Parameter Identification Research Articles

Automated method for structural modal identification based on multivariate variational mode decomposition and its applications in damage characteristics of subway tunnels

This paper introduces a fully automated modal identification algorithm based on the Multivariate Variational Mode Decomposition (MVMD) of free vibration responses to determine structural modal parameters. Addressing the challenge of setting MVMD parameters, we introduce a fusion parameter combining power spectral cross-entropy with reconstruction error as an adaptive fitness function in the optimization algorithm, enabling optimal parameter selection. Then, modal frequencies, damping ratios, and shapes of structures can be extracted from autonomously decomposed Intrinsic Mode Functions by employing the principle of modal superposition and least squares fitting without manual parameter adjustments. Validated by a four-degree-of-freedom numerical model, the method demonstrated accurate, automatic modal parameter identification. The method was further applied to a subway tunnel structure model experiment. Comprehensive modal identification was conducted on tunnel structures under varying degrees of damage. The results validate the proposed method’s effectiveness and reveal the damaged segment structure’s multimodal parameter variation patterns and surrounding soil.

Waste-Heat Renewable Gasifier Design through Taguchi's Method and MANFIS

Ensuring a global average temperature increase of no more than 1.5°C is crucial for aligning with the objectives of the Paris Agreement. The Intergovernmental Panel on Climate Change (IPCC) estimates that a 45% reduction in carbon dioxide emissions by 2030 or a 25% reduction by the same year is necessary to cap the temperature rise at 2°C. Failure to achieve this target by 2030 would necessitate substantial subsequent reductions to compensate for the delayed progress toward net zero emissions, potentially incurring higher costs. Elevating the focus on enhancing energy efficiency has emerged as a key strategy in expediting progress toward these climate goals. Hydrogen, renowned for its high efficiency and emission-free combustion, is anticipated to play a pivotal role in this endeavor. Traditionally, gasifiers have served as the conduit for transforming inexpensive fuels like coal or biomass into hydrogen. However, their energy-intensive nature poses a challenge.Addressing this inherent issue, an innovative approach known as the external-heating gasifier has been proposed and designed. The design of the external-heating gasifier represents a quintessential nonlinear and multiple-input multiple-output (MIMO) problem. To achieve optimal performance, the integration of Taguchi's method and inverse-model technology has been employed, with a simulation platform developed using the COMSOL Multiphysics software.Initially, Taguchi's method was utilized to determine the minimum number of experiments required for a full-factorial design and identify the most critical factors influencing product performance. Subsequently, an adaptive neuro-fuzzy inference system (ANFIS) was developed with a MIMO–ANFIS architecture. This system was employed to train an inverse model, facilitating the mapping of relationships between each input and output set. In essence, this approach enables the identification of manufacturing parameters that result in optimal performance. To illustrate the efficacy of the proposed method, a simulation study on an existing gasifier is presented.

Identification of the Asymmetric Transmission Error and Gear Mesh Dynamic Parameters using Full-Spectrum Responses in a Geared-Rotor System

A dominant source of vibration in geared-rotor systems are the gear mesh fault parameters. They include the asymmetric transmission error, phases of transmission error, the gear mesh stiffness, the gear mesh damping and the gear runouts. The present work deals with the experimental identification of aforementioned parameters. A mathematical model of geared-rotor system has been developed using Lagrangian dynamics. Equations of motion are transformed into frequency domain using the full-spectrum response analysis. These transformed equations are used to develop an identification algorithm based on least-squares fit to estimate the transmission error and gear mesh dynamic parameters. The system identification algorithm is initially verified using numerical simulations. The robustness of the algorithm is checked by introducing white Gaussian noise in the simulated responses. A geared-rotor experimental rig was developed and used to measure responses at gear locations in two orthogonal directions. Measured responses are transformed in frequency domain using the full-spectrum analysis and used in the present novel identification algorithm to identify the gear parameters. The identified parameters are validated by comparing the numerically generated full-spectrum response using experimentally estimated parameters and that from the experimental rig. Conflict of Interest Statement The authors declare no conflicts of interest.

