Extended Kalman Filter frequency tracker for nonstationary harmonic signals

a b s t r a c tThe field of prognostics has attracted significant interest from the research community in recent times. Prognostics enables the prediction of failures in machines resulting in benefits to plant operators such as shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning. Prognostic systems have been successfully deployed for the monitoring of relatively simple rotating machines. However, machines and associated systems today are increasingly complex. As such, there is an urgent need to develop prognostic techniques for such complex systems operating in the real world. This review paper focuses on prognostic techniques that can be applied to rotating machinery operating under nonlinear and non-stationary conditions. The general concept of these techniques, the pros and cons of applying these methods, as well as their applications in the research field are discussed. Finally, the opportunities and challenges in implementing prognostic systems and developing effective techniques for monitoring machines operating under nonstationary and non-linear conditions are also discussed.

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“…[77,89]. Hajimolahoseini et al [90] used frequency tracking algorithm based on EKF on non-stationary systems with success.…”

Section: Extended Kalman Filteringmentioning

confidence: 99%

A review on prognostic techniques for non-stationary and non-linear rotating systems

Kan

Tan

Mathew

2015

Mechanical Systems and Signal Processing

305

153

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“…In [8] an extra state x 4 = α was proposed, where α is the rate of change of the amplitude, such that A k+1 = α k A k . Something similar can be done for the frequency tracking using f k+1 = f k + ∆ f,k , where ∆ f,k is the estimated frequency increase.…”

Section: Signal Modelmentioning

confidence: 99%

“…In general, x k , u k , w k , y k and v k are vectors. In this article it is assumed that w and v are zero-mean noise (7), (8) and are uncorrelated (11):…”

Section: Kalman Filtersmentioning

confidence: 99%

Kalman filters for real-time magnetic island phase tracking

Borgers

Lauret

Baar

2013

Fusion Engineering and Design

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For control of neoclassical tearing modes (NTMs) and the resulting rotating magnetic islands in tokamak plasmas, the frequency and phase of the magnetic islands need to be accurately tracked in real-time. In previous experiments on TEXTOR, this was achieved using a phase-locked loop (PLL). For ASDEX Upgrade however, the desired frequency range in which the islands are to be tracked (5 Hz -20 kHz) is much larger than is possible with a PLL. In this contribution an extended Kalman filter (EKF) and an unscented Kalman filter (UKF) are proposed for real-time frequency, phase and amplitude tracking of sinusoidal signals using noisy measurements. Compared to PLLs, the EKF and UKF are able to track magnetic islands in a much larger frequency range. The filters are applied on synthetic data and on experimental data from the TEXTOR and TCV tokamaks, from which we conclude that the UKF can be useful for real-time control of magnetic islands on ASDEX Upgrade.

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“…However, the accuracy drops in the case of time-varying frequencies. In this respect, a number of methods have been conceived to provide online detection of the time-varying amplitude/frequency (see, for example, [1], [2], [3], [4] and references cited therein). Among them, it is worth to recall the adaptive notch-filtering method (ANF) (see [5], [6]) and the Phase-Locked-Loop (PLL) (see, for instance [7], [8]) for their popularity in power and electrical systems, though they are applicable for a single sinusoid only.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive-Observer-Based Robust Estimator of Multi-sinusoidal Signals

Chen

et al. 2018

IEEE Trans. Automat. Contr.

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Abstract-This paper presents an adaptive observer-based robust estimation methodology of the amplitudes, frequencies and phases of biased multi-sinusoidal signals in presence of bounded perturbations on the measurement. The parameters of the sinusoidal components are estimated on-line and the update laws are individually controlled by an excitation-based switching logic enabling the update of a parameter only when the measured signal is sufficiently informative. This way doing, the algorithm is able to tackle the problem of over-parametrization (i.e., when the internal model accounts for a number of sinusoids that is larger than the true spectral content) or temporarily fading sinusoidal components. The stability analysis proves the existence of a tuning parameter set for which the estimator's dynamics are input-to-state stable with respect to bounded measurement disturbances. The performance of the proposed estimation approach is evaluated and compared with other existing tools by extensive simulation trials and real-time experiments.

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Extended Kalman Filter frequency tracker for nonstationary harmonic signals

Cited by 54 publications

References 22 publications

A review on prognostic techniques for non-stationary and non-linear rotating systems

A review on prognostic techniques for non-stationary and non-linear rotating systems

Kalman filters for real-time magnetic island phase tracking

An Adaptive-Observer-Based Robust Estimator of Multi-sinusoidal Signals

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