Analysis of Coupled Vibration Characteristics of Linear-Angular and Parameter Identification

Study shake the phenomena in the electromechanical systems (EMS) are presented by permanent interest for the wide circle of researchers. The most effective source of vibrations in many cases is an electromagnetic causative vibration exciters (EMCV) in that there is transformation of electric power to energy of mechanical vibrations, that is related to the change of inductance. Electromagnetic vibration exciters have a number of significant advantages over other types of exciters. Due to the absence of rubbing parts they have high reliability and durability. They allow you to smoothly adjust the amplitude of vibrations over a wide range. EMCV are easy to integrate into production lines, they have easy maintenance, low operating costs and extensive automation capabilities. This phenomenon presents interest at development of electromechanics devices for intensive vibrotechnologies. Especially development guided on frequency of resonant EMCV promotes the economy of consumption of electric power and productivity of vibrotechnique. To the article the results of theoretical and experimental researches are driven on the increase of energy efficiency of EMCV and determination of output-input ratio. It is shown that losses of electric power in EMS with EMCV related to the origin of permanent constituents and ultra-harmonics of current, choice of magnetic chain and dynamic.

전자기 진동 가진기를 적용한 전기 집진기의 효과적인 탈진 여부를 판단할 수 있는 기준은 집진기내 집진판들의 진동 가속도이다. 이러한 진동 가속도는 외부로부터의 가진 주파수와 시스템의 고유주파수의 일치로 인하여 공진이 일어날 때 간헐적으로 최대치를 보이며 효율적인 탈진성능을 기대할 수 있다. 본 연구에서는 한대의 전자기 진동 가진기를 이용하는 단일 객체를 대상으로 한 관점에서 더 나아가, 실제 현장에서 복수의 전기집진탈진기가 설치됨을 고려하여 진동 탈진기의 설치방식에 따라 시스템을 모델링 하였다. 이러한 직렬 및 병렬회로로 연결된 가진기를 적용한 시스템을 대상으로 진동 가속도 계측 실험을 수행하여 직렬 및 병렬식 연결에 따른 진동 가속도의 차이를 비교함으로써 가진기의 배열 방식과 그에 따른 기대 탈진 성능 및 소비전력의 유효성을 검증하였다. 최종적으로 직렬 배열형 모듈이 증가할수록 선형적으로 필요전류량이 증가할 것으로 예상하였으나 급격한 감소나 (6.9% ~ 37.6%) 증가를 (5% ~ 45%) 보여주었다. 단독의 가진기가 사용된 병렬 배열보다, 2대의 가진기가 사용되고 그 전기회로의 연결이 병렬일 때 동일한 전류가 인가되었음에도 약 11.64% 의 가속도 감소 현상을 보였다. 2대의 가진기를 사용하고 입력전류의 값을 소비 전력의 관점에서 동일하게 보정하였을 경우 약 16.80 % 의 가속도 증가 현상을 확인하였다. One of the most significant requisite that should be considered for effective rapping of the electrostatic precipitator using electromagnetic vibration exciter is vibration acceleration and resonance frequency of collecting plates. This vibration acceleration shows its peak points when natural frequencies of the system are corresponded with excitation frequency from the power source, and effective rapping performance can be expected. In this research, extend view of single electrostatic precipitator using one electromagnetic vibration exciter, the system was remodeled by arrangement of the exciters in view of multiple modules of the electrostatic precipitator in fields. And vibration acceleration measurement experiment is performed and measured values are compared with these remodeled systems. By this experimental comparison in series and parallel arrangement, effectiveness of arrangement methods for the electromagnetic vibration exciter, expected rapping performance, and power consumption are verified.

Inertial Measurement Units (IMUs) using MEMS (Micro Electro Mechanical Systems) sensors, to detect rotation and acceleration, are extremely sensitive to vibration and shock. Because these IMUs are used in aircraft, missiles, and even in gun launched projectiles, the vibration and shock levels can be extremely high. We have been successful in designing isolation systems that protect the MEMS sensors from these harsh environments. These isolation systems attenuate the shock and vibration in frequency ranges that would otherwise degrade IMU performance and even damage the sensors. In designing an isolation system, for an IMU with MEMS devices, the designer must first acquire a good definition of the shock and vibration environment to which the IMU will be exposed. Then the designer must determine the frequencies and levels to which the MEMS devices are sensitive. Finally the designer must have knowledge of IMU properties such as resonant frequencies, mass properties, clock frequencies, etc. By knowing all of this information, and how it affects IMU performance, the designer can determine the proper parameters for the isolation system. A well designed isolation system using these parameters will be capable of protecting the MEMS devices while maintaining IMU performance through out all specified environments.

