Parameter Identification Method Research Articles

Synchronous Vibration Parameter Recognition of Constant-Speed Blades Based on Blade Tip Clearance Measurement

A new method for the synchronous vibration parameter identification of constant-speed rotating blades based on blade tip clearance (BTC) measurement and blade tip timing (BTT) is introduced. A BTC sensor is used to measure the BTC when the blade tip passes through each sensor. The BTT method is used to determine whether the blade tip arrives in advance or lags. The geometric model between the BTC and the blade tip vibration displacement (BTVD) is established, and the BTVD of the blade tip passing through each sensor is obtained. Then, the nonlinear least squares method is used to determine the synchronous vibration parameters of the constant-speed rotating blade. The results show that with an increase in amplitude, the higher the accuracy of the vibration parameter identification proposed in this paper; with a decrease in the random error of the BTC measurement, the higher the accuracy of the vibration parameter identification proposed in this paper; with a decrease in the random error in the measurement of the blade disk dimensions, the higher the accuracy of the vibration parameter identification proposed in this paper. In addition, the smaller the ratio of the blade length to the blade disk radius, the higher the accuracy of the vibration parameter identification method introduced in this paper. Because the structure of the gas turbine compressor and turbine blade disk has a small blade disk ratio, the method proposed in this paper is suitable for the simultaneous vibration parameter identification of gas turbine compressor blades and turbine blades.

Battery parameter identification method of a battery module based on a multi-physical measurement system

The secondary utilization of retired electric vehicle batteries is beneficial for improving resource utilization efficiency. Capacity and internal resistance are battery parameters that can reflect the battery state. To identify the parameters of a single battery in a battery module, it is usually necessary to disassemble the battery module. The process is complex, time-consuming, and unsafe. In this paper, a battery parameter identification method without disassembling the battery module is developed based on a multi-physical measurement system. First, a multi-physical electrical-thermal-spatial measurement system is constructed to measure the physical parameters of the battery module during charging and discharging. Then, the electrical model and the thermal model are developed. To obtain the capacity and internal resistance of each cell within the battery module, a battery parameter identification model is established with temperature and total battery current as input parameters and battery capacity and internal resistance as output parameters. Batteries with different capacities and internal resistances are connected in series and parallel to simulate retired battery modules for electric vehicles. The battery parameters are identified using the method presented in this paper. According to the experiments, the relative errors of parameter identification for battery capacity and internal resistance are both within 9 %. This method can improve the efficiency of the battery screening process as well as the reliability of the battery system.

Parametric identification method for an absorption air conditioning parabolic trough collector solar plant

In this work is established a parametric identification method for an absorption air conditioning solar plant. A scaled thermal plant, consisting of a thermal capacitor and a flow line that acts as a capacitor and thermal energy radiator is used. As the mathematical model of the scaled plant is structurally identical to that of the solar plant the first is used to determine the methodology that can be used later for the identification of the PTC solar plant. Parametric identification is a necessary step that allows to determine the unknown parameters of the mathematical model of any solar/thermal plant. This model then can be used to analyze the plant characteristics and design an appropriate control algorithm. Although the system model is nonlin-ear it can be expressed in the form of a linear regressor in the parameters. This permits to use the least squares method as the identification method. The method is applied to the thermal plant to identify the useful form that the covariance matrix and excitation signals should have to ensure that when applied to the solar plant its unknown parameters can be properly estimated. Once the solar plant parameters are properly estimated model can be used to analyze and simulate the operation of the ab-sorption air conditioning system.

