Process fault prognosis using a fuzzy-adaptive unscented Kalman predictor. (English) Zbl 1234.93104
Summary: 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 estimated 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.
Keywords:
process fault prognosis; unscented Kalman predictor; fuzzy logic; error covariance; Takagi–Sugeno fuzzy logicSoftware:
QSIMVNReferences:
| [1] | Willsky, A survey of design methods for failure detection in dynamic systems, Automatica 12 (6) pp 601– (1976) · Zbl 0345.93067 · doi:10.1016/0005-1098(76)90041-8 |
| [2] | Jardine, A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing 20 (7) pp 1483– (2006) · doi:10.1016/j.ymssp.2005.09.012 |
| [3] | Tian, Situation and perspectives of statistical process control, Journal of China University of Petroleum 32 (5) pp 175– (2008) |
| [4] | Lu, Failure prediction for an on-line maintenance system in a Poisson shock environment, IEEE Transactions on Systems, Man, and Cybernetics 9 (6) pp 356– (1979) · Zbl 0403.94034 · doi:10.1109/TSMC.1979.4310225 |
| [5] | Yang, State estimation for predictive maintenance using Kalman filter, Reliability Engineering and System Safety 66 (1) pp 29– (1999) · doi:10.1016/S0951-8320(99)00015-0 |
| [6] | Yang, An experiment of state estimation for predictive maintenance using Kalman filter on a DC motor, Reliability Engineering and System Safety 75 (1) pp 103– (2002) · doi:10.1016/S0951-8320(01)00107-7 |
| [7] | Juricek, Predictive monitoring for abnormal situation management, Journal of Process Control 11 (2) pp 111– (2001) · doi:10.1016/S0959-1524(00)00043-3 |
| [8] | Cao, Nonlinear system fault prognosis based on SVM and Kalman predictor, Control and Decision 24 (3) pp 477– (2009) |
| [9] | Chen, A new particle predictor for fault prediction of nonlinear time-varying systems, Developments in Chemical Engineering and Mineral Processing 13 (3-4) pp 379– (2005) · doi:10.1002/apj.5500130320 |
| [10] | Ho, The use of ARIMA models for reliability forecasting and analysis, Computers and Industrial Engineering 35 (1-2) pp 213– (1998) · doi:10.1016/S0360-8352(98)00066-7 |
| [11] | Weber, Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas, Engineering Applications of Artificial Intelligence (2010) · doi:10.1016/j.engappai.2010.06.002 |
| [12] | Wang, Prognosis of machine health condition using neuro-fuzzy systems, Mechanical Systems and Signal Processing 18 (4) pp 813– (2004) · doi:10.1016/S0888-3270(03)00079-7 |
| [13] | Wang, An adaptive predictor for dynamic system forecasting, Mechanical Systems and Signal Processing 21 (2) pp 809– (2007) · doi:10.1016/j.ymssp.2005.12.008 |
| [14] | Wang, Novel grey model for the prediction of trend of dissolved gases in oil-filled power apparatus, Electric Power System Research 67 (1) pp 53– (2003) · doi:10.1016/S0378-7796(03)00047-6 |
| [15] | Zhao, Predictive maintenance policy based on process data, Chemometrics and Intelligent Laboratory Systems 104 (1) pp 137– (2010) · doi:10.1016/j.chemolab.2010.06.009 |
| [16] | Watson, Dynamic modeling and wear-based remaining useful life prediction of high power clutch systems, Tribology Transactions 48 (2) pp 208– (2005) · doi:10.1080/05698190590927451 |
| [17] | Li, Stochastic prognostics for rolling element bearings, Mechanical Systems and Signal Processing 14 (5) pp 747– (2000) · doi:10.1006/mssp.2000.1301 |
| [18] | Yang, A condition-based preventive maintenance arrangement for thermal power plants, Electric Power Systems Research 72 (1) pp 49– (2004) · doi:10.1016/j.epsr.2004.03.007 |
| [19] | Tran, Machine condition prognosis based on regression trees and one-step-ahead prediction, Mechanical Systems and Signal Processing 22 (5) pp 1179– (2008) · doi:10.1016/j.ymssp.2007.11.012 |