Real‐Time System Identification: An Algorithm for Simultaneous Model Class Selection and Parametric Identification

In the ultra-tight integration, the signal processing of GNSS receiver baseband can be enhanced by the aiding of INS information. With the purpose of improving the performance of ultra-tight integration with low-cost MEMS (Micro-Electro-Mechanical System) IMU (Inertial Measurement Unit), the paper explores a deeply coupled integration scheme of GNSS and the software defined IMU (SDI) based on vibratory MEMS rate gyroscope. The error of vibratory MEMS rate gyroscope demodulated by peak detection technique is interpreted in the gyroscope signal domain and the gyroscope demodulation parameter deviations are estimated by the integration filter and adjusted accordingly. The presented integration scheme is tested by an integrated navigation system which consists of a software defined GNSS receiver, a single-axis vibratory MEMS gyroscope GI-CVG-N2100A based SDI and an Xsens MEMS IMU. The single-axis vibratory MEMS gyroscope GI-CVG-N2100A in the test, is employed to replace one of the gyroscope axes of the Xsens MEMS IMU.

Damage identification methods based on structural modal parameters are influenced by the structure form, number of measuring sensors and noise, resulting in insufficient modal data and low damage identification accuracy. The additional virtual mass method introduced in this study is based on the virtual deformation method for deriving the frequency-domain response equation of the virtual structure and identify its mode to expand the modal information of the original structure. Based on the initial condition assumption that the structural damage was sparse, the damage identification method based on sparsity with l1 and l2 norm of the damage-factor variation and the orthogonal matching pursuit (OMP) method based on the l0 norm were introduced. According to the characteristics of the additional virtual mass method, an improved OMP method (IOMP) was developed to improve the localization of optimal solution determined using the OMP method and the damage substructure selection process, analyze the damage in the entire structure globally, and improve damage identification accuracy. The accuracy and robustness of each damage identification method for multi-damage scenario were analyzed and verified through simulation and experiment.

This paper deals with the identification of material parameters in constitutive models of cyclic plasticity for sheet metals using the bending moment vs. curvature diagrams obtained by cyclic bending tests. After a brief discussion on the identification for some simple plasticity models, a new identification method based on the iterative multipoint approximation concept is presented for complicated elastic-plastic consititutive models incorporating many numbers of material parameters. As an example, eight material parameters in Chaboche-Rosselier's model were identified by this method. This approach has been verified by comparing the simulated stress-strain curve using the constitutive model incorporating the identified material parameters with the experimental curve determined by uniaxial tension.

Parameter identification in models of hydrologic systems using the digital simulation language PDEL

Parameter identification of reduced-order electrochemical model simplified by spectral methods and state estimation based on square-root cubature Kalman filter

This letter highlights some issues which were overlooked in a recently published paper called maximum likelihood identification of inertial sensor noise model parameters. The latter paper does not consider existing alternative methods, which specifically tackle this issue in a possibly more direct manner and, although remaining a generally valid proposal, does not appear to improve on the earlier proposals. Finally, a simulation study rectifies the poor results of an estimator of reference in the same publication.