Identification and modelling of parameters for the information-physical-social convergence characteristics of user-side flexible resources

The use of flexible resource information on the user side helps to increase system efficiency. Power system power variation becomes more pronounced with the access to renewable resources. Therefore, the study proposes a parameter identification and modeling method for the physical and social integration characteristics of flexible resource information on the user side. Taking the user’s air conditioning load as the object, the thermal dynamic model of the air conditioning building is constructed using equivalent thermal parameters, and the variable frequency air conditioning load is embedded in the battery model. The model parameter identification is carried out using high-dimensional model expression technology. According to the experimental data, in options 2 and 3, the system operator makes power purchases based on the storage status of the lithium battery or virtual battery, increasing the number of power purchases when the price of electricity is low and decreasing the number of power purchases when the price of electricity is high. This effectively reduces the system operator’s electricity costs. The error of multiple linear regression modelling varies widely, with relative errors up to 0.75 and an average relative error of 15.1%. The relative error of modelling based on the high-dimensional model expression technique is in the range of 0 to 0.2, with an average relative error of 5.5%. The results show that compared with multiple linear regression models, high-dimensional model representation technology has higher modeling accuracy and can accurately identify the parameters of the air conditioning load aggregation model, solving the problem of difficult parameter calculation in the practical application of the air conditioning load aggregation model, and providing technical support for power system regulation.

An improved dynamic model identification method for small unmanned helicopter

PurposeThe purpose of this paper is to introduce an improved system identification method for small unmanned helicopters combining adaptive ant colony optimization algorithm and Levy’s method and to solve the problem of low model prediction accuracy caused by low-frequency domain curve fitting in the small unmanned helicopter frequency domain parameter identification method.Design/methodology/approachThis method uses the Levy method to obtain the initial parameters of the fitting model, uses the global optimization characteristics of the adaptive ant colony algorithm and the advantages of avoiding the “premature” phenomenon to optimize the initial parameters and finally obtains a small unmanned helicopter through computational optimization Kinetic models under lateral channel and longitudinal channel.FindingsThe algorithm is verified by flight test data. The verification results show that the established dynamic model has high identification accuracy and can accurately reflect the dynamic characteristics of small unmanned helicopter flight.Originality/valueThis paper presents a novel and improved frequency domain identification method for small unmanned helicopters. Compared with the conventional method, this method improves the identification accuracy and reduces the identification error.

Vibration-Based Recognition of Wheel-Terrain Interaction for Terramechanics Model Selection and Terrain Parameter Identification for Lugged-Wheel Planetary Rovers.

Identifying terrain parameters is important for high-fidelity simulation and high-performance control of planetary rovers. The wheel-terrain interaction classes (WTICs) are usually different for rovers traversing various types of terrain. Every terramechanics model corresponds to its wheel-terrain interaction class (WTIC). Therefore, for terrain parameter identification of the terramechanics model when rovers traverse various terrains, terramechanics model switching corresponding to the WTIC needs to be solved. This paper proposes a speed-independent vibration-based method for WTIC recognition to switch the terramechanics model and then identify its terrain parameters. In order to switch terramechanics models, wheel-terrain interactions are divided into three classes. Three vibration models of wheels under three WTICs have been built and analyzed. Vibration features in the models are extracted and non-dimensionalized to be independent of wheel speed. A vibration-feature-based recognition method of the WTIC is proposed. Then, the terrain parameters of the terramechanics model corresponding to the recognized WTIC are identified. Experiment results obtained using a Planetary Rover Prototype show that the identification method of terrain parameters is effective for rovers traversing various terrains. The relative errors of estimated wheel-terrain interaction force with identified terrain parameters are less than 16%, 12%, and 9% for rovers traversing hard, gravel, and sandy terrain, respectively.

Research on railway vehicle modal parameter identification method based on drop impact load

This paper investigates the modal parameter identification technique utilizing a drop impact load. A 12-DOF mathematical model of a Railway Vehicle System (RWVS) was constructed, followed by theoretical calculations, simulation analysis, and tests with four different drop impact loads. The responses to these loads were inspected through FFT, and the modal frequencies of the RWVS were determined by the Peak Picking (P-P) method. The simulation demonstrated that four types of drop impact loads can activate the bounce, pitch, and roll modes of the RWVS. The theoretical calculation and simulation analysis revealed that the maximum error of modal frequency identification is no greater than 6.5%. The experimental results verified that the drop impact excitation method can be used to identify the modal parameters of a complex railway vehicle system, which is highly beneficial for the design and verification of the vehicle structure.

