MECHENG 7372
Transcript Abbreviation:
(N/A)
Course Description:
Covers the theory and application of fault diagnosis in multi-domain dynamic systems. Theory and case studies drawn from industrial applications.
Course Levels:
(N/A)
Designation:
(N/A)
General Education Course:
(N/A)
Cross-Listings:
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
3.00
Select if Repeatable:
Off
Maximum Repeatable Credits:
(N/A)
Total Completions Allowed:
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
(N/A)
Prerequisites and Co-requisites:
Prereq: 7380, ECE 4551, 5551, AeroEng 4521, or permission of instructor.
Electronically Enforced:
No
Exclusions:
(N/A)
Course Goals / Objectives:
Introduce the concepts of fault diagnosis and prognosis. Develop methodologies for system hazard analysis including fault trees and failure modes and effects analysis
Review of linear system theory with focus on multivariable systems and observability concepts
Present analytical redundancy concepts and develop the notion of fault modeling and residuals
Develop the parity equation method for diagnosing systems using linear input-output models. Develop design methods for the synthesis of parity equation algorithms
Develop observer-based methods for diagnosis systems using state estimation methods. Luenberger observers, reduced-order and unknown-input observers; Kalman filters, extended Kalman filters
Introduce the concept of passive robustness in fault detection, including threshold adaptation
Develop methods for the statistical analysis and testing of residuals. Binary hypothesis testing, sequential probability ratio test, other statistical methods
Introduce frequency-domain methods for signal analysis. Diagnosis by spectral estimation. Parameter identification methods. Use of non-stationary (time-frequency) analysis methods
Develop methods for fault diagnosis in nonlinear systems; nonlinear parity equation residual generation methods; nonlinear observers
Introduce the concept of prognosis, and develop analysis and synthesis methods for model-based prognosis
Check if concurrence sought:
No
Contact Hours:
(N/A)
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| No Grade Breakdown Entered. | |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| No Textbooks and Other Course Materials Entered. | ||
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
(N/A)
Embedded Literacies Info: