Parameter Identification Method Research Articles

Novel Parameter Identification Method of Extended Debye Model for Long-Length Submarine Cables

Novel Parameter Identification Method of Extended Debye Model for Long-Length Submarine Cables

Determination of the Gurson-Tvergaard-Needleman damage model parameters for simulating small punch tests of heat-resistant alloys

The small punch (SP) test is utilized to assess the mechanical characteristics and damage progression of heat-resistant alloys. The inverse finite element analysis method incorporating SP tests is a parameter identification method based on adjusting the accuracy of the simulated load-displacement curves. In this paper, the elastoplastic parameters of the Hollomon model and the damage parameters of the Gurson-Tvergaard-Needleman (GTN) model are determined based on the undamaged and damaged stages of the load-displacement curves, respectively. The whole stress-strain curves of the tested materials are then built using the results of finite element simulations of the tensile specimens of ZG15Cr2Mo1, P91, 316H, and Hastelloy X at room and elevated temperatures. Comparison with uniaxial tensile tests indicates that the simulated stress-strain curves closely resemble the experimental data from the tensile testing. In addition, the simulated damage evolution characteristics of the SP specimens are consistent with the mechanical model based on the actual deformation behavior. It is possible to comprehend the damage evolution process by analyzing the SP specimens’ stress and strain change characteristics.

Kinematic calibration of industrial robot using Bayesian modeling framework

Positioning accuracy of an end-effector is a crucial metric for evaluating industrial robot performance. Uncertainties in joint angles and joint backlash deviate actual angles from the designed nominal values to negate positioning accuracy. Most existing parameter identification methods overlook or not properly account for such uncertainties, leading to usually overconfident identification results. To this gap, the present study introduces a kinematic calibration methodology employing Bayesian parameter estimation to achieve identification of joint variables. New formulas based on data features of industrial robots for constructing the likelihood function are proposed, and model selection is applied to assess various likelihood functions for a tradeoff balance between complexity and accuracy. To evaluate the robustness of the proposed approach, identification of joint variables is conducted under different measurement noises. The position response of kinematic model is predicted based on the identified joint uncertainty information. The efficacy is verified through rigorous scrutiny involving both a numerical example and an engineering application. Results indicate that the proposed method exhibits satisfactory kinematic parameter identification accuracy and robustness. In addition, the uncertainty of parameters can be measured and the prediction of trajectory uncertainty intervals is realized simultaneously, which promotes the application of industrial robots in high-precision scenes.

Joint Model Parameter Identification and EKF Algorithm for the SOC Estimation of LFP Battery

Abstract Accurately estimating the state of charge (SOC) of batteries is crucial for achieving the safety and efficient driving of electric vehicles. To address the negative impact of voltage platform flatness and accumulated errors in current sampling, the SOC estimation method jointing model parameter identification and extended Kalman filter (EKF) algorithm is proposed and verified through simulation in this paper. Firstly, the parameter identification method is obtained based on the second-order dual polarization model, and effective identification of the parameters under different SOC is achieved using experimental conditions of hybrid pulse power characteristic and constant current discharge. On this basis, a function model with SOC as the independent variable and model parameters as the dependent variable is established by jointing model parameter identification and EKF algorithm, and the iterative estimation of SOC is achieved through the 1stopt and cftool methods. Finally, the SOC estimation accuracy of the proposed method is validated under three operating conditions that adopt the latest standards and are closer to the actual driving environment. The simulation results show that the SOC estimation method jointing model parameter identification and EKF algorithm has higher accuracy and smaller fluctuations than the traditional AH integration method, and the mean squared error (MSE) of estimation for the four test conditions are less than 0.29%, 0.72% and 0.25%, respectively.