Examination of machine learning method for identification of material model parameters

In this work, we compare two methods of using artificial neural network (ANN) to determine the optimal parameters of material model by results of high-speed plate impact experiments. The first ANN is trained to determine the free surface velocity history based on the impact parameters and material model parameters; this ANN is used as a fast emulator of model to speed-up the statistical Bayesian calibration of the model parameters. The second ANN directly determines the material model parameters based on the impact parameters and the free surface velocity history, because this ANN is trained by inverse data set; this approach for inverse problem is proposed for the first time and can be more preferable for fast-track expert systems in engineering applications. Both approaches are successfully used to determine the optimal parameters of material model in the case of impact of copper and aluminum plates at different temperatures and impact velocities with experimental data from the literature. A simple relaxation plasticity model with a constant relaxation time is used in the present research as an example; more accurate coincidence with the experimental data and wider applicability of the ANN-based approach can be expected for a more realistic plasticity model.

Co-estimation of maximum available capacity and state-of-charge for lithium-ion batteries in multi-operating mode with temperature and degradation state adaptivity

Accurate state of charge (SOC) estimation is critical for battery management systems (BMS). A new SOC estimation framework is developed, containing four modules: battery modeling, parameter identification, SOC estimation, and capacity estimation. It mainly makes three improvements to improve the adaptability. In the parameter identification module, an improved forgetting factor parameters identification method is proposed to obtain model parameters with high precision. For SOC estimation, considering the impact of noise on SOC estimation, an adaptive square root unscented Kalman filter is proposed, which can solve the divergence issue caused by inappropriate noise matrix iteration. Moreover, a weighted recursive least squares algorithm is developed to estimate the maximum available capacity and feed the result back into the SOC estimation module to improve the estimation accuracy. The experimental results show that the proposed framework can obtain high-precision SOC estimation results under different temperatures and aging conditions.

An Accurate Dynamic Model Identification Method of an Industrial Robot Based on Double-Encoder Compensation

Aiming at the challenges to accurately simulate complex friction models, link dynamics, and part uncertainty for high-precision robot-based manufacturing considering mechanical deformation and resonance, this study proposes a high-precision dynamic identification method with a double encoder. Considering the influence of the dynamic model of the manipulator on its control accuracy, a three-iterative global parameter identification method based on the least square method and GMM (Gaussian Mixture Model) under the optimized excitation trajectory is proposed. Firstly, a bidirectional friction model is constructed to avoid using residual torque to reduce the identification accuracy. Secondly, the condition number of the block regression matrix is used as the optimization objective. Finally, the joint torque is theoretically identified with the weighted least squares method. A nonlinear model distinguishing between high and low speeds was established to fit the nonlinear friction of the robot. By converting the position and velocity of the motor-side encoder to the linkage side using the deceleration ratio, the deformation quantity could be calculated based on the discrepancy between theoretical and actual values. The GMM algorithm is used to compensate the uncertainty torque that was caused by model inaccuracy. The effectiveness of the proposed method is verified by a simulation and experiment on a 6-DoF industrial robot. Results prove that the proposed method can enhance the online torque estimation performance by up to 20%.

Dynamic parameter identification based on improved particle swarm optimization and comprehensive excitation trajectory for 6R robotic arm

PurposeRobotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm.Design/methodology/approachThis study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification.FindingsExperiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved.Originality/valueA new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.

