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

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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

Abstrakti

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.
AlkuperäiskieliEnglanti
OtsikkoASME 2008 Dynamic Systems and Control Conference
KustantajaASME
Sivut163-170
Sivumäärä8
ISBN (elektroninen)978-07918-3838-9
ISBN (painettu)978-0-7918-4335-2
DOI - pysyväislinkit
TilaJulkaistu - 2008
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaASME Dynamic Systems and Control Conference - Ann Arbor, Yhdysvallat
Kesto: 20 lokakuuta 200822 lokakuuta 2008

Conference

ConferenceASME Dynamic Systems and Control Conference
LyhennettäDSCC
MaaYhdysvallat
KaupunkiAnn Arbor
Ajanjakso20/10/200822/10/2008

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