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Real-time frequency estimation for sinusoidal signals with application to robust fault detection. (English) Zbl 1283.93273

Summary: This paper has investigated the problem of estimating unknown frequencies of a given sinusoidal signal with disturbances and noises, as well as its usages for fault detection. On the basis of a parametric linear model of the signal, a gradient estimator with leakage is adopted to identify the frequencies. The estimation mechanism is applied to fault detection, as it achieves not only the tolerance to disturbances but also the sensitivity to faults. Simulations verify the feasibility and capability of the frequency identifier for estimating frequencies and detecting faults.

MSC:

93E10 Estimation and detection in stochastic control theory
94C12 Fault detection; testing in circuits and networks
93C73 Perturbations in control/observation systems
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[1] Hsu, A globally convergent frequency estimator, IEEE Transactions on Automatic Control 44 (4) pp 698– (1999) · Zbl 0958.93089 · doi:10.1109/9.754808
[2] Marino, Global estimation of n unknown frequencies, IEEE Transactions on Automatic Control 47 (8) pp 1324– (2002) · Zbl 1364.93778 · doi:10.1109/TAC.2002.800761
[3] Hou, Amplitude and frequency estimator of a sinusoid, IEEE Transactions on Automatic Control 50 (6) pp 855– (2005) · Zbl 1365.93485 · doi:10.1109/TAC.2005.849244
[4] Lee, Time-varying frequency estimation by VFF Kalman filtering, Signal Processing 77 (3) pp 343– (1999) · Zbl 0941.94007 · doi:10.1016/S0165-1684(99)00085-7
[5] Hou, Estimation of sinusoidal frequencies and amplitudes using adaptive identifier and observer, IEEE Transactions on Automatic Control 52 (3) pp 493– (2007) · Zbl 1366.93634 · doi:10.1109/TAC.2006.890389
[6] Sharma, Design of asymptotically convergent frequency estimation using contraction theory, IEEE Transactions on Automatic Control 53 (8) pp 1932– (2008) · Zbl 1367.93152 · doi:10.1109/TAC.2008.927682
[7] Jia, Disturbance rejection through disturbance observer with adaptive frequency estimation, IEEE Transactions on Magnetics 45 (6) pp 2675– (2009) · doi:10.1109/TMAG.2009.2018605
[8] Yang, Robust modified Newton algorithm for adaptive frequency estimation, IEEE Signal Processing Letters 14 (11) pp 879– (2007) · doi:10.1109/LSP.2007.903272
[9] Yang J Xi H Yang F Adaptive modified Newton algorithm for multiple frequencies estimation 7th World Congress on Intelligent Control and Automation (WCICA) 2008 2992 2995 10.1109/WCICA.2008.4593399
[10] So, Adaptive algorithm for direct frequency estimation, IEE Proceedings-Radar, Sonar and Navigation 151 (6) pp 359– (2004) · doi:10.1049/ip-rsn:20041001
[11] Dash, Adaptive complex unscented Kalman filter for frequency estimation of time-varying signals, IET Science, Measurement & Technology 4 (2) pp 93– (2010) · doi:10.1049/iet-smt.2009.0003
[12] Yang S Zhao Q Real-time frequency estimation of sinusoids with low-frequency disturbances 2011 American Control Conference 2011 4275 4280
[13] Ioannou, Robust Adaptive Control (1996)
[14] Chen, Linear System Theory and Design (1999)
[15] Desoer, Feedback Systems: Input-Output Properties (1975)
[16] Sastry, Adaptive Control: Stability, Convergence, and Robustness (1989)
[17] Preston, Application of a robust nonlinear fault detection observer to a hydraulic system, UKACC International Conference on Control 2 pp 1484– (1996) · doi:10.1049/cp:19960771
[18] Yu, A bilinear fault detection observer and its application to a hydraulic drive system, International Journal of Control 64 (6) pp 1023– (1996) · Zbl 0850.93121 · doi:10.1080/00207179608921673
[19] Wen X Observer based fault detection method for a hydraulic rig M. Eng. Report 2008
[20] Ding, Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms and Tools (2008)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.