Orthotropic Material Parameters Identification Method of Stator Core and Windings in Electric Motors

The accurate equivalent orthotropic material parameters (OMPs) of stator core and windings are the basis of modal analysis in electric motors and the key to precisely predicting electromagnetic vibration and noise. A CNN (convolutional neural network)-MPGA (multi-population genetic algorithm) based OMPs identification method of stator core and windings for electric motors is proposed in this study. Firstly, the surrogate models between the material parameters and eigenmode frequencies are trained with CNN for stator core and windings. Then, MPGA is utilized to identify the equivalent OMPs of stator core and windings combined with tested eigenmode frequencies. Finally, two different classes of electric machines are selected to verify the method. The CNN-MPGA based method in this study can quickly and accurately identify OMPs of stator core and windings. It is significant for the calculation and suppression of electromagnetic noise for electric motors.

Modal Identification of Aircraft Time-varying Structure Based on Fiber Grating Measurement

To meet the increasing mission requirements, the time-varying characteristics of the aircraft structural system have been increasingly considered and utilized. Therefore, it is significant to obtain the structural modal parameters during the mission of the aircraft. However, the traditional electrical sensors have many limitations in the online measurement of spacecraft structure, and fiber grating sensors are expected to better adapt to the requirements. The online modal parameter identification methods of time-varying structures also lack sufficient research. The traditional modal parameter identification methods include non-parametric method and parametric method. However, the rapid development of neural networks in recent years makes them an effective tool for modal parameter identification. In this paper, a moving mass beam simulation model is established to simulate the time-varying characteristics of the structure, and the modal parameters of the dynamic strain signal are identified. The comparison results of three identification methods are obtained.

Parameter Estimation of Multiphase Machines Applicable to Variable Phase-Pole Machines

Variable phase-pole machines are envisioned in applications requiring a large torque-speed operating area. The control of these machines relies on accurate models and parameters but research on their parameter identification is scarce. This paper presents an offline parameter-identification method for variable phase-pole machines adopting a harmonic-plane decomposition model. The method employs standard tests with single-frequency three-phase excitation in multiple pole configurations and uses the results to minimize a constrained, regularized weighted least-squares problem. It relies on identifying a set of parameters common to all phase-pole configurations and transforming them into the adopted model. Good agreement is exhibited when comparing experimental results to an analytical harmonic-plane decomposition model using the inferred parameters. Steady-state, as well as pole transitions, are compared. The paper emphasizes the importance of performing measurements in multiple pole configurations and weighing these measurements appropriately to render an accurate set of parameters.

An empirical parameter identification method considering hysteresis effects for LiFePO4 battery's electrochemical model

Compared with other types of lithium-ion batteries, there is an obvious hysteresis effect in LiFePO4 battery, which leads to inaccurate parameter identification for its mechanical modelling. This article proposes an empirical parameter identification method for the P2D model of LiFePO4 battery, which takes the hysteresis effect into consideration. The proposed method adds a modified zero-state model to describe hysteresis effects during the identification process of lithium intercalation intervals. Simulation results prove that errors of identified parameters under various working conditions are relatively small, and the average relative error is within 0.5 %. The proposed method to compensate for hysteresis effect is relatively simple and easy to implement, and can improve the accuracy and generalization ability of the electrochemical model of LiFePO4 batteries.

Optimal viscoelastic properties for passive damping treatments

In the context of noise and vibration control, constrained viscoelastic materials can be used to reduce structure-borne noise of vibrating structure. This passive damping treatment finds many applications due to its robustness and low cost. While several approaches are proposed in the literature to optimise the placement of constrained viscoelastic patches, few studies has been dedicated to the optimisation of the viscoelastic material's properties. The difficulty stems from their frequency-dependency. In this work, a fractional derivative model is used to describe the complex frequency-dependent viscoelastic properties. This study illustrates the effectiveness of using surrogate modeling to predict the damping performance of viscoelastic patches. A multi-model reduction method combined with a modal parameter identification method is used to build a surrogate model at a reasonable computational cost. Once trained, this metamodel is explored to optimise the parameters of the fractional derivative model, thus allowing the design optimisation of viscoelastic properties. This approach could be extended to optimise the material properties of viscoelastic foams in the low frequency range.