Localization Method for Insulation Degradation Area of the Metro Rail-to-Ground Based on Monitor Information

Since rail-to-ground insulation decreases, large-level direct currents (DCs) leak from railways and form metro stray currents, corroding the buried metal. To locate the rail-to-ground insulation deterioration area, a location method is proposed based on parameter identification methods and the monitored information including the station rail potentials, currents at the traction power substations (TPSs), and train traction currents and train positions. According to the monitoring information of two adjacent TPSs, the section location model of the metro line is proposed, in which the rail-to-ground conductances of the test section are equivalent to the lumped parameters. Using the rail resistivity and traction currents as the known information, the rail-to-ground conductances are calculated with the least square method (LSM). The rail-to-ground insulation deterioration sections are identified by comparing the calculated conductances with thresholds determined by the standard requirements and section lengths. Then, according to the section location results, a detailed location model of the degradation section is proposed, considering the location distance accuracy. Using the genetic algorithm (GA) to calculate the rail-to-ground conductances, degradation positions are located by comparing the threshold calculated with the standard requirements and location distance accuracy. The location method is verified by comparing the calculation results under different degradation conditions. Moreover, the applications of the proposed method to different degradation lengths and different numbers of degradation sections are analyzed. The results show that the proposed method can locate rail-to-ground insulation deterioration areas.

Identification of fractional order time delay system with measurement noise using variable period integration operational matrix

This paper proposes a parameter identification method for fractional-order time-delay systems with measurement noise, based on the Legendre wavelet variable-period integration operational matrix. A variable-period integration operational matrix and a variable-period time-delay integration operational matrix are constructed by variable-period sampling principle. For the impulse noise, global Gaussian noise and local Gaussian noise present in the identification data, the variable-period sampling, operational matrix global coefficient decomposition and matrix local coefficient decomposition methods are used to reconstruct the variable-period integration operational matrix to separate the noise from the data; the fractional-order time-delay system is expressed as an integral equation by using the reconstructed variable-period operational matrix, this reduces the influence of measurement noise on system identification accuracy. To further improve the parameter identification accuracy, the integral equation is solved by the multi-innovation least squares algorithm. The proposed method’s effectiveness is verified through several simulations and an experimental study of a constant-temperature system.

Parameter identification of Johnson–Cook constitutive model based on genetic algorithm and simulation analysis for 304 stainless steel

Addressing the significant discrepancy between actual experimental cutting force and its predicted values derived from traditional constitutive model parameter identification methods, a reverse identification research of the Johnson–Cook (J-C) constitutive model for 304 stainless steel was conducted via genetic algorithm. Considering actual cutting zone and the influence of feed motion on the rake (flank) angle, an unequal division shear zone model was established to implement the theoretical calculation for shear zone stress. Through cutting experiments, the spindle speed was negatively correlated with the cutting force at first, and then became positively correlated; The empirical formula (EXP model) for turning force was corrected, revealing that the EXP model was unable to provide optimal predicted values for cutting force. The influence of the J-C constitutive parameter C on the cutting morphology was firstly investigated through simulation analysis, and determined an appropriate value for C, then obtained the precise values for the other four constitutive parameters by genetic algorithm. Moreover, the simulated values of cutting force in JC1 model (obtained from the Split Hopkinson Pressure Bar test) and JCM model (the improved model using genetic algorithm) were obtained by three-dimensional (3-D) simulation via FEM software. The results indicated that, the maximum error between actual experimental cutting force and its simulated values (by JCM model) was 14.8%, with an average error of 6.38%. These results outperformed the JC1 and EXP models, suggesting that the JCM model identified via genetic algorithm was more reliable.

Optimized array for powering sensors: Thermal and electromagnetic energy harvesting and fusion

Environmental energy harvesting technology has achieved great success in remote sensor power supply applications. However, existing technologies in the power grid neglect the potential of weak energy sources. This study aims to optimize the most reasonable array configurations and create hybrid energy fusion schemes for different weak energy environments. Taking thermal and electromagnetic energy harvesting from cable surfaces and currents in coaxial cables as an example, this paper introduces a genetic algorithm-based optimization method of harvester array configuration under multiple constraints and a dual-source energy fusion system. First, a vector impedance parameter identification method based on the equivalent circuit model of current transformer (CT) is proposed. Later, an optimization model for harvester array is established. Then, a genetic algorithm is used to optimize array combinations. And, a dual-source energy fusion system is proposed. Finally, thermal and electromagnetic energy harvesting and fusion are performed using 3 × 1 TEGs array and 6 × 1CT array. Under the specified conditions of a hot end temperature of 50 ℃ and an effective current value of 2 A, the power supply for the Zigbee wireless sensing system is realized to monitor the temperature of the cable core.

