Intelligent Kalman filtering for fault detection on an active magnetic bearing system

Nana K. Noel, Kari Tammi, Gregory D. Buckner, Nathan S. Gibson

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


One of the challenges of condition monitoring and fault detection is to develop techniques that are sufficiently sensitive to faults without triggering false alarms. In this paper we develop and experimentally demonstrate an intelligent approach for detecting faults in a single-input, single-output active magnetic bearing. This technique uses an augmented linear model of the plant dynamics together with a Kalman filter to estimate fault states. A neural network is introduced to enhance the estimation accuracy and eliminate false alarms. This approach is validated experimentally for two types of fabricated faults: changes in suspended mass and coil resistance. The Kalman filter alone is shown to be incapable of identifying all fault cases due to modeling uncertainties. When an artificial neural network is trained to compensate for these uncertainties, however, all fault conditions are identified uniquely.
Original languageEnglish
Title of host publicationASME 2008 Dynamic Systems and Control Conference
Number of pages8
ISBN (Electronic)978-07918-3838-9
ISBN (Print)978-0-7918-4335-2
Publication statusPublished - 2008
MoE publication typeA4 Article in a conference publication
EventASME Dynamic Systems and Control Conference - Ann Arbor, United States
Duration: 20 Oct 200822 Oct 2008


ConferenceASME Dynamic Systems and Control Conference
Abbreviated titleDSCC
CountryUnited States
CityAnn Arbor

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