In today's competitive business environments, companies have urgent needs to use advanced analytical tools to manage their data to gain more insights and make timely decisions. Those insights include equipment and machine health condition, system remaining useful life, system performance, and other key performance indicators. With the advent of smart networks and devices, an abundance of data now exists in virtually every level of industry, from the individual asset to different field applications. This data is often not used to its greatest potential. Most organizations understand the need for acquiring useful data, but do not understand how to make the data useable. This course will introduce AI for industrial applications in a systematical way including machine learning, prognostics and health management (PHM), data-centric engineering analytics--that ultimately enable students to gain effective abilities to systematically engineer industrial data and analytic tools to improve quality, reliability, productivity and resilience of industrial systems.
Prerequisite: ENME 202 or equivalent, experience using Python, or permission of the instructor.
Jointly offered with: ENME485.
Credit only granted for: ENME 691 or ENME 485.
Semesters OfferedFall 2023