This proposal is for the enhancement of research/education al activities of the PI who is moving from Michigan Technological University (MTU) to the University of Southwestern Louisiana (USL) due to family reasons. The major educational activity will be the development of interdisciplinary courses. The first step toward this has already been taken: the PI developed and taught a graduate-level interdisciplinary course on Neural Networks at MTU. The next plan is to: (1) develop an undergraduate version of this course, (2) create a web-based tutorial for this course, (3) share the tutorial and the course outline and other materials with students and faculty nationwide. The undergraduate course on Neural Networks would be developed for a broad spectrum of students (all engineering and computer science majors). After the initial experimental phase with this course is over, the PI has the long-term plan to write an appropriate textbook for it. The technical research will involve a graduate student and an undergraduate student, and focus on the use of neural networks for the implementation of NARMAX a models for nonlinear system identification. Neural networks have been used for modeling and system identification by other researchers, but usually with the goal of control rather than fault detection. The main goal of the proposed research is to use recurrent neural networks (with successive degrees of refinement) to carry out system identification of the specific purpose of generating residuals that can be used to design efficient schemes for detection, isolation, and diagnosis of faults.
