NARX Model Identification Using Correntropy Criterion in the Presence of Non-Gaussian Noise

Araújo, Ícaro Bezerra Queiroz de; Guimarães, João Paulo; Fontes, Aluisio I. R.; Linhares, Leandro L. S.; Martins, Allan de Medeiros; Araújo, Fábio Meneghetti Ugulino de

doi:10.1007/s40313-019-00476-9

Cited by 13 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first is to employ timesequential methods, such as the Long Short-Term Memory (LSTM) 9 neural network, and the second is to combine static neural networks and Nonlinear Autoregressive with Exogenous Inputs (NARX). [10][11][12] Considering that the storage capacity and the computational complexity, the data-driven model needs to be close to the interpolation model, hence a shallow Back-Propagation neural network with NARX (NARX-BPNN) is adopted.…”

Section: Introductionmentioning

confidence: 99%

Aero-engine on-board model based on big Quick Access Recorder data

Ren

Zhu

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

With the development of the Full Authority Digital Engine Controller (FADEC) technology, the aero-engine on-board model is widely used in Engine Health Management (EHM) and control. Due to the FADEC’s limited computational capability and storage capacity, the model should not be very intricate; consequently, the interpolation model is widely utilized. Although the interpolation model’s low precision precludes further development of on-board models for EHM and control. To address the trade-off between precision and complexity, a novel on-board modeling method is proposed based on the Nonlinear Autoregressive with Exogenous Inputs Backpropagation neural network (NARX-BPNN) trained using the mini-batch Levenberg–Marquardt (LM) algorithm on large Quick Access Recorder (QAR) data. The NARX model’s features and time delay are chosen by referring to the line interpolation model, which gives interpretability for feature selection. The combination of a shallow neural network and big data training can guarantee the on-board model’s real-time and storage requirements, as well as its generalizability. The mini-batch LM method can avoid both the local optimum problem in the shallow neural network and the storage difficulty associated with massive data while still achieving a rapid convergence rate due to the LM algorithm’s global view. The NARX-BPNN models are compared to an existing line interpolation model using 100 different aero-engines' QAR data. The results reveal that accuracy may be increased by approximately 30% while maintaining superior dynamic performance and anti-noise capacity compared to the line interpolation approach.

show abstract

Section: Introductionmentioning

confidence: 99%

Aero-engine on-board model based on big Quick Access Recorder data

Ren

Zhu

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

show abstract

“…Peng et al [28] established the mathematical model of aluminum plate based on the NARX model, and a further fault diagnosis is carried out. Araújo et al [29] derived the NARX model of Quanser Servor Base Unit, the relationship between the voltage applied to the motor and position was successfully established based on the model. In addition, the NARX-based modeling method can also be used in scenarios such as steel plate identification [30] and the modeling of global magnetic disturbance in near-Earth space [31].…”

Section: Introductionmentioning

confidence: 99%

Frequency Sweep Modeling Method for the Rotor-Bearing System in Time Domain Based on Data-Driven Model

Jin

Zhu

et al. 2022

Processes

View full text Add to dashboard Cite

In practice, the modeling and analysis of nonlinear rotor-bearing systems are difficult due to the nonlinearity and complexity. In the previous studies, finite element simulation and mathematical modeling methods are mostly adopted to conduct the analysis. However, due to the time-consuming problem in finite element simulation and the lack of sufficient prior knowledge in mathematical modeling, the traditional method is difficult to establish the representation model. In order to overcome this issue, in this study, a data-driven model referred to as the NARX (Nonlinear Auto-Regressive with exogenous inputs) model is introduced to conduct the modeling and analysis of the rotor-bearing system. The identification of the NARX model requires random excitation as the system input, while the input signal of the rotor system is harmonics. Therefore, a time-domain frequency sweep modeling method is proposed in this paper by introducing the rotating speed into the coefficients function expression of the NARX model, the system output can be predicted according to the given speed. Moreover, the representation model of the rotor-bearing system obtained by using the proposed method is validated under different rotating speeds, the results show the applicability of the proposed modeling approach. Finally, an experimental case of the rotor-bearing test rig is demonstrated to show the application in practice. Both the numerical and experimental studies illustrate the applicability of the proposed modeling method, which provides a reliable model for dynamic analysis and fault diagnosis of the rotor-bearing system.

show abstract

Digital Twin Application on System Identification and Control

Pattanaik,

Mohanty

2024

Simulation Techniques of Digital Twin in Real‐Time Applications

View full text Add to dashboard Cite

NARX Model Identification Using Correntropy Criterion in the Presence of Non-Gaussian Noise

Cited by 13 publications

References 31 publications

Aero-engine on-board model based on big Quick Access Recorder data

Aero-engine on-board model based on big Quick Access Recorder data

Frequency Sweep Modeling Method for the Rotor-Bearing System in Time Domain Based on Data-Driven Model

Digital Twin Application on System Identification and Control

Contact Info

Product

Resources

About