Reliability Engineering Course Website

Reliability Engineering Course List, 

Active courses:

1.  ENRE447 -- Fundamentals of Reliability Engineering (3)
Prerequisites: MATH 246. This course provides a general survey of the techniques of reliability engineering with a focus on quantitative methods. Topics covered include: failure modes and effects analysis, mathematical definition of reliability, probabilistic models to represent failure phenomena, statistical life models for non-repairable components, reliability data analysis, and system reliability models including fault trees, event trees. Students will learn how to apply these techniques to problems related to engineering systems, with example cases for process plants, energy systems and infrastructure.

Offered: Spring 2025, Spring 2026 

2.    ENRE 489K – Design for Reliability (3)
Design for Reliability (DFR) has become a worldwide goal regardless of the industry. The engineering managers are intent on harvesting the value proposition for competing globally while significantly lowering the life cycle costs. The DFR principles are based on proactively preventing hardware failures, software failures, and product malfunctions. Most experienced engineers are expert in a segment of the field, and need to understand the entire field. This is obvious from the NASA Challenger accident, and millions of automobiles recalled by Toyota, GM, and Volkswagen. This science of engineering requires creativity and innovative skills to aim at zero failures and elegant solutions that cost less, which is the aim of this course.

Course Offered: Summer 2024, Summer 2025, Summer 2026 

Advanced failure mechanisms in reliability engineering will be taught from a basic materials and defects point of view. The methods of predicting the physics of failure of devices, materials, components and systems are reviewed. The main emphasis will be given to basic degradation mechanisms through understanding the physics, chemistry, and mechanics of such mechanisms. Mechanical failures are introduced through understanding fatigue, creep and yielding in materials, devices and components. The principles of cumulative damage and mechanical yielding theory are taught. The concepts of reliability growth, accelerated life testing, environmental testing are introduced. Physical, chemical and thermal related failures are introduced through a basic understanding of degradation mechanisms such as diffusion, electromigration, defects and defect migration. The failure mechanisms in basic material types will be taught. Failure mechanisms observed in real electronic devices and electronic packaging will also be presented. Problems related to manufacturing, and microelectronics will be analyzed. Mechanical failures are emphasized from the point of view of complex fatigue theory.

Course Offered: Fall 2025, Fall 2026 

Principal methods of reliability analysis, including fault tree and reliability block diagrams; Failure Mode and Effects Analysis (FMEA); event tree construction and evaluation; reliability data collection and analysis; methods of modeling systems for reliability analysis. Focus on problems related to process industries, fossil-fueled power plant availability, and other systems of concern to engineers.

Course Offered: Fall 2025, Fall 2026 

5.    ENRE 607 – Reliability Engineering Seminar (1-3)
This seminar addresses varied topics in identifying, analyzing, assessing, and managing reliability and risk of engineered system. This seminar series comprises guest lectures and internal faculty and student presentations on topics related to reliability engineering. Examples are drawn from foundational concepts in the discipline.

Prerequisites: MATH 246 or permission of department.  Basic probability and statistics
Application of selected mathematical techniques to the analysis and solution of reliability engineering problems.  Applications of matrices, vectors, tensors, differential equations, integral transforms, and probability methods to a wide range of reliability-related problems.

Course Offered: Spring 2025, Spring 2026 

Prerequisites: ENRE 620 and ENRE 602. Basic life model concepts.  Probabilistic life models, for components with both time independent and time dependent loads. Data analysis, parametric and nonparametric estimation of basic time-to-failure distributions. Data analysis for systems.  Accelerated life models.  Repairable systems modeling. Reliability data collection and analysis is of high (practical) importance in many essential engineering tasks including but not limited to: design alternatives evaluation, failure root cause analysis, early detection of field reliability problems, warranty reserve allocation, and others. The course teaches nonparametric and parametric statistical procedures of reliability data analysis for both non-repairable and repairable systems. It covers test data analysis (including accelerated and degradation testing), field data analysis (including warranty data and connected fleets data). Machine learning methods in reliability data analysis are discussed as well, along with special topics on condition-based maintenance and prognostics.

Course Offered: Spring 2025, Spring 2026 

8.    ENRE 641 – Probabilistic Physics of Failure and Accelerated Testing (3)
Prerequisites: ENRE 600 and ENRE 602. Models for life testing at constant stress.  Graphical and analytical methods.  Test plans for accelerated testing.  Competing failure modes and size effects.  Models and data analyses for step and time varying stresses.  Optimizing of test plans.

Course Offered: Fall 2025, Fall 2026 

9.    ENRE 642 -- Reliability Engineering Management (3)
Unifying systems perspective of reliability engineering management. Design, development and management of organizations and reliability programs including: management of systems evaluation and test protocols, development of risk management-mitigation processes, and management of functional tasks performed by reliability engineers.

Course Offered: Summer 2025, Summer 2026

Prerequisites: ENRE 600 and ENRE 602; or permission of department. Methods of solving practical human reliability problems, cognitive and behavioral modeling, task analysis, performance shaping factors, error classification, distribution of human performance and uncertainty bounds, sources of human error probability data, human error risk mitigation, examples and case studies.

Course Offered: Fall 2025, Fall 2026

Repeatable to 6 credits if content differs. For students who have definite plans for individual study of faculty-approved problems.  Credit given according to extent of work.

