Actuator Fault Detection for Discrete-Time Descriptor Systems via a Convex Unknown Input Observer with Unknown Scheduling Variables

Martinez, Javier; Aguiar, Braulio; Estrada-Manzo, Víctor; Bernal, Miguel

doi:10.1155/2021/8825609

Cited by 2 publications

(3 citation statements)

References 74 publications

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“…The learning rate of GRU determines whether the fitness function can converge to the minimum, which is optimized by EO. The index RMSE taken as the fitness function of EO optimization is shown as Equation (20), in which fi refers to the prediction value of REWS obtained through the GRU neural network and f i refers to the actual REWS. In order to obtain fi , the four component quantities (including high-, medium-, and low-frequency groups as well as the residual) under all average wind speeds are used as the input of the GRU neural network, and the first 3/4 of the input is used for training.…”

Section: Gru Prediction Based On Eomentioning

confidence: 99%

“…Initial weight Aggregation computing (18) Next population of Weight (17) Update EO concentrations (15), (16) EO fitness (20) Construct equilibrium pool ( 14…”

Section: Rews Endmentioning

confidence: 99%

“…This type of method mainly includes two parts: data extraction and prediction modeling. The former generally refers to data selection and information acquisition [20], while the latter refers to algorithm selection and extrapolation prediction [21]. Differently from general wind speed prediction, the prediction of REWS needs to establish a complex spatiotemporal mapping between inputs and output.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

From Lidar Measurement to Rotor Effective Wind Speed Prediction: Empirical Mode Decomposition and Gated Recurrent Unit Solution

Shi,

Liu,

Deng

et al. 2023

Sensors

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Conventional wind speed sensors face difficulties in measuring wind speeds at multiple points, and related research on predicting rotor effective wind speed (REWS) is lacking. The utilization of a lidar device allows accurate REWS prediction, enabling advanced control technologies for wind turbines. With the lidar measurements, a data-driven prediction framework based on empirical mode decomposition (EMD) and gated recurrent unit (GRU) is proposed to predict the REWS. Thereby, the time series of lidar measurements are separated by the EMD, and the intrinsic mode functions (IMF) are obtained. The IMF sequences are categorized into high-, medium-, and low-frequency and residual groups, pass through the delay processing, and are respectively used to train four GRU networks. On this basis, the outputs of the four GRU networks are lumped via weighting factors that are optimized by an equilibrium optimizer (EO), obtaining the predicted REWS. Taking advantages of the measurement information and mechanism modeling knowledge, three EMD–GRU prediction schemes with different input combinations are presented. Finally, the proposed prediction schemes are verified and compared by detailed simulations on the BLADED model with four-beam lidar. The experimental results indicate that compared to the mechanism model, the mean absolute error corresponding to the EMD–GRU model is reduced by 49.18%, 53.43%, 52.10%, 65.95%, 48.18%, and 60.33% under six datasets, respectively. The proposed method could provide accurate REWS prediction in advanced prediction control for wind turbines.

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Section: Gru Prediction Based On Eomentioning

confidence: 99%

“…Initial weight Aggregation computing (18) Next population of Weight (17) Update EO concentrations (15), (16) EO fitness (20) Construct equilibrium pool ( 14…”

Section: Rews Endmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

From Lidar Measurement to Rotor Effective Wind Speed Prediction: Empirical Mode Decomposition and Gated Recurrent Unit Solution

Shi,

Liu,

Deng

et al. 2023

Sensors

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A fault detection method based on the self-organizing fuzzy Luenberger for the generalized linear discrete time system with disturbances

Li¹,

Jin²,

Bao

et al. 2023

IFS

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A novel for an integrated fault and states estimator was proposed for the generalized linear discrete-time system with disturbances. The proposed scheme was based on a Self-Organizing fuzzy Luenberger system to estimate the states and approximate the fault information simultaneously for the generalized linear discrete-time system. For this purpose, a generalized linear discrete-time system with disturbances was transformed into an equivalent standard state-space system with disturbances and faults. Then, the faults and disturbances of generalized linear discrete-time system can be separated with the coordinate transform, meanwhile the Self-Organizing fuzzy Luenberger estimator was designed to obtain the accurate fault information. Based on the obtained fault information, the fault detection experiments were performed, and the fault feature parameters required for fault isolation were determined. Finally, the proposed strategy was applied for a direct current motor to demonstrate the effectiveness of the proposed approach.

show abstract

Actuator Fault Detection for Discrete-Time Descriptor Systems via a Convex Unknown Input Observer with Unknown Scheduling Variables

Cited by 2 publications

References 74 publications

From Lidar Measurement to Rotor Effective Wind Speed Prediction: Empirical Mode Decomposition and Gated Recurrent Unit Solution

From Lidar Measurement to Rotor Effective Wind Speed Prediction: Empirical Mode Decomposition and Gated Recurrent Unit Solution

A fault detection method based on the self-organizing fuzzy Luenberger for the generalized linear discrete time system with disturbances

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