POE-Based Error Modeling and Multiple Plane Constraint-Based Parameter Identification for the Kinematic Calibration of a 4-UPS/SPR Parallel External Fixator

Limb deformity is a common complaint in orthopedic surgery. Currently, gradual treatment using parallel external fixator (PEF) has become the preferred option for deformity correction. As the key medical apparatus with the special application properties of temporary assembly and direct utilization, the geometric errors generated during the manufacturing and assembly process of the PEF contribute to the post-correction residual and iatrogenic deformities. However, considering the complexity of clinical scenarios, there is a lack of an applicable solution to reduce the effect of the above error source of PEF. To overcome these problems, taking a 4-UPS/SPR PEF as the research object, this paper proposes a novel kinematic calibration method. First, the error model with the features of completeness, continuity, and minimality is first established based on the product of exponentials (POE) formula. Then, the identifiability analysis is conducted to eliminate the redundant error parameters and ensure the stability and accuracy of the iterative calculation in the process of parameter identification. Furthermore, a multiple plane constraint (MPC)-based parameter identification approach is introduced to the calibration task of the PEF to effectively reduce the difficulty of the pose information measurement in the current cost-effective constraint calibration method. Finally, the simulation and prototype experiments validate that the proposed calibration method (i.e., the combination of the POE-based error model and the MPC-based parameter identification approach) can accomplish the calibration tasks in clinical applications and meet the cost-effectiveness and efficiency requirements.

An enhanced indirect modal identification procedure for bridges based on the dynamic response of moving vehicles

Indirect dynamic structural identification, referred to as Vehicle Scanning Method (VSM), pertains to the estimation of bridge modal parameters by using data recorded from sensors directly mounted on a moving vehicle. This procedure has recently gained increasing attention due to its low cost and simple implementation. Nevertheless, the non-stationary and complex nature of the vehicle–bridge interaction phenomenon poses some limitations to the applicability of the method, especially in terms of accuracy. In this regard, in this paper, an enhanced procedure for the dynamic identification of bridges modal parameters based on the VSM is introduced, taking into account the effects of vehicle/bridge damping and road pavement roughness. Specifically, based on the Variational Mode Decomposition (VMD) method, the relevant Intrinsic Mode Functions (IMFs) and corresponding modal frequencies are determined from the recorded signal. Further, the Natural Excitation Technique (NExT) is adopted in conjunction with a noise-robust area ratio-based approach, for modal damping ratios estimation. Finally, mode shapes are evaluated by properly correcting the instantaneous amplitudes of each IMF considering the influence of the corresponding estimated modal damping ratio. Several numerical simulations are presented to show the validity of the proposed procedure and comparisons with the classical Hilbert Spectrum-based identification approach are employed to assess its reliability and improved accuracy.

Implementation of inverse method for identification of process parameters in a laser heating process

Changes in process parameters can alter the temperature data of a solid body during the laser heating process. This paper proposes an inverse method to determine the laser heating parameters, namely, sheet width, sheet thickness, laser power, and scan speed of the work specimen for the given temperature response which is assigned by the user. The method uses an analytical tool on moving heat source as a forward model capable of real-time temperature prediction of the sheet under the laser heating process. The effectiveness of the present forward model that incorporated the temperature-dependent material properties is tested by experimental findings performed on Al 6061-T6 sheets. Next, an iterative search process based on heuristic method is carried out for satisfying a prescribed temperature response by the optimization of unknown parameters. To illustrate the implementation and assess the practicality of the inverse method, two examples based on laser heating applications are used. The method proposed herein recover the unknowns for the prescribed temperature response after a few iterations, provides an efficient way to optimize the laser heating process. Additionally, the effect of measurement error on the findings of the inverse problem is addressed.

Technical note: Exploring parameter and meteorological uncertainty via emulation in volcanic ash atmospheric dispersion modelling

Abstract. ​​​​​​​Consideration of uncertainty in volcanic ash cloud forecasts is increasingly of interest, with an industry goal to provide probabilistic forecasts alongside deterministic forecasts. Simulations of volcanic clouds via dispersion modelling are subject to a number of uncertainties relating to the eruption itself (mass of ash emitted and when), parameterisations of physical processes, and the meteorological conditions. To fully explore these uncertainties through atmospheric dispersion model simulations alone may be expensive, and instead, an emulator can be used to increase understanding of uncertainties in the model inputs and outputs, going beyond combinations of source, physical, and meteorological inputs that were simulated by the dispersion model. We emulate the NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model for simulations of the Raikoke 2019 eruption and use these emulators to compare simulated ash clouds to observations derived from satellites, constraining NAME source and internal parameters via history matching. We demonstrate that the effect of varying both meteorological scenarios and model parameters can be captured in this way with accurate emulation and using only a small number of runs per meteorological scenario. We show that accounting for meteorological uncertainty simultaneously with other uncertainties may lead to the identification of different sensitive model parameters and may lead to less constrained source and internal NAME parameters; however, through idealised experiments, we argue that this is a reasonable result and is properly accounting for all sources of uncertainty in the model inputs.