12.    ENRE648E –  Operations Research Models in Engineering (3)
A survey of the fundamentals of operations research models and methods in engineering including: optimization using linear programming, nonlinear programming, integer programming, as well as equilibrium/game theory via mixed complementarity problems. Examples of specialized course items include: specifics of optimizing power and gas networks, discussion of other network optimization problems, resource-constrained problems, two-level optimization as an example of mixed integer nonlinear programming (MINLP) programming problems as well as algorithms to solve the above types of problems.

13.    ENRE648J –  Applications of AI in Reliability: Prognostics and Health Management (3)
Prognostics and health management (PHM) is an enabling discipline consisting of technologies and methods to assess the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risk. PHM permits the reliability of a system to be evaluated and predicted in its actual application conditions. In recent years, prognostics and health management (PHM) has emerged as a key enabling technology to provide an early warning of failure; to forecast maintenance as needed; to reduce maintenance cycles; to assess the potential for life extensions; and to improve future designs and qualification methods. In the future, PHM will enable systems to assess their own real-time performance (self-cognizant health management and diagnostics) under actual usage conditions and adaptively enhance life cycle sustainment with risk-mitigation actions that will virtually eliminate unplanned failures.

14.    ENRE648M  –  Risk Analysis in Engineering and Economics (3)
Covers quantitative risk analysis and management using probability theory and statistics starting with system definition, hazard and scenario identification, likelihood estimation and consequence assessment, and finishes with economic valuation and microeconomics for informing decision making. It covers the topics: uncertainty, risk, knowledge and ignorance related definitions; natural and anthropogenic hazards and fundamental risk methods; system abstraction and associated complexities; analytical and empirical reliability and resilience estimation for components and systems; consequence, severity and loss analysis and accumulation including property and life; economic valuation; risk-cost-benefit tradeoffs and analysis; microeconomics and socioeconomics in risk analysis for informing decisions; risk management, acceptance, tolerance and finance; data needs and sources; expert-opinion elicitation; applications in engineering, sciences and economics.

Prerequisites: None.  In this course, students will learn representative machine learning algorithms with applications to reliability engineering. This course will cover model-based methods for reliability analysis, reliability model parameter estimation with both maximum likelihood approaches and Bayesian approaches, model selection, and model-based methods for health monitoring and reliability prediction. This course will also cover data-driven methods for reliability analysis, including neural networks, deep neural networks, random forest, and support vector machines. Lastly, this course will cover topics on decision optimization based on reliability analysis, focusing on the Markov decision process and reinforcement learning.

Course offered: Spring 2025, Spring 2026

Prerequisite: ENRE 602. Why study risk, sources of risk, overview of Risk Assessment and Risk Management, relation to System Safety and Reliability Engineering; measures, representation, communication, and perception of risk; overview of use of risk assessment results in decision making; overview of Probabilistic Risk Assessment (PRA) process; detailed converge of PRA methods including (1) methods for risk scenario development such as identification of initiators, event sequence diagrams, event trees, causal modeling (fault trees, influence diagrams, and hybrid methods), and simulation approaches; (2) methods of risk scenario likelihood assessment, including quantitative and qualitative approaches, as well as uncertainty modeling and analysis. Also covers methods for risk modeling of system hardware behavior, physical phenomena, human behavior, software behavior, organizational environment, and external physical environment. Additional core topics include risk model integration and quantification (Boolean-based, binary decsion diagram, Bayesian belief networks, and hybrid methods), simulation-based Dynamic PRA methods (discrete and continuous) and several examples of large scale PRAs for space missions, nuclear power, aviation and medical systems.

Course Offered: Spring 2025, Spring 2026 

Prerequisite: ENRE 670. In the course of engineering design, project management, and other functions, engineers have to make decisions, almost always under time and budget constraints. Managing risk requires making decisions in the presence of uncertainty. This course will cover material on individual decision making, group decision making, and organizations of decision-makers. The course will present techniques for making better decisions, for understanding how decisions are related to each other, and for managing risk.

18.    ENRE 682   Software Reliability and Integrity (3)
Cukier suggesting delete; keep on the books until we identify new faculty member.

19.    ENRE 684 Information Security (3)
The course introduce students to the core concepts of cybersecurity. This course is divided into three major components: overview, detailed concepts and implementation techniques. The topics to be covered are: general security concerns and concepts from both a technical and management point of view, principles of security, architectures, access control and multi-level security, trojan horses, covert channels, trap doors, hardware security mechanism, security models, security kernels, formal specifications and verification, networks and distribution systems and risk analysis.

Course Offered: Spring 2025, Spring 2026

20.    ENRE695 Design for Reliability (3)
Reliability is the ability of a product or system to perform as intended (i.e., without failure and within specified performance limits) for a specified time, in its life-cycle conditions. Knowledge of reliability concepts and principles, as well as risk assessment, mitigation and management strategies prepares engineers to contribute effectively to product development and life cycle management. This course teaches the fundamental knowledge and skills in reliability as it pertains to the design, manufacture, and use of electrical, mechanical, and electro-mechanical products. Topics cover the suitability of the supply chain members to contribute towards development, manufacturing, distribution and support of reliable products; efficient and cost-effective design and manufacture of reliable products; process capability and process control; derating, uprating, FMMEA, reliability prediction and reliability allocation; how to plan and implement product testing to assess reliability; how to analyze degradation, failure, and return data to estimate fundamental reliability parameters; root cause analysis; and reliability issues associated with warranties, regulatory requirements, and liabilities.

Course Offered: Spring 2025, Spring 2026