Dynamic Identification Method Research Articles

A support vector regression (SVR)-based method for dynamic load identification using heterogeneous responses under interval uncertainties

A support vector regression (SVR)-based method is presented in this paper, which aims to reconstruct the uncertain dynamic load using heterogeneous responses. Essentially, the technology of load identification can be regarded as an issue of nonlinear regression. Considering the incomplete limitation of the single response signal, the complicated relationship between the dynamic load and heterogeneous responses for a specific structure may be determined through some support vectors in simulated training samples based on the SVR algorithm. In view of multi-source uncertainties, an interval dimension-by-dimension (D–D) method is investigated, which can approximate the load changing concerning the each-dimensional uncertain parameter by Legendre orthogonal polynomials. Then, the dynamic load boundaries can be calculated through the maxima/minima of the approximation polynomial in the time history. The validity and specificity of the proposed methodology are clarified by 3 numerical examples. In addition, the results indicate that the proposed method can be utilized to identify the interval of dynamic load with outstanding accuracy and efficiency.

Dominant responses of bridge abutments

Under seismic conditions a bridge abutment can transfer significant inertial effects to the superstructure due to its interaction with a large volume of soil. These effects can lead to a marked dynamic amplification of the abutment response that should be accounted for in bridge design. In order to quantify these effects, we present analytical solutions for the modal characteristics of the soil-abutment system at small strains, expressed in terms of the vibration periods and mass participation factors for the approach embankment considering different directions of motion. The effect of nonlinear soil behaviour is also investigated through nonlinear numerical analyses of the soil-abutment system in OpenSees, which establish the dependence of the vibration periods on the intensity of the external perturbation. The numerical results were used as a benchmark to validate the analytical predictions at small strains; they can also provide some indication on the applicability of the analytical formulation at medium and large strain levels. The proposed method of dynamic identification can be used as a tool to evaluate the dynamic characteristics of bridge abutments, which can be in turn regarded as the inertial features of macroelement representations for abutments in the structural analysis of bridges.

Bp-ant colony algorithm: escape the Louvre

With the spread of terrorism in the new era, France has suffered many terrorist attacks in recent years. The new situation calls for a review of emergency evacuation plans for popular destinations. Taking the Louvre Museum as the research object, this paper discusses the emergency evacuation plan in detail. At the same time, in order to make effective planning, this study selects the day with the largest number of visitors to the Louvre Museum as the sample. In the establishment of emergency evacuation model, ant colony algorithm is introduced. The training time of two kinds of traffic evacuation algorithm is given, and BP neural network is used for training. In addition, the dynamic Bottleneck Identification Method Based on fuzzy set theory is used in this study. Through model optimization, the possible bottleneck location is accurately determined. Finally, this paper analyzes the advantages and disadvantages of the model, and gives the specific evacuation routes.

Nonlinear dynamical system identification using the sparse regression and separable least squares methods

This paper proposes a novel nonlinear dynamical system identification method based on the sparse regression algorithm and the separable least squares method. To effectively avoid solving the second derivative of the displacement signal and reduce the effect of noise, the Duhamel's integral is adopted to represent the dynamic relationship between the system input and output. In the expression form of Duhamel's integral, nonlinear dynamical system identification can be cast as a separable least squares problem. Thus, the separable least squares method is leveraged to separately identify the parameters of the linear subsystem and the coefficients corresponding to nonlinearities among the nonlinear dynamical system. During the identification process of nonlinear restoring forces, one complete set of nonlinear basis functions are used to represent the nonlinear restoring forces. Not all the candidate nonlinear terms are contributing, however, thus the sparse regression algorithm is adopted to select the actual contributing nonlinear components in the candidate nonlinear terms and eliminate the non-contributing nonlinear components, and then the corresponding parameters of contributing nonlinear components are estimated by the unbiased least squares method. Finally, one RKHS (Reproducing Kernel Hilbert Space)-based non-parametric de-noise method is further proposed to reduce the noise in the vibration displacement and obtain the noise-reduced velocity from the displacement signal. The numerical simulation about the identification of the rotating blade-casing system and the dynamic experiment of the HSLDS (high-static-low-dynamic stiffness) isolator system verify the effectiveness of the new identification method for nonlinear dynamical systems proposed in this paper.

