them in specific situations. (Apply)
• Non-destructive evaluation 
• Describe the types and uses of these tools (e.g., fatigue, delamination, vibration signature analysis) to look for potential defects. (Understand)
• Testability 
• Use various testability requirements and methods (e.g., built in tests (BITs), false-alarm rates, diagnostics, error codes, fault tolerance) to achieve reliability goals (Apply)
• Spare parts analysis 
• Describe the relationship between spare parts requirements and reliability, maintainability, and availability requirements. Forecast spare parts requirements using field data, production lead time data, inventory and other prediction tools, etc. (Analyze)

DATA COLLECTION AND USE 

• Data collection
• Types of data 
• Identify and distinguish between various types of data (e.g., attributes vs. variable, discrete vs. continuous, censored vs. complete, univariate vs. multivariate). Select appropriate data types to meet various analysis objectives. (Evaluate)
• Collection methods 
• Identify appropriate methods and evaluate the results from surveys, automated tests, automated monitoring and reporting tools, etc., that are used to meet various data analysis objectives. (Evaluate)
• Data management 
• Describe key characteristics of a database (e.g., accuracy, completeness, update frequency). Specify the requirements for reliability-driven measurement systems and database plans, including consideration of the data collectors and users, and their functional responsibilities. (Evaluate)
• Data use
• Data summary and reporting