Dynamic Attitude Inertial Measurement Method for Typical Regions of Deck Deformation

Due to the deformation of ships, it becomes difficult to ensure the accuracy of attitude measurement in typical areas on the deck, which seriously impacts the safety and operational efficiency of shipborne equipment. To address this issue, this paper presents a parameter identification method for dynamic deformation models based on angle increment differences and introduces the related vector machine (RVM) algorithm for online estimation of dynamic deformation model parameters. In view of the truncation error and non-Gaussian noise of the model, this article proposes a dynamic attitude measurement method based on model predictive filtering (MPF), constructs a dynamic measurement model using Rodrigues parameters in an inertial frame, and designs a maximum correlation entropy (MCE) robust filter to achieve robust estimation of deck dynamic deformation. The performance of the method is verified through simulation analysis and shipborne experiments. The comparative results indicate that, compared with existing methods, the proposed improved deck dynamic attitude measurement algorithm based on model prediction (IDAM) can substantially enhance the accuracy of attitude measurement in the presence of deck dynamic deformations.

Estimation of temperature-dependent piezoelectric material parameters using ring-shaped specimens

Abstract Simulation-driven design processes of piezoelectric actuators and sensors necessitate precise material parameter knowledge. Historically, the IEEE standard on piezoelectricity is used for this purpose, which proposes an analytical method for material parameter identification based on resonance frequency evaluations. This method requires different impedance measurements on differently shaped and processed samples, which yields inconsistent material parameter sets due to processing effects on material properties. Previous studies show that an inverse procedure using a single disc-shaped specimen with some adaptations can be used to identify a full set of parameters. As high-power actuators often use ring-shaped components, this method needs adaptation for this kind of specimen. Furthermore, the dependence of the material parameters on temperature needs to be considered to characterise the temperature effects and the associated non-linear effects of the specimens. For this purpose, samples with different geometrical dimensions at a range of different temperatures are examined in this study. Material parameters are then estimated using an inverse approach utilising the measurement data in conjunction with a numerical FEM simulation model. The observed temperature dependence of the estimated material parameters is examined and presented in this contribution.

A Bayesian framework-based vehicle parameters identification method with unknown road excitation

A Bayesian framework-based vehicle parameters identification method with unknown road excitation

Continuous-discrete extended Kalman filter based parameter identification method for space robots in postcapture

Continuous-discrete extended Kalman filter based parameter identification method for space robots in postcapture

Investigation of axis-fitting-based measurement and identification techniques for kinematic parameters in multi-joint industrial robots

To improve the positioning accuracy of industrial robots and meet the requirements of industrial applications, this study begins with the structural design of multi-jointed industrial robots and proposes a kinematic calibration method based on axis fitting. The proposed method introduces an axis fitting technique based on circle fitting. Utilizing the results of the axis fitting, a method for establishing link coordinate systems by referencing the Modified Denavit-Hartenberg (MD-H) model is presented. Subsequently, a kinematic parameter identification method is proposed. This study primarily investigates the impact of joint rotation angles on the accuracy of axis fitting. The study reveals that when the rotation angle is greater than or equal to 20°, the circularity of the circle fitting is less than 0.008 mm, and the planarity is less than 0.01 mm, indicating that the proposed fitting algorithm meets the required precision for small angle rotations. Finally, the positioning accuracy of the target robot is verified according to ISO 9283:1998. After calibration, the positioning accuracy at point P1 improves from 1.64 mm to 0.46 mm, an enhancement of 71.95 %, which is the most significant improvement. At point P2, the positioning accuracy improves from 1.75 mm to 0.69 mm, an enhancement of 60.5 %, which is the least improvement. The advantage of this method lies in its ability to identify kinematic parameters that align with the actual structure of the robot using a simple measurement method, thereby improving the robot's positioning accuracy.

