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Process fault prognosis using a fuzzy-adaptive unscented Kalman predictor

Process fault prognosis using a fuzzy-adaptive unscented Kalman predictor
Process fault prognosis using a fuzzy-adaptive unscented Kalman predictor
By monitoring the future process status via information prediction, process fault prognosis is able to give an early alarm and therefore prevent faults, when the faults are still in their early stages. A fuzzy-adaptive unscented Kalman filter (FAUKF)-based predictor is proposed to improve the tracking and forecasting capability for process fault prognosis. The predictor combines the strong tracking concept and fuzzy logic idea. Similar to the standard adaptive unscented Kalman filter (AUKF) that employs an adaptive parameter to correct the estimation error covariance, a Takagi–Sugeno fuzzy logic system is designed to provide a better adaptive parameter for smoothing this regulation. Compared with the standard AUKF, the proposed FAUKF has the same strong tracking ability but does not suffer from the drawback of serious tracking fluctuation. Two simulation examples demonstrate the effectiveness of the proposed predictor.
0890-6327
813-830
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Cao, Yuping
40cf0d64-d37a-453d-a8df-c4edccc1d531
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Cao, Yuping
40cf0d64-d37a-453d-a8df-c4edccc1d531
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Tian, Xuemin, Cao, Yuping and Chen, Sheng (2011) Process fault prognosis using a fuzzy-adaptive unscented Kalman predictor. International Journal of Adaptive Control and Signal Processing, 25 (9), 813-830.

Record type: Article

Abstract

By monitoring the future process status via information prediction, process fault prognosis is able to give an early alarm and therefore prevent faults, when the faults are still in their early stages. A fuzzy-adaptive unscented Kalman filter (FAUKF)-based predictor is proposed to improve the tracking and forecasting capability for process fault prognosis. The predictor combines the strong tracking concept and fuzzy logic idea. Similar to the standard adaptive unscented Kalman filter (AUKF) that employs an adaptive parameter to correct the estimation error covariance, a Takagi–Sugeno fuzzy logic system is designed to provide a better adaptive parameter for smoothing this regulation. Compared with the standard AUKF, the proposed FAUKF has the same strong tracking ability but does not suffer from the drawback of serious tracking fluctuation. Two simulation examples demonstrate the effectiveness of the proposed predictor.

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Published date: September 2011
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 272650
URI: http://eprints.soton.ac.uk/id/eprint/272650
ISSN: 0890-6327
PURE UUID: d0868728-8df5-4fae-bf68-aefb76cd1ffc

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Date deposited: 09 Aug 2011 08:57
Last modified: 21 Oct 2019 19:32

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