An adaptive confidence limit for periodic non-steady conditions fault detection

Wang, Tianzhen; Wu, Hao; Mengqi, Ni; Zhang, Milu; Dong, Jiwen; Benbouzid, Mohamed; Xiong, Hu

doi:10.1016/j.ymssp.2015.10.015

Cited by 51 publications

(27 citation statements)

References 30 publications

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“…The proposed methods can be expanded to parameter estimation of speech signal processing, 51,52 the self-organizing map, 53 and applied to other fields. [54][55][56][57][58][59][60][61][62][63][64][65]…”

Section: Resultsmentioning

confidence: 99%

The parameter estimation algorithms based on the dynamical response measurement data

2017

Advances in Mechanical Engineering

149

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This article studies the parameter estimation to the system response from the discrete measurement data. By constructing the dynamical rolling cost functions and using the nonlinear optimization, the gradient identification method is presented for estimating the parameters of the sine response signal with double frequency. In order to overcome the difficulty for determining the step size and deduce the influence of noises, the stochastic gradient identification method is derived to estimate the signal parameters. For the purpose of improving the accuracy, a multi-innovation stochastic gradient parameter estimation algorithm is presented using the moving window data. Finally, the simulation examples are provided to test the algorithm performance.

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Section: Resultsmentioning

confidence: 99%

The parameter estimation algorithms based on the dynamical response measurement data

2017

Advances in Mechanical Engineering

149

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“…The proposed algorithms can be extended to other system identification models, e.g., the linear state space models, and Wiener models, 41,42 and Hammerstein-Wiener systems, 43,44 or applied to nonlinear systems 45,46 and other fields. [47][48][49]…”

Section: Discussionmentioning

confidence: 99%

Combined state and parameter estimation for Hammerstein systems with time delay using the Kalman filtering

Ding

Xiong

et al. 2017

Adaptive Control & Signal

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SUMMARYThis paper discusses the state and parameter estimation problem for a class of Hammerstein state space systems with time-delay. Both the process noise and the measurement noise are considered in the system. Based on the observable canonical state space form and the key term separation, a pseudo-linear regressive identification model is obtained. For the unknown states in the information vector, the Kalman filter is used to search for the optimal state estimates. A Kalman-filter based least squares iterative and a recursive least squares algorithms are proposed. Extending the information vector to include the latest information terms which are missed for the time-delay, the Kalman-filter based recursive extended least squares algorithm is derived to obtain the estimates of the unknown time-delay, parameters and states. The numerical simulation results are given to illustrate the effectiveness of the proposed algorithms.

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“…Based on the proposed algorithms, the combined state and parameter estimation are obtained. The proposed algorithms can be extended to other system identification models, e.g., the linear state space models, and Wiener models [41,42] and Hammerstein-Wiener systems [43,44] or applied to nonlinear systems [45,46] and other fields [47,48,49].…”

Section: Discussionmentioning

confidence: 99%

Adaptive filtering‐based multi‐innovation gradient algorithm for input nonlinear systems with autoregressive noise

Mao

Ding

Yang

2017

Adaptive Control & Signal

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In this paper, by means of the adaptive filtering technique and the multi-innovation identification theory, an adaptive filtering-based multi-innovation stochastic gradient identification algorithm is derived for Hammerstein nonlinear systems with colored noise. The new adaptive filtering configuration consists of a noise whitening filter and a parameter estimator. The simulation results show that the proposed algorithm has higher parameter estimation accuracies and faster convergence rates than the multi-innovation stochastic gradient algorithm for the same innovation length. As the innovation length increases, the filtering-based multi-innovation stochastic gradient algorithm gives smaller parameter estimation errors than the recursive least squares algorithm

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An adaptive confidence limit for periodic non-steady conditions fault detection

Cited by 51 publications

References 30 publications

The parameter estimation algorithms based on the dynamical response measurement data

The parameter estimation algorithms based on the dynamical response measurement data

Combined state and parameter estimation for Hammerstein systems with time delay using the Kalman filtering

Adaptive filtering‐based multi‐innovation gradient algorithm for input nonlinear systems with autoregressive noise

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