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ES506 RELIABILITY

Course Objectives

By the end of this course, students will be able to:
1. Apply fundamental concepts of probability theory to reliability and risk problems in engineering.
2. Analyze and derive the distribution of functions (sum, quotient) of random variables.
3. Evaluate and compare different risk-based design methods and identify their strengths and limitations.
4. Model and assess system reliability for series, parallel, and complex systems.
5. Apply statistical decision theory to engineering problems involving uncertainty.
6. Interpret and communicate risk and reliability metrics to support engineering decisions.
By the end of this course, students will be able to:
1. Apply fundamental concepts of probability theory to reliability and risk problems in engineering.
2. Analyze and derive the distribution of functions (sum, quotient) of random variables.
3. Evaluate and compare different risk-based design methods and identify their strengths and limitations.
4. Model and assess system reliability for series, parallel, and complex systems.
5. Apply statistical decision theory to engineering problems involving uncertainty.
6. Interpret and communicate risk and reliability metrics to support engineering decisions.

Course Content

Brief review of applied probability. Distributions of sum and quotient of two random variables. Topics in risk-based engineering design. Methods available, advantages and disadvantages. System reliability concepts. Statistical decision theory and its application in engineering.

Course Learning Outcomes

Apply core concepts of probability theory to model uncertainty in engineering systems.

Analyze the distribution of functions of random variables (e.g., sums, quotients) using transformation techniques and approximations.

Construct and evaluate reliability models for simple and complex systems using tools such as reliability block diagrams, fault trees, and event trees.

Identify minimal cut sets and minimal path sets to assess system-level reliability, including cases with common-cause failures.

Evaluate and compare different risk-based engineering design strategies, considering trade-offs between safety, cost, and performance.

Estimate component and system reliability using life data analysis and parametric, non-parametric, and Bayesian statistical methods.

Formulate and solve engineering decision-making problems under uncertainty using principles from statistical decision theory, including utility-based analysis and Bayes risk.

Communicate quantitative risk and reliability assessments clearly to support design, maintenance, and operational decisions.

Apply reliability and risk analysis tools to real-world case studies from mechanical, structural, and electronic systems, demonstrating familiarity with relevant engineering standards and codes.

Program Outcomes Matrix