Deep regression adaptation networks with model-based transfer learning for dynamic load identification in the frequency domain

Frequency-domain dynamic load identification methods based on neural network (NN) models construct models independently at each frequency, but are inaccurate and inefficient to train. To address these problems, a deep regression adaptation network (DRAN) with model-transfer learning is proposed for identifying dynamic loads in the frequency domain. The aim is to take advantage of the similarity of uncorrelated multi-source dynamic loads and multi-vibration response at adjacent frequencies. First, a DRAN model for load identification is established using the historical data for a specific frequency. Second, the trained DRAN parameters are transferred to the DRAN for the target frequency as the initial parameter values. Next, the transferred DRAN is fine-tuned with the historical data of the target frequency to obtain the load identification model of the target frequency. Finally, the trained DRAN parameters of the current target frequency are transferred to the next target frequency. This process is iterated until a DRAN model for all frequencies is established. Because a frequency response function is a continuous function varying with frequency, the relationships between the dynamic loads and response at adjacent frequencies are similar. DRAN can adapt the historical data of different frequencies to one neural network for training, and then extract the common feature information of different frequencies to improve the accuracy of the model. Moreover, instead of setting the initial weights randomly and training them independently for each DRAN model, model-transfer learning is used to obtain better initial weights from the trained weights of DRAN models of adjacent frequencies. The proposed method was evaluated on the experimental data of a cylindrical shell structure under acoustic vibration joint excitation. The results show that the proposed method can obtain better initial weights, higher accuracy, better noise robustness, and shorter training time than a neural network.

An Equivalent Identification Method for Dynamic Loads Acting on Nonlinear Structures

An efficient dynamic load identification method for nonlinear structures is proposed. Assuming that the nonlinear elements of a structure can be separated, or the structural kinetic equation can be available, the whole structural damping and stiffness matrices can be divided into linear and nonlinear sub matrices. Regarding the effects of the nonlinear elements on the linear ones as dynamic load constraints, which can be realized by moving the product of the nonlinear sub matrices and their corresponding responses to the right-hand side of the original kinetic equation, a remaining linear structure subjected to multi-source of dynamic loads can then be obtained. The instability of the obtained structure caused by the load equivalence is discussed and improved. The case that the characteristics of the nonlinear parameters are unknown is also investigated. Finally, based on the improved structure, Green’s kernel function method, LSQR regularization is employed to reconstruct the dynamic loads. The effectiveness of the proposed method is proved by two numerical examples and an engineering application.

Research on the dynamic identification method of weak parts in cantilever structures.

Determining the weak parts of a structure is one of the key issues in the field of machine tool stiffness improvement. However, studies show that overcoming the static deformation with acquisition difficulty is a complex problem in practical structures. This study considers the machine tool cantilever structure, as a cantilever beam and bar structure, where the objective is to propose a weakness index, to identify the weak part, using system reconstruction to extract the measured static deformation data and the fitting data. Stiffness reduction is used to simulate weak parts, while the effectiveness of the method is evaluated, in the case of various weakness values and of different noise levels, using the finite element simulation approach. The validity of the proposed method is illustrated through comparison of the theoretical results to the experimental ones, using the cantilever structure of a test machine tool. The research content provides some means of improving the machining accuracy of machine tools.

On-line monitoring of minor oil spills in natural waters using sediment microbial fuel cell sensors equipped with vertical floating cathodes

Oil spills near natural water bodies pose considerable threats to aquatic ecosystem and drinking water system. Various detection techniques have been developed to identify the oil pollution in natural waters. These techniques mainly focus on large and major oil spills involving significant changes in environmental characteristics. However, monitoring of minor oil spills (from seepage and dripping) in waters remains a bottleneck, allowing inconspicuous and persistent oil contamination. To overcome this drawback, a sediment microbial fuel cell (SMFC) sensor equipped with a vertical floating cathode is developed for on-line and in-situ monitoring of minor oil spills in natural waters. The vertical floating cathode was intended for recognizing oil on water surface. Oil on the cathode will trigger current drop. Two kinds of natural sediments were adopted in two sensors (SMFC1 from a lake and SMFC2 from an urban stream) for comparison. Both showed linear relationship between net steady-state current decrease and oil dose (30.78 and 27.29 μA/mL of sensitivity, respectively). The current change process was fitted well to a pseudo-first order kinetic equation. A one-point/two-point dynamic identification methods were derived from the kinetic equation. Therefore, the detection time was shortened from 10 h to 10/30 min. The triggered current decrease was mainly attributed to the increase in internal resistance related to charge and mass transfer. Despite the power loss after oil contamination, results implied SMFC sensor could still achieve self-sustainability. This study shows that the SMFC sensor with vertical floating cathodes is applicable to monitoring the unnoticeable minor oil pollutions in natural waters.