Parameter identification of the linear discrete-time stochastic systems with unknown exogenous inputs

The paper addresses a parameter identification problem for linear discrete-time stochastic systems with unknown exogenous inputs. Such systems are considered when solving practical problems related to the measurements processing in the case when it is impossible to do any assumptions about the evolution of unknown input signal or its statistical characteristics that can change over time. We consider a class of discrete time linear stochastic systems with unknown exogenous inputs, where an additional source of a priori uncertainty of the system model is introduced, namely, the unknown parameter, on the elements of which the system model matrices can depend. This formulation of the parameter identification problem under the conditions of unknown inputs and the presence of random noises describes a high degree of uncertainty of a discrete time linear stochastic system. We propose a novel solution to this problem based on the construction of a new instrumental identification criterion. Minimization of this criterion allows for evaluating the unknown system model parameters simultaneously with the estimating of the state vector and unknown exogenous inputs of the system. Numerical experiments confirm the validity and efficiency of the proposed parameter identification method.

Parameter identification of hysteretic model of UHPC-connected precast segmental columns under cyclic loading

Due to their numerous advantages, precast segmental columns have emerged as an important development direction in bridge engineering. Understanding and researching their hysteretic characteristics holds great significance. Precast columns connected with UHPC achieve performance similar to cast-in-place (CIP) monolithic columns, making them suitable for use in constructing bridges located in high seismic regions. In this study, the Bouc-Wen-Baber-Noori (BWBN) model was employed to characterize the hysteretic behaviors of UHPC-connected precast segmental columns, considering strength degradation, stiffness degradation, and pinching effects. The Nelder-Mead simplex (NMS) algorithm was utilized to identify the parameters of the BWBN model, demonstrating its computational efficiency. The proposed parameter identification method was validated using experimental data derived from cyclic loading tests performed on five UHPC-connected precast segmental columns of three structural types. Comparative analyses encompassing force-displacement relationship, residual drift and energy consumption convincingly affirmed the applicability and precision of the proposed method for UHPC-connected precast segmental columns.

Operational modal identification of structures based on improved empirical wavelet transform

When empirical wavelet transform (EWT) is used to identify the modal parameters of civil engineering structures, the frequency band division is generally not accurate due to the noise effect on the Fourier spectrum. This phenomenon will lead to modal mixing and false modes in the analysis results. Therefore, this article establishes a signal frequency band division method by taking advantages of maximal spectrum technique and spectral skewness index. Combining the random decrement technique (RDT) and the least square method of single component modal parameter identification, an adaptive approach based on the improved EWT for operational modal parameter identification of civil engineering structures under stationary environmental excitations is proposed. The traditional frequency band division method based on EWT can not completely and effectively divide the meaningful frequency bands. While the proposed frequency band division technology based on the spectral skewness index can prevent the phenomenon of insufficient division and excessive division and realize adaptive frequency band division. The effectiveness of the proposed approach is validated by a numerical three-story frame and a field three-span concrete box girder bridge under ambient vibrations. The modal identification results from both numerical and experimental validations demonstrate that the proposed approach can effectively and accurately decompose the vibration responses and identify the structural modal parameters under operational conditions.

Hierarchical lattice stiffened cylindrical shell: Design, high-order vibration theory and composite parameter identification

A carbon fiber reinforced hierarchical lattice stiffened cylindrical shell (HLSCS) was designed and manufactured. Introducing effects of bending and coupling stiffness caused by neutral plane offsetting, an improved smeared stiffener method was proposed to homogenize the HLSCS to an anisotropic continuous cylindrical shell with varying-stiffness. The transverse shear stiffness was predicted by the higher-order shear deformation theory to build a high order vibration theory. Composite parameter identification method of the HLSCS was proposed based on the experimental and theoretical results to identify the composite parameters nondestructively. The method suggests discounted fiber volume fraction and stiffness for the completed HLSCS.