The NewSpace landscape for microlaunchers is highly competitive. The cost is one of the main drivers in this competition. Several microlauncher companies have decided to outsource the GNC sub-system to external providers, procuring low-cost commercial-of-the-shelf solutions. Lately, Astos has been involved in several activities related with GNC for (micro-)launchers. Astos’ work has been focused on providing GNC development tools and flexible and easily reconfigurable GNC software that can be applied to a wide range of launch vehicles. Navigation is one of the main drivers for launcher orbital injection accuracy and it is also one of the main drivers for cost in the GNC sub-system. Usually, this cost is driven by the Inertial Navigation System (INS) or Inertial Measurement Unit (IMU), in which expensive technologies are employed such as ring-laser gyroscopes. In traditional inertial propagation algorithms, errors are accumulated due to integration resulting in a steady divergence of the position and attitude estimate. Therefore, the performance of the navigation mainly depends on the performance of the IMU. This makes an investment into a highly accurate inertial sensors, especially the gyroscope, necessary to meet the navigation requirements. Lately, the use of hybrid navigation solutions that fuse the inertial measurements with Global Navigation Satellite Systems (GNSS) measurements has diminished, in some degree, the reliance of the navigation algorithm on the IMU performance. Although this is true for translational states, as position and velocity, the same cannot be said with the same degree of validity for attitude determination and, more specifically, for roll angle estimation. One possible way to reduce the cost of inertial sensors is by using microelectromechanical systems (MEMS). The advantages of using this technology, apart from the large reduction in cost, are the significant saves in mass and power required to operate these sensors. The disadvantage is clearly the lower performance, especially in high-vibration environments such as encountered in launcher vehicles. Lately, efforts to improve the performance of MEMS navigation solutions were driven by the demand of industries such as the wearables and unmanned aerial systems. This led to the development of solutions that utilize multiple low-cost inertial measurement units. By that, the hardware cost is not gravely increased in exchange for higher navigation accuracy, reliability, and redundancy. In this paper, a navigation algorithm is proposed that incorporates the measurements from multiple low-grade MEMS IMUs. The algorithm implements parallel Extended Kalman Filters (EKF), one for each IMU, combining the inertial measurements with the position, velocity, and time solutions provided by a single GNSS receiver. The algorithm has a loosely coupled architecture and the inertial propagation is regularly reset using the fused estimates of the vehicle’s state and IMU biases (closed-loop implementation). The navigation solution, providing estimates of position, velocity, and attitude, is then a combination of the individual EKF results through weighted means based on the covariance of the individual estimates. This formulation utilizing parallel filters is called in the literature as Federated Kalman Filter architecture. An analysis will be presented relating the performance of the navigation architecture with the number of sensors used and their distribution on the launch vehicle’s body. The assessment is performed with relation to a baseline navigation architecture also implementing a loosely coupled, closed-loop, hybrid structure but employing a single low-grade IMU. The analysis will determine the most promising sensor constellation, based on navigation performance, cost and implementation complexity. The selected multiple IMU solution is then compared with two different navigation architectures: Strapdown inertial propagation utilizing a single high-grade IMU; and hybrid navigation scheme implementing a loosely coupled, closed-loop architecture with a medium grade IMU. The performance evaluation is conducted utilizing simulated measurements retrieved from high-fidelity 6 degrees-of-freedom non-linear simulator of a representative microlauncher scenario. The simulators’ dynamics, kinematics, environment, and sensor modelling are provided by the ASTOS tool. Astos intends to further study the feasibility and performance of the proposed Inertial/GNSS hybrid navigation architecture with multiple IMUs. In the roadmap for raising the algorithm’s technology readiness level, more advanced simulations are foreseen, with improved sensor error modelling and previously unmodelled dynamical effects such as flexibility and sloshing. Additionally, processor- and hardware-in-the-loop tests with real sensor data are planned.

Smart sensors are the core of condition monitoring of power equipment. However, energy supply for sensors in complex electric power field is difficult and in the spotlight. Solar energy as an easily available energy is a good solution to this problem. Accurate identification of photovoltaic cell model parameters can ensure the subsequent stable energy supply. The purpose of this paper is to realize the accurate identification of photovoltaic cell model parameters, so as to serve the energy supply of monitor devices. Firstly, the basic circuit of photovoltaic panel energy supply is built. Secondly, the U-I characteristic curve of photovoltaic cell under different loads is measured. Thirdly, the equivalent parameter model of photovoltaic cell is constructed. Finally, the model parameters are accurately identified based on Levenberg Marquarelt (LM)-Genetic Algorithm (GA). LM algorithm is used for fast global calculation to determine the approximate range of global optimal solution, and then GA is used to further iterate within this range to obtain high-precision local extremum. The proposed method achieved a better fitting effect and results has higher precision in parameter identification of photovoltaic cell. Moreover, under the rated working condition, the errors between the identified parameters and the numerical values provided by the manufacturer are within ±5%. This paper presents an optimization algorithm combining LM and GA, which can accurately identify the parameters of photovoltaic model under different solar radiation levels, and provide strong technical support for the energy supply of sensors and other monitoring equipment.

A procedure for the experimental identification of the material standard linear solid model parameters by means of dynamic mechanical analysis test instrument measurements is presented. Since the standard linear solid material stress–strain functional D(ω) relationship in the frequency domain formally depends on the standard linear solid material parameters, a procedure able to identify these parameters from test measurement estimates is proposed in this work. Nevertheless, a critical, nonlinear and non-parametric approach is to be followed since the number of the material standard linear solid block components is generally unknown, and the material D(ω) shows a highly nonlinear dependency on the unknown standard linear solid material parameters. For these reasons, measurement and test model noise is expected to strongly influence the accuracy of the identification results. A multi-step procedure is presented, consisting first in the non-parametric identification of a frequency dependent, two degrees of freedom model instrument frame by means of a polynomial rational function, where polynomial order and parameters, such as polynomial coefficients and pole-residue couples, are optimally identified by means of an algebraic numerical technique and of an iterative stabilization procedure. Another procedure able to identify the material D(ω) polynomial rational functional relationship in the frequency domain is also proposed, taking into account the dynamic contribution of the instrument frame, of the inertial contribution of the distributed mass of the beam and of the lumped mass of the instrument force measuring system. An effective procedure, able to identify the standard linear solid material model parameters in the time domain from the identified material physical poles, is finally proposed. Some application examples, concerning the identification of the standard linear solid model of a known material and of an unknown composite material, are shown and discussed as well.

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.

Reversibility and irreversibility in the packing of vibrated granular material