Online courses directory (3)
This course covers interpretations of the concept of probability. Topics include basic probability rules; random variables and distribution functions; functions of random variables; and applications to quality control and the reliability assessment of mechanical/electrical components, as well as simple structures and redundant systems. The course also considers elements of statistics; Bayesian methods in engineering; methods for reliability and risk assessment of complex systems (event-tree and fault-tree analysis, common-cause failures, human reliability models); uncertainty propagation in complex systems (Monte Carlo methods, Latin Hypercube Sampling); and an introduction to Markov models. Examples and applications are drawn from nuclear and other industries, waste repositories, and mechanical systems.
This course was created for the "product development" track of MIT's System Design and Management Program (SDM) in conjunction with the Center for Innovation in Product Development. After taking this course, a student should be able to:
- Formulate measures of performance of a system or quality characteristics. These quality characteristics are to be made robust to noise affecting the system.
- Sythesize and select design concepts for robustness.
- Identify noise factors whose variation may affect the quality characteristics.
- Estimate the robustness of any given design (experimentally and analytically).
- Formulate and implement methods to reduce the effects of noise (parameter design, active control, adjustment).
- Select rational tolerances for a design.
- Explain the role of robust design techniques within the wider context of the product development process.
- Lead product development activities that include robust design techniques.