| [20] | Zhou, Extension of Friedland’s separate-bias estimation to randomly time-varying bias of nonlinear systems, IEEE Transactions on Automatic Control 38 (8) pp 1270– (1993) · Zbl 0793.93118 · doi:10.1109/9.233167 |
| [21] | Jwo, Adaptive fuzzy strong tracking extended Kalman filtering for GPS navigation, IEEE Sensors Journal 7 (5) pp 778– (2007) · doi:10.1109/JSEN.2007.894148 |
| [22] | Julier, A new method for the nonlinear transformation of means and covariances in filters and estimators, IEEE Transactions on Automatic Control 45 (3) pp 477– (2000) · Zbl 0973.93053 · doi:10.1109/9.847726 |
| [23] | Julier, Unscented filtering and nonlinear estimation, Proceeding of IEEE 92 (3) pp 401– (2004) · doi:10.1109/JPROC.2003.823141 |
| [24] | Xiong, Performance evaluation of UKF-based nonlinear filtering, Automatica 42 (2) pp 261– (2006) · Zbl 1103.93045 · doi:10.1016/j.automatica.2005.10.004 |
| [25] | Xiong, Robust unscented Kalman filtering for nonlinear uncertain systems, Asian Journal of Control 12 (3) pp 426– (2010) · doi:10.1002/asjc.190 |
| [26] | Rambabu, Applying the unscented Kalman filter for nonlinear state estimation, Journal of Process Control 18 (7-8) pp 753– (2008) · doi:10.1016/j.jprocont.2007.11.004 |
| [27] | Romanenko, The unscented filter as an alternative to the EKF for nonlinear state estimation, a simulation case study, Computers and Chemical Engineering 28 (3) pp 347– (2004) · doi:10.1016/S0098-1354(03)00193-5 |
| [28] | Qu, Process monitoring and parameter estimation via unscented Kalman filtering, Journal of Loss Prevention in the Process Industries 22 (6) pp 703– (2009) · doi:10.1016/j.jlp.2008.07.012 |
| [29] | Cao, Fault detection method based on multi-variable sequential probability ratio test of unscented Kalman filter innovation, Journal of China University of Petroleum 34 (3) pp 165– (2010) |
| [30] | Cao, Information divergence based process fault detection and diagnosis, Journal of Zhejiang University 44 (7) pp 1315– (2010) |
| [31] | Cao YP Tian XM An adaptive UKF algorithm for process fault prognostics 487 490 |
| [32] | Xiong, Author’s reply to ’Comments on Performance evaluation of UKF-based nonlinear filtering’, Automatica 43 (3) pp 569– (2007) · Zbl 1137.93418 · doi:10.1016/j.automatica.2006.10.002 |
| [33] | Xu JH Dimirovski GM Jiang YW Shen C UKF design and stability for nonlinear stochastic systems with correlated noises 6226 6231 |
| [34] | Sugeno, Industrial Applications of Fuzzy Control (1985) · Zbl 0586.93053 |
| [35] | Maybeck, Stochastic Models, Estimation and Control (1979) |
| [36] | Lee DJ Nonlinear Bayesian Filtering with Applications to Estimation and Navigation 2005 |
| [37] | Song, An adaptive UKF algorithm for the state and parameter estimations of a mobile robot, Acta Automatica Sinica 34 (1) pp 72– (2008) · Zbl 1174.93712 · doi:10.3724/SP.J.1004.2008.00072 |
| [38] | Li, A strong tracking unscented Kalman filter and its application in passive target tracking, Telecommunication Engineering 45 (1) pp 160– (2005) |
| [39] | Stoumbos, The state of statistical process control as we proceed into the 21st century, Journal of the American Statistical Association 95 (451) pp 992– (2000) · doi:10.2307/2669484 |
| [40] | Chiang, Fault Detection and Diagnosis in Industrial Systems (2001) · doi:10.1007/978-1-4471-0347-9 |
| [41] | Genz, Numerical computation of multivariate normal probabilities, Journal of Computational and Graphical Statistics 1 (2) pp 141– (1992) · Zbl 0741.65015 · doi:10.2307/1390838 |
| [42] | Genz, Numerical computation of rectangular bivariate and trivariate normal and t probabilities, Statistics and Computing 14 (3) pp 251– (2004) · doi:10.1023/B:STCO.0000035304.20635.31 |
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