Certified Reliability Engineer

A Certified Reliability Engineer is a professional who understands the principles of performance evaluation and prediction to improve product/systems safety, reliability and maintainability. CREs use engineering, probability, statistics, and other tools to ensure that their products and systems are more efficient and reliable.

A CRE certified professional possesses following competence but not minimums to:

A broad overview of reliability fundamentals including predictive modeling, root cause analysis, mean time to failure (MTTF), corrective and preventive action (CAPA), and more.

Identification, analysis, and mitigation of risk using techniques such as fault tree analysis (FTA), failure mode and effect analysis (FMEA), hazard analysis, and need for design change. Analysis, management of data, and insights using probability statistics for reliability. A focus on life-cycle reliability through design techniques such as stress-strength analysis, design of experiments (DOE), design for reliability (DfR), and maintainability strategies. Application of reliability planning, testing (accelerated life, stress screening, etc.), and modeling (reliability block diagrams, failure models, etc.) to inform design choices.


Program Details

This Body of Knowledge (BOK) and applied technologies include, but are not limited to, design review and control; prediction, estimation, and apportionment methodology; failure mode effects and analysis; the planning, operation and analysis of reliability testing and field failures, including mathematical modeling; understanding human factors in reliability; and the ability to develop and administer reliability information systems for failure analysis, design and performance improvement and reliability program management over the entire product life cycle.

All tenured professionals who are willing to work in Leadership roles and looking for advancement in career, this program is a right choice. Core Competencies of a CRE. A broad overview of reliability fundamentals including predictive modeling, root cause analysis, mean time to failure (MTTF), corrective and preventive action (CAPA), and more. Identification, analysis, and mitigation of risk using techniques such as fault tree analysis (FTA), failure mode and effect analysis (FMEA), hazard analysis, and need for design change. Analysis, management of data, and insights using probability statistics for reliability.


Application of reliability planning, testing (accelerated life, stress screening, etc.), and modeling (reliability block diagrams, failure models, etc.) to inform design choices. A focus on life-cycle reliability through design techniques such as stress-strength analysis, design of experiments (DOE), design for reliability (DFR), and maintainability strategies.



  • 1. Reliability Management (18 Questions)
  • A. Strategic Management
  • i. Benefits of reliability engineering
  • ii. Interrelationship of safety, quality, and reliability
  • iii. Role of the reliability function in the organization
  • iv. Reliability in product and process development
  • v. Failure consequence and liability management
  • vi. Warranty management
  • vii. Customer needs assessment
  • viii. Supplier reliability
  • B. Reliability Program Management
  • i. Terminology
  • ii. Elements of a reliability program
  • iii. Types of risk
  • iv. Product lifecycle engineering
  • v. Design evaluation
  • vi. Systems engineering and integration
  • C. Ethics, Safety, and Liability
  • i. Ethical issues
  • ii. Roles and responsibilities
  • iii. System safety
  • 2. Probability and Statistics for Reliability (27 Questions)
  • A. Basic Concepts
  • i. Statistical terms
  • ii. Basic probability concepts
  • iii. Discrete and continuous probability distributions
  • iv. Poisson process models
  • v. Non-parametric statistical methods
  • vi. Sample size determination
  • vii. Statistical process control (SPC) and process capability
  • B. Statistical Inference
  • i. Point estimates of parameters
  • ii. Statistical interval estimates
  • iii. Hypothesis testing (parametric and non-parametric)
  • 3. Reliability in Design and Development (26 Questions)
  • A. Reliability Design Techniques
  • i. Environmental and use factors
  • ii. Stress-strength analysis
  • iii. FMEA and FMECA
  • iv. Common mode failure analysis
  • v. Fault tree analysis (FTA) and success tree analysis (STA)
  • vi. Tolerance and worst-case analyses
  • vii. Tolerance and worst-case analyses
  • viii. Fault tolerance
  • ix. Reliability optimization
  • x. Human factors
  • xi. Design for X (DFX)
  • xii. Reliability apportionment (allocation) techniques
  • B. Parts and Systems Management
  • i. Selection, standardization, and reuse
  • ii. Derating methods and principles
  • iii. Parts obsolescence management
  • iv. Establishing specifications
  • 4. Reliability Modeling and Predictions (22 Questions)
  • A. Reliability Modeling
  • i. Sources and uses of reliability data
  • ii. Reliability block diagrams and models
  • iii. Physics of failure models
  • iv. Simulation techniques
  • v. Dynamic reliability
  • B. Reliability Predictions
  • i. Part count predictions and part stress analysis
  • ii. Reliability prediction methods
  • 5. Reliability Testing (24 Questions)
  • A. Reliability Test Planning
  • i. Reliability test strategies
  • ii. Test environment
  • B. Testing During Development
  • i. Accelerated life tests
  • ii. Discovery testing
  • iii. Reliability growth testing
  • iv. Software testing
  • C. Product Testing
  • i. Qualification/demonstration testing
  • ii. Product reliability acceptance testing
  • iii. Ongoing reliability testing
  • iv. Stress screening
  • v. Attribute testing
  • vi. Degradation (wear–to-failure) testing
  • 6. Maintainability and Availability (15 Questions)
  • A. Management Strategies
  • i. Planning
  • ii. Maintenance strategies
  • iii. Availability tradeoffs
  • B. Maintenance and Testing Analysis
  • i. Preventive maintenance (PM) analysis
  • ii. Corrective maintenance analysis
  • iii. Non-destructive evaluation
  • iv. Testability
  • v. Spare parts analysis
  • 7. Data Collection and Use (18 Questions)
  • A. Data Collection
  • i. Types of data
  • ii. Collection methods
  • iii. Data management
  • B. Data Use
  • i. Data summary and reporting
  • ii. Preventive and corrective action
  • iii. Measures of effectiveness
  • C. Failure Analysis and Correction
  • i. Failure analysis methods
  • ii. Failure reporting, analysis, and corrective action system (FRACAS)
  • Levels of Cognition
  • Based on Bloom’s Taxonomy—Revised (2001)
  • In addition to content specifics, the subtext for each topic in this BOK also indicates the intended complexity level of the test questions for that topic. These levels are based on “Levels of Cognition” (from Bloom’s Taxonomy—Revised, 2001) and are presented below in rank order, from least complex to most complex.
  • Levels of Cognition
  • Based on Bloom’s Taxonomy—Revised (2001)
  • In addition to content specifics, the subtext for each topic in this BOK also indicates the intended complexity level of the test questions for that topic. These levels are based on “Levels of Cognition” (from Bloom’s Taxonomy—Revised, 2001) and are presented below in rank order, from least complex to most complex.
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