Establishment and Development of Socio-Legal Work in the Children Primary Care in USSR

Formative experience of establishment and development of legal support for maternity and childhood in USSR is considered over time and systematized. The role of measures taken by the socio-legal cabinet, as well as social patronage as the method of dynamic preventive identification of families with children who are in difficult circumstances. The author's vision on the accumulated experience significance in solving modern problems of protecting the child health and rights is presented.

An improved parameter identification method of redundant manipulator

To achieve more accurate simulation and control in the use of the manipulator, it is necessary to establish an accurate dynamic model of the redundant manipulator. The research of this article focuses on the dynamic parameter identification method of the redundant manipulator. In the study, the spinor theory is applied to the Newton–Euler dynamic equation, the Coulomb + viscous friction model is adopted, and the minimum parameter set is obtained by linearization derivation. The parameter identification of the manipulator is realized using the method of offline identification of the measured current, and the coefficient of the excitation trajectory is optimized using the nonlinear optimization function. Finally, the parameter set with high accuracy is obtained, and the motion trajectory of each joint can be obtained. The scheme has high accuracy and can meet the needs of practical application. To verify the accuracy and reliability of this method, we have carried out experiments on a service robot “Walker” and obtained the desired results.

Dynamic Parameter Identification of a Pointing Mechanism Considering the Joint Clearance

The clearance of the revolute joint influences the accuracy of dynamic parameter identification. In order to address this problem, a method for dynamic parameter identification of an X–Y pointing mechanism while considering the clearance of the revolute joint is proposed in this paper. Firstly, the nonlinear dynamic model of the pointing mechanism was established based on a modified contact model, which took the effect of the asperity of contact surface on joint clearance into consideration. Secondly, with the aim of achieving the anti-interference incentive trajectory, the trajectory was optimized according to the condition number of the observation matrix and the driving functions of activate joints that could be obtained. Thirdly, dynamic simulation was conducted through Adams software, and clearance was involved in the simulation model. Finally, the dynamic parameter identification of the pointing mechanism was conducted based on an artificial bee colony (ABC) algorithm. The identification result that considered joint clearance was compared with that which did not consider joint clearance. The results showed that the accuracy of the dynamic parameter identification was improved when the clearance was taken into consideration. This study provides a theoretical basis for the improvement of dynamic parameter identification accuracy.

A new iteration regularization method for dynamic load identification of stochastic structures

In this paper, we propose a novel fast convergence iterative regularization method on the basis of a new iterative regularization operator to solve the dynamic load identification of stochastic structures. Firstly, the validity and convergence of the proposed algorithm is proved by strict mathematical theory and numerical results of engineering example. Secondly, the dynamic load identification of uncertain structures can be transformed into a series of deterministic inverse problems by the matrix perturbation method. Finally, we also assess the statistical characteristics of identified loads using the statistical theory. Particularly, the proposed method successfully solves the dynamic load identification of stochastic structure. Numerical simulations are given to validate the feasibility of the presented method in this paper.

Dynamic parameters identification for sliding joints of surface grinder based on deep neural network modeling

Dynamic parameters of joints are indispensable factors affecting performance of machine tools. In order to obtain the stiffness and damping of sliding joints between the working platform and the machine tool body of the surface grinder, a new method of dynamic parameters identification is proposed that based on deep neural network (DNN) modeling. Firstly, the DNN model of dynamic parameters for working platform-machine tool body sliding joints is established by taking the stiffness and damping parameters as the input and the natural frequencies as the output. Secondly, the number of hidden layers in DNN topology is optimally selected in order to the optimal training results. Thirdly, combining the predicted results by DNN model with experimental results by modal test, the stiffness and damping are identified via cuckoo search algorithm. Finally, the relative error between the predicted and experimental results is less than 2.2%, which achieves extremely high prediction precision; and thereby indicates the feasibility and effectiveness of the proposed method.

