NARX model-based dynamic parametrical model identification of the rotor system with bolted joint

Li, Yuqi; Zhang, Luo; Shi, Baolong; He, Fei

doi:10.1007/s00419-021-01906-4

Cited by 9 publications

(3 citation statements)

References 53 publications

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“…As for the ball bearings, the nonlinear bearing force generated in the vertical and horizontal directions can be calculated by the following [47][48][49] ( ) ( )…”

Section: Rotor-bearing Modelmentioning

confidence: 99%

Numerical and experimental analysis of the effect of eccentric phase difference in a rotor-bearing system with bolted-disk joint

Zhang

Wang

et al. 2021

Nonlinear Dyn

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Bolted joints are widely used in industrial rotating machines to fasten the adjacent disks together and affect system dynamic properties. Therefore, there is a strong need to study their influence on the response of such systems. This paper investigated the effect of the eccentric phase difference of the disk and bolted-disk joint on rotor dynamics, which takes into account the time-varying bending stiffness of the bolted joint. The bolted-disk joint is modeled as a two-node element based upon the energy theorem and Lagrange's principle, where the relative lateral displacement stiffness, relative bending stiffness, and coupling stiffness between the adjacent disks are considered. And then the dynamic model of the rotor-bearing system is derived based on the proposed bolted joint element and lumped mass modeling method. Combining with the Newmark-β integration scheme, the established model allows the dynamic response characteristics of the rotor-bearing system with the bolted joint to be predicted, and the impact of eccentric phase difference in the rotor system on the response to be investigated. The validity of the simulation results was confirmed by experiment. Through the modeling method proposed in this paper and obtained results, the bifurcation characteristics of the bolted joint rotor system can be predicted.

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“…As for the ball bearings, the nonlinear bearing force generated in the vertical and horizontal directions can be calculated by the following [47][48][49] ( ) ( )…”

Section: Rotor-bearing Modelmentioning

confidence: 99%

Numerical and experimental analysis of the effect of eccentric phase difference in a rotor-bearing system with bolted-disk joint

Zhang

Wang

et al. 2021

Nonlinear Dyn

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“…The accuracy of the obtained ENGB system model was experimentally verified. In [13], a new method for system identification of dynamic parameter models of rotor-bearing systems based on exogenous input nonlinear autoregressive (NARX) models was proposed, in which the physical parameters of the system appear as coefficients in the model. In [14], observer/Kalman filter identification (OKID) and bilinear transform discretization (BTD) were proposed, and the results showed that the proposed BTD identification algorithm is simpler and more computationally efficient.…”

Section: Introductionmentioning

confidence: 99%

Reverse Nonlinear Sparrow Search Algorithm Based on the Penalty Mechanism for Multi-Parameter Identification Model Method of an Electro-Hydraulic Servo System

et al. 2022

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Aiming at the multi-parameter identification problem of an electro-hydraulic servo system, a multi-parameter identification method based on a penalty mechanism reverse nonlinear sparrow search algorithm (PRN-SSA) is proposed, which transforms the identification problem of a non-linear system into an optimization problem in a high-dimensional parameter space. In the initial stage of the sparrow search algorithm (SSA), the population distribution is not uniform, and the optimization process is easily disturbed by the local optimal solution. First, adopting a reverse learning strategy increases the exploratory nature of individuals in a population, improves population diversity, and prevents premature maturity. Subsequently, a flexible strain mechanism is provided through the nonlinear convergence factor, adaptive weight factor, and golden sine and cosine factor. The introduction of a nonlinear factor fully balances the global search and local development abilities of the algorithm. Finally, a punishment processing mechanism is developed for vigilantes while retaining the population, providing a suitable search scheme for individuals beyond the boundary, and making full use of the value of each sparrow individual. The effectiveness of each improved strategy is verified through simulation experiments with 23 benchmark functions, and the improved algorithm exhibits better robustness. The results of the model parameter identification of the electro-hydraulic servo system show that the method has a high fitting accuracy between the identification model data and the experimental data, and the fitting degree of the identification model exceeds 97.54%, which further verifies the superiority of the improved algorithm and the effectiveness of the proposed identification strategy.

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“…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:

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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.

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NARX model-based dynamic parametrical model identification of the rotor system with bolted joint

Cited by 9 publications

References 53 publications

Numerical and experimental analysis of the effect of eccentric phase difference in a rotor-bearing system with bolted-disk joint

Numerical and experimental analysis of the effect of eccentric phase difference in a rotor-bearing system with bolted-disk joint

Reverse Nonlinear Sparrow Search Algorithm Based on the Penalty Mechanism for Multi-Parameter Identification Model Method of an Electro-Hydraulic Servo System

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

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