A Building Point Cloud Extraction Algorithm in Complex Scenes

Buildings are significant components of digital cities, and their precise extraction is essential for the three-dimensional modeling of cities. However, it is difficult to accurately extract building features effectively in complex scenes, especially where trees and buildings are tightly adhered. This paper proposes a highly accurate building point cloud extraction method based solely on the geometric information of points in two stages. The coarsely extracted building point cloud in the first stage is iteratively refined with the help of mask polygons and the region growing algorithm in the second stage. To enhance accuracy, this paper combines the Alpha Shape algorithm with the neighborhood expansion method to generate mask polygons, which help fill in missing boundary points caused by the region growing algorithm. In addition, this paper performs mask extraction on the original points rather than non-ground points to solve the problem of incorrect identification of facade points near the ground using the cloth simulation filtering algorithm. The proposed method has shown excellent extraction accuracy on the Urban-LiDAR and Vaihingen datasets. Specifically, the proposed method outperforms the PointNet network by 20.73% in precision for roof extraction of the Vaihingen dataset and achieves comparable performance with the state-of-the-art HDL-JME-GGO network. Additionally, the proposed method demonstrated high accuracy in extracting building points, even in scenes where buildings were closely adjacent to trees.

Linear Identification Problems for Equations with the Dzhrbashyan–Nersesyan Derivative

Linear Identification Problems for Equations with the Dzhrbashyan–Nersesyan Derivative

Identifying the plane position of the receiver coil in a multi‐transmitter IPT system through the identification of parameters

AbstractThe study introduces a method for identifying the location of receiver coils within a multi‐transmitter IPT system that utilizes shared transmitter coils. In contrast to the conventional receiver coil position recognition methods, this approach eliminates the need for additional detection coils and position sensors. Identifying the location of the receiver coil in a multi‐transmitter IPT system is achieved through the concurrent optimization of the transmitter coil and its electrical characteristics. The study presents the design of coil grouping and control mechanisms for a 3×3 multi‐transmitter. A mathematical model has been developed for the mutual inductance between receiver and transmitter coils on the X–Y plane. Adaptive cooperative particle swarm optimizer (ACPSO) facilitates the identification of mutual inductance and load parameters in a multi‐transmitter IPT system, positioning the receiver coil in a 2D plane using genetic particle swarm optimization (GAPSO) and a mutual inductance mathematical model. The discrepancy in accuracy between the receiver coil's test coordinates and the absolute coordinates remains below 3.5%.

An adaptive backstepping control strategy based on radial basis function neural networks for the magnetorheological semi-active suspension

Owing to nonlinear issues such as external disturbances and uncertain parameters within the semi-active suspension system (SASS), the vibration amplitude of the suspension system tends to increase, and the time required for the suspension system to reach a steady-state response is prolonged. Hence, this paper proposes an adaptive backstepping control strategy based on radial basis function neural networks (RBF-NNs). Firstly, the damping force characteristics of the magnetorheological (MR) damper are tested, and the experimental data are utilized for parameters identification and fitting of the Bouc-Wen model. To establish a connection between the controller and the forward model of the MR damper, the forward model of the MR damper, the inverse model of the MR damper, and the model of the MR-SASS are constructed. Secondly, the backstepping controller and the adaptive backstepping controller based on RBF-NNs are designed. The stability and reliability of the closed-loop suspension system are verified through stability analysis using Lyapunov function. Finally, the dynamic characteristics of the passive control, backstepping control, and adaptive backstepping control strategies based on RBF-NNs applied to MR-SASS are analyzed under B-Class road excitation and speed bump road excitation. The acceleration, suspension dynamic deflection, and tire dynamic load are selected as the evaluation indices. The results demonstrate that the adaptive backstepping controller based on RBF-NNs significantly enhances the ride comfort of the SASS.

