Reliability and Maintainability Materials Science and Engineering Graduate Certificate
Reliability and maintainability (R&M) are crucial aspects of engineering because they directly impact the performance, safety, cost-effectiveness, and lifespan of products and systems, making them an essential foundation in the materials science and engineering field. Obtaining a grad certificate in this area sets students and professionals apart with specialized skills highly valued in the materials science and engineering industry.
Program overview
The reliability and maintainability materials science and engineering graduate certificate program is an interdepartmental initiative designed for students who wish to pursue careers in reliability and maintainability engineering within the materials science and engineering industry. It is also suitable for professionals and managers currently working in the field looking to improve their knowledge and skills.
The program consists of four graduate engineering courses, two required courses and two elective courses. The two required courses introduce the student to the fundamentals of maintenance engineering and reliability engineering. The two elective courses are selected from a list of RME-related courses that currently includes courses in five traditional engineering disciplines.
Why get a Certificate in Reliability and Maintainability Materials Science and Engineering?
With industry standards and compliance constantly evolving at a fast pace, it’s important for materials science engineers to continue learning R&M best practices to gain a professional edge and support movement into leadership and strategic roles.
What can you do with a Certificate in Reliability and Maintainability Materials Science and Engineering?
Certificate holders are better equipped for roles in reliability engineering, maintenance management, quality control, and asset management within the materials science and engineering field. Completing this program equips individuals with a unique set of skills that opens the door to pursuing careers as reliability engineers or data analysts, maintenance engineers, quality assurance specialists, predictive maintenance technicians, R&M consultants, and many more.
Featured Courses
Below are some of the courses that students in our program can choose to take. For a list of all courses, visit the graduate catalog.
IE 483 Introduction to Reliability Engineering
Probabilistic failure models and parameter estimation (maximum likelihood, Bayes techniques). Model identification and comparison, accelerated life tests, failure prediction, system reliability, preventive maintenance, and warranties.
IE 484 Introduction to Maintainability Engineering
Principles of maintenance and reliability engineering and maintenance management. Topics include information extraction from machinery measurements, rotating machinery diagnostics, nondestructive testing, life prediction, failure models, lubrication oil analysis, establishing a predictive maintenance program, and computerized maintenance management systems.
IE 517 Reliability of Lean Systems
Course is divided into two major components. First half of the course will focus on introducing the students to the concepts of reliability and maintainability and the impact of lean on the reliability of complex systems. The concepts of reliability engineering are utilized to address lean system failures, including equipment failures, human failures, material failures and scheduling failures. Will develop the ability to design systems that are both lean and reliable. The second half of the course will introduce students to specific case studies of systems failures and ask student to develop solutions by considering different dimensions including financial, technical feasibility, risk, safety, security and others. Multi criteria decision making methodologies will be presented to allow students to make decisions when different criteria lead to conflicting solutions.
STAT 567 Lifetime Data and Survival Analysis
Statistical analysis of time-to-event data with censored observations. Nonparametric methods including Kaplan Meier curves and the log-rank test, parametric regression based on accelerated failure models, and semiparametric analysis with the Cox proportional hazards model are explained and applied to practical data sets. Case studies from both engineering and business analytics are used. Use of statistical software required.
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