Dynamics Parameter Identification of Articulated Robot

Dynamics parameter identification in the establishment of a multiple degree-of-freedom (DOF) robot’s dynamics model poses significant challenges. This study employs a non-symbolic numerical method to establish a dynamics model based on the Newton–Euler formula and then derives a proper dynamics model through decoupling. Initially, a minimum inertial parameter set is acquired by using QR decomposition, with the inclusion of a friction model in the robot dynamics model. Subsequently, the least squares method is employed to solve for the minimum inertial parameters, forming the basis for a comprehensive robot dynamics parameter identification system. Then, after the optimization of the genetic algorithm, the Fourier series trajectory function is used to derive the trajectory function for parameter identification. Validation of the robot’s dynamics parameter identification is performed through simulation and experimentation on a 6-DOF robot, leading to a precise identification value of the robot’s inertial parameters. Furthermore, two methods are employed to verify the inertia parameters, with analysis of experimental errors demonstrating the effectiveness of the robot dynamics parameter identification method. Overall, the effectiveness of the entire calibration system is verified by experiments, which can provide valuable insights for practical engineering applications, and a complete and effective robot dynamics parameter identification scheme for a 6-DOF robot is established and improved.

A physics-informed neural network approach to parameter estimation of lithium-ion battery electrochemical model

The electrochemical models (EMs) have gained increasing attention for their ability to reflect the internal states of the lithium-ion battery, yet the parameter identification is still an essential and tricky task. This paper proposes a novel physical-informed neural network (PINN) framework for the parameter identification of the EM, where three neural networks are operated in parallel to search the suitable microscopic parameters. Compared with existing neural network-based methods, PINN can utilize the prior knowledge of battery such as the physical laws that govern the system. To reduce the dimension of searching space, a parameter categorization approach is designed where parameters can be identified based on their properties under different discharge conditions. Afterwards, the PINN is integrated with EM to achieve the model prediction and parameter identification. The validation results demonstrate the good performance of the proposed method in both parameter identification and model prediction under various scenarios.

Effective parameter identification of the GMS friction model for feed systems in CNC machines

One of the main factors influencing machine tool feed system tracking performance is friction. By creating an accurate friction model and implementing feed-forward compensation based on the model, the negative impacts of friction can be efficiently reduced. The generalized Maxwell-slip (GMS) model is commonly used to model feed system friction; however, simple and effective parameter identification methods are lacking. In this paper, a parameter identification method based on a metaheuristic Gaussian swarm optimization (GSO) algorithm is proposed. The method divides the parameters into two parts via a theoretical derivation, and employs GSO to identify each part successively. The proposed GSO is a novel metaheuristic algorithm inspired by the Gaussian probability function. The excellent performance of the GSO ensures that the friction parameters can be accurately and quickly identified. The results of the simulation and physical identification experiments show that the proposed GSO-based identification method can accurately identify the parameters of the GMS model with average and maximum relative errors of 3.96% and 14.05%, respectively. The identified model can accurately predict the friction of the feed system. Additionally, after friction compensation, the tracking error was decreased by an average of 78.9%.

Production Optimization of Premium Food Can with Distortion Printing under Waving Requirement

This research aims to propose a novel approach for evaluating and minimizing scraps in an industrial production of premium food cans with distortion printing. Beyond conventional formability criteria, a waving requirement is introduced to ensure aesthetic quality of the printed graphics. The research focuses on real production conditions, specifically involving double-cold-reduced (DR) low-carbon steel sheets and chromium-coated tin-free steel with a thickness of 0.16 mm. The sheets are laminated on both sides with a plastic film prior to undergoing distortion printing on the exterior. Subsequently, a blank is subjected to a drawing-redrawing process to form a food can. To address challenges associated with characterizing these thin sheets, a material parameter identification method is proposed and demonstrated. The thickness profile and flange length are identified as key criteria for this identification process. Measurements of thickness distribution and flange length are obtained using digital image correlation (DIC) and microscopy techniques. Within the manufacturing system, uncertainties related to material properties and forming processes can result in scraps or defects. To analyze these processes, finite element analysis (FEA) is employed and validated through experiments. For the evaluation of scrap rates, uncertainty propagation is conducted using a metamodeling technique, specifically employing radial basis function (RBF) neural networks. The study concludes by offering process optimization recommendations aimed at reducing the scrap rate.

Statistical blade tip timing measurement, Part II: High-order cumulant architecture

Blade tip timing (BTT) is a potential non-contact vibration measurement technique for rotating blades owing to its high efficiency and long service life. However, probe layout design and vibration parameter identification are critical challenges in BTT due to its inherent undersampling. To address this issue, we have proposed the concept of statistical BTT measurement which promotes efficient layouts and parameter identification methods by shifting the recovery target from the signal to itself to its statistics. In the first paper of series papers, we have developed covariance architecture for BTT measurement and signal post-processing. In this second part of series papers, we set out to develop BTT measurement from a higher-order statistic perspective. Specifically, we find that the 2qth-order cumulant retains the vibration information (frequency and amplitude) of the signal itself and it can be recovered at lower sampling rates than what is prescribed by the covariance architecture. On this basis, we propose a higher-order cumulant architecture for BTT measurement, which contains two aspects: higher-order difference layout (HODL) and higher-order cumulant-based parameter identifications. HODL is a special family of layouts that physically guarantee the availability of consecutive higher-order cumulant estimations and thus leads to a satisfactory parameter identification. To facilitate application, a series of concrete HODLs are provided for users, including a four-level nested layout and its enhanced version, and optimal fourth-order difference layout. The quantitative comparison in the simulations and experiments shows the effectiveness of the proposed higher-order cumulant architecture. Compared with the architectures developed from covariance and signal itself perspective, the proposed higher-order cumulant architecture is not only more robust to noise but also further reduces the number of required probes.

A Dynamic Iwan Model to Describe the Impact Failure of Bolted Joints

Due to the nonlinearity of the contact interface, as well as the material, jointed structures exhibit complex mechanical behaviors under impact loading. In order to accurately characterize the dynamic response of a joint, this work presents a nonlinear dynamic model (DICF model). First, the effects of loading velocity, preload and friction coefficient on the displacement–load curve are discussed based on a validated finite element model. Numerical simulation results show that the critical load and critical displacement are linearly related to the normalized logarithmic velocity and linearly related to the normalized preload and friction coefficient. Subsequently, a DICF model that consists of sliding, collision and failure is proposed. The constitutive relations of the model are derived, and dynamic correction functions are introduced to characterize the effects of velocity, preload and friction coefficient. A parameter identification method for the model is also provided. Finally, the DICF model is compared with the finite element simulation results, with an error of 0.43% for quasi-static conditions, a minimum error of 0.17% and a maximum error of −1.41% for impact conditions, in addition to significantly improved accuracy compared to the EC3 model, which indicates that it can effectively capture the behavior of bolted joints under impact loading conditions.

Parameter identification method of load modeling based on improved dung beetle optimizer algorithm

The role of load modeling in power systems is crucial for both operational and regulatory considerations. It is essential to develop an effective and reliable method for optimizing load modeling parameter identification. In this paper, the dung beetle algorithm is improved by using the good point set, and a load model parameter identification strategy based on the good point set dung beetle optimization algorithm (GDBO) within the framework of the measurement-based load modeling method. The proposed parameter identification strategy involves utilizing PMU voltage data as input, selecting a comprehensive load model, and refining the initialization process based on the good point set to mitigate the influence of local maxima. Through iterative optimization of the objective function using the Dung Beetle Optimizer (DBO) algorithm, the optimal parameters for the comprehensive load model are determined, enhancing the model’s ability to accurately capture the power curve. Analysis of examples pertaining to PMU-measured modeling parameter identification reveals that the proposed GDBO algorithm, which incorporates a good point set, outperforms alternative methods such as the improved differential evolution algorithm (IDE), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO), and conventional DBO algorithm. This demonstrates the superior performance of the introduced approach in the context of load model parameter identification.