On the experimental determination and prediction of damage evolution in composites via cyclic testing

This study introduces a novel approach to obtaining and predicting damage evolution laws using quasi-static cyclic testing within the framework of continuum damage mechanics. To achieve this, a comprehensive set of characterization and parameter identification tests was performed. Carbon-epoxy specimens were manufactured using the filament-winding technique, and these laminates were tested using a universal testing machine. Digital image correlation was employed in all experiments to capture strain fields, and an alternative method utilizing the combined loading compression device is presented to obtain mechanical properties. Once the material was characterized, cyclic tests were conducted, including [Formula: see text], [Formula: see text], and [Formula: see text] tensile tests, and shear v-notch tests. These aim to determine damage evolution laws for both conditions, pure and coupled stress states. From these tests, values for damage onset and threshold were obtained and used to define four new parameters. These parameters permit the estimation of degradation relationships for any arbitrary orientation without requiring additional tests. This approach was tested as the proof-of-concept within the positive range of transverse tension and in-plane shear stress domain. The obtained results are promising, justifying its extension to other failure mechanisms. While acknowledging its limitations, this new approach holds potential for implementation in the analysis of progressive failure in fiber-reinforced composite materials, possibly in conjunction with established failure criteria and computational tools, mainly in the finite element method domain.

Dimension-Reduction Representation of Multivariate Mainshock-Aftershock Ground Motions Based on Measured Records

ABSTRACT This study proposes an integrated dimension-reduction representation strategy for simulating multivariate mainshock-aftershock stochastic ground motions based on measured motion records. The strategy includes a parameter identification method for modeling evolutionary power spectrum density (EPSD) of mainshock-aftershocks, a coherence function describing the spectrum correlation between mainshock and aftershock, and the EPSD function matrix establishment for mainshock-aftershock field combining with complex spatial coherence function. Furthermore, the dimension-reduction representation for simulating the ground motions considering the sequence and spatial variability simultaneously is presented with merely two elementary random variables. Numerical results effectively demonstrate the accuracy, efficiency, and engineering applicability of the proposed method.

Asymptotically efficient Quasi-Newton type identification with quantized observations under bounded persistent excitations

This paper is concerned with the optimal identification problem of dynamical systems in which only quantized output observations are available under the assumption of fixed thresholds and bounded persistent excitations. Based on a time-varying projection, a weighted Quasi-Newton type projection (WQNP) algorithm is proposed. With some mild conditions on the weight coefficients, the algorithm is proved to be mean square and almost surely convergent, and the convergence rate can be the reciprocal of the number of observations, which is the same order as the optimal estimate under accurate measurements. Furthermore, inspired by the structure of the Cramér–Rao lower bound, an information-based identification (IBID) algorithm is constructed with an adaptive design about weight coefficients of the WQNP algorithm, where the weight coefficients are related to the parameter estimates which leads to the essential difficulty of algorithm analysis. Beyond the convergence properties, this paper demonstrates that the IBID algorithm tends asymptotically to the Cramér–Rao lower bound, and hence is asymptotically efficient. A numerical example is simulated to show the effectiveness of the proposed algorithms.

Using active cooling/heating for 1C1R grey-box model parameter identification in actual environment: A proof-of-concept study

A model predictive control (MPC) has been important method for managing both comfort and energy saving in heating, ventilation, and air conditioning (HVAC) problems. However, recent data-driven models for MPC, relying on extensive training data, pose challenges for practical implementation in real-world thermal environments due to their time-consuming nature. The grey-box approach, utilizing a simple RC model as a thermal environment proxy, is well known for its data efficiency. Nevertheless, numerical instability arising from the multi-collinearity of observed data can degrade the model’s time efficiency. To deal with this issue, we propose and implement a rapid and stable parameter identification method that integrates active cooling-heating within the actual thermal environment. Our method requires only a single day of data to estimate basic RC model parameters, allowing for immediate use of the RC model in MPC as soon as an air conditioner is installed. Practical Application Using a grey-box model for MPC in HVAC systems is efficient, but the multicollinearity problem of observed data undermines its time efficiency. Our method deals with this challenge by enabling immediate model parameter identification with just 1 day’s data, incorporating active cooling-heating. We demonstrate it in an actual thermal environment. The high level of time and data efficiency afforded by our method is particularly beneficial for IoT applications like malfunction detection and adaptive room reparameterization associated with room rearrangement.

Detection and Analysis of Abnormal High-Current Discharge of Cylindrical Lithium-Ion Battery Based on Acoustic Characteristics Research

The improvement of battery management systems (BMSs) requires the incorporation of advanced battery status detection technologies to facilitate early warnings of abnormal conditions. In this study, acoustic data from batteries under two discharge rates, 0.5 C and 3 C, were collected using a specially designed battery acoustic test system. By analyzing selected acoustic parameters in the time domain, the acoustic signals exhibited noticeable differences with the change in discharge current, highlighting the potential of acoustic signals for current anomaly detection. In the frequency domain analysis, distinct variations in the frequency domain parameters of the acoustic response signal were observed at different discharge currents. The identification of acoustic characteristic parameters demonstrates a robust capability to detect short-term high-current discharges, which reflects the sensitivity of the battery’s internal structure to varying operational stresses. Acoustic emission (AE) technology, coupled with electrode measurements, effectively tracks unusually high discharge currents. The acoustic signals show a clear correlation with discharge currents, indicating that selecting key acoustic parameters can reveal the battery structure’s response to high currents. This approach could serve as a crucial diagnostic tool for identifying battery abnormalities.