Study on the dynamic identification method of the weak part of the bar-shaped combined structure

The weak part of the stiffness of machine tool combined structure is the key to improve the stiffness of machine tool. To overcome the static deformation with difficulty acquisition, the paper chooses machine tool combined structure which can be equivalent to one-dimensional bar structure, and a weakness index (WI) is proposed to identify the weak part of the stiffness by means of the dynamic hammer test method. Based on the bar structure as a numerical example, the weak parts are modeled as EA reduction in stiffness while the mass is maintained at a constant value. Thorough finite element (FE) method simulations are performed to assess the robustness and limitations of the method in several scenarios with single and multiple weakness. On the crossbeam of gantry type machine tool, the sensors are used to collect vibration data, the structural modal parameters are obtained by singular value decomposition (SVD) technique, and the dynamic characteristics are systematically reconstructed by using modal state space method to obtain stiffness data at zero-frequency. Then, the weak part of the structural stiffness is identified by the weakness index. Finally, the comparison of FE simulations and experiment results are provided to illustrate the working of the method.

Low Velocity Impact Energy Monitoring and Recognition of Composite Laminates with Variable Thickness Based on Optical Fiber Sensor Network

At present, most of the research on low velocity impact of composite laminates focuses on load location and damage assessment. To provide further early warnings about structural impact damage, impact energy can be monitored and identified. For high strength composite laminates with variable thickness, in order to further accurately evaluate the impact energy, it is necessary to adopt more suitable dynamic load signal analysis and impact energy identification methods. Therefore, a new low velocity impact monitoring and identification method for composite plates with variable thickness is proposed. All impact sample signals collected by optical fiber sensor network are decomposed by whitening Empirical Mode Decomposition (EMD); the energy feature set is established according to the impact energy eigenvalue of sample signal; according to the first order component of signal decomposition, the thickness coefficient is determined and the energy feature set is modified to evaluate the actual impact energy. Meanwhile, combined with optical fiber sensing and signal processing technology, an impact energy monitoring system has been established, and the low velocity impact monitoring and identification experiments of composite laminates with variable thickness were carried out. The proposed energy identification method successfully identified 1–3 J impact energy with an average error of 4.82%, and the average error of large thickness area with low sensitivity was significantly reduced from 13.25% to 5.67%. The results show that the thickness coefficient correction method based on whitening EMD can evaluate the low velocity impact energy more accurately, and the thickness coefficient correction step significantly improves the recognition performance.

Review of Bridge Structural Health Monitoring Based on GNSS: From Displacement Monitoring to Dynamic Characteristic Identification

Deformation monitoring and dynamic characteristic analysis of bridge structures are the vital and basic requirements for the safe operation of bridges. In recent years, Global Navigation Satellite System (GNSS) has become increasingly widely used in bridge structural health monitoring with the development of the GNSS technology, especially the continuous improvement and development of China's Beidou navigation satellite system (BDS). This article summarizes the application process of GNSS dynamic deformation monitoring and the development of GNSS deformation measurement technology of bridge structural health monitoring, the dynamic characteristic identification method and its application in bridge GNSS monitoring. The positioning solution methods for GNSS monitoring, the high sampling rate GNSS receiver for monitoring, multi-frequency and multi-system GNSS monitoring and the weakening of multipath effect of GNSS monitoring are summarized in detail. Then, the conclusions and prospects are posed for future research and related application.

Human-Exoskeleton Coupling Dynamics of a Multi-Mode Therapeutic Exoskeleton for Upper Limb Rehabilitation Training

The purpose of this study is to establish the human-exoskeleton coupling (HEC) dynamic model of the upper limb exoskeleton, overcome the difficulties of dynamic modeling caused by the differences of individual and disease conditions and the complexity of musculoskeletal system, to achieve early intervention and optimal assistance for stroke patients. This paper proposes a method of HEC dynamics modeling, and analyzes the HEC dynamics in the patient-active training (PAT) and patient-passive training (PPT) mode, and designs a step-by-step dynamic parameter identification method suitable for the PAT and PPT modes. Comparing the HEC torques obtained by the dynamic model with the real torques measured by torque sensors, the root mean square error (RMSE) can be kept within 13% in both PAT and PPT modes. A calibration experiment was intended to further verify the accuracy of dynamic parameter identification. The theoretical torque of the load calculated by the dynamic model, is compared with the difference calculated by parameter identification. The trends and peaks of the two curves are similar, and there are also errors caused by experimental measurements. Furthermore, this paper proposes a prediction model of the patient’s height and weight and HEC dynamics parameters in the PPT mode. The RMSE of the elbow and shoulder joints of the prediction model is 9.5% and 13.3%. The proposed HEC dynamic model is helpful to provide different training effects in the PAT and PPT mode and optimal training and assistance for stroke patients.

Quantum Operator Model for Data Analysis and Forecast

A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates for compressing the size of stored data while retaining the resolution of information. Quantum vectors are introduced as the basis of a linear space