Parameter identification of Yoshida–Uemori combined hardening model by using a variable step size firefly algorithm

Abstract The material behavior under cyclic loading is more complex than under monotonic loading and the usage of the sophisticated constitutive models is required to accurately define the elastoplastic behaviors of the advanced high-strength steels and aluminum alloys. These models involve the numerous material parameters that are determined from cyclic tests and accurate calibration of the variables has a great influence on the description of the material response. Therefore, the development of a precise and robust identification method is needed to obtain reliable results. In this study, a systematic methodology depending upon the firefly algorithm (FA) with variable step size has been developed and Yoshida–Uemori combined hardening model parameters of a dual-phase steel (DP980) and an aluminum alloy (AA6XXX-T4) are determined. The identified parameters are verified based on comparisons between the finite element simulations of the cyclic uniaxial tension-compression tests and experimental data and also the search performance of the variable FA is evaluated by comparing it with the standard FA. It is seen from these comparisons that variable FA can easily find and rapidly converge to the global optimum solutions.

Kalman Filter Using a Third-Order Tensorial Decomposition of the Impulse Response

For system identification problems associated with long-length impulse responses, the recently developed decomposition-based technique that relies on a third-order tensor (TOT) framework represents a reliable choice. It is based on a combination of three shorter filters, which merge their estimates in tandem with the Kronecker product. In this way, the global impulse response is modeled in a more efficient manner, with a significantly reduced parameter space (i.e., fewer coefficients). In this paper, we further develop a Kalman filter based on the TOT decomposition method. As compared to the recently designed recursive least-squares (RLS) counterpart, the proposed Kalman filter achieves superior performance in terms of the main criteria (e.g., tracking and accuracy). In addition, it significantly outperforms the conventional Kalman filter, while also having a lower computational complexity. Simulation results obtained in the context of echo cancellation support the theoretical framework and the related advantages.

Dynamic modeling and energy demand analysis of chain heavy load transfer system on marine mobile platform

ABSTRACT The demand for transferring the heavy load on marine mobile platform is steadily increasing, surpassing the capabilities of the current system. This paper introduces a chain heavy load transfer system and develops its dynamic model. This paper studies the sensitivity laws of the system’s energy demand to unknown parameters and proposes an adaptive mutation particle swarm optimization (AMPSO), whose identification accuracy is verified through comparative analysis. Experimental verification confirms the reliability of the system model while studying the influence of environmental loads on the system’s energy demand. The results demonstrate that the chain transfer system meets the requirements of marine mobile platforms, and the system model incorporates the primary influencing factors. Unknown parameters significantly influence the system’s energy demand, and the displacement error after parameter identification using AMPSO is 0.00056 rad, substantially enhancing the accuracy of model calculations. The model calculations of the system closely align with experimental measurements, with a maximum displacement error not exceeding 0.004 rad. Sway exerts a greater influence on the system’s energy demand compared to tilt. When tilt and sway are superimposed, the friction torque between the load and the track results in increased input torque and power for the vertical arrangement of the system, with a maximum torque of 328.6kN·m and maximum power of 400 kW.

Sensorless fuzzy control algorithm for permanent magnet synchronous motor based on particle swarm optimization parameter identification and harmonic extraction

ABSTRACT In sensorless control of permanent magnet synchronous motor (IPMSM) with sliding mode observer, the problem of parameter robustness and poor stability seriously affects the control effect of sensorless operation. Therefore, a fuzzy sliding mode observer (SMO) phaselocked loop (PLL) combining particle swarm optimization (PSO) parameter identification and recursive least squares adaptive linear (RLS-Adaline) harmonic extractor is proposed. First, the fuzzy controller is used to process the control parameters of the sliding mode observer and the phaselocked loop. Secondly, the RLS-Adaline harmonic extractor is used to effectively filter the higher harmonic component of the back electromotive force (EMF), thus significantly improving the sensorless control effect. Thirdly, the PSO algorithm is used to identify the parameters of the motor, and the effect of parameter identification is judged by the performance index. The experimental results show that the proposed control method can effectively reduce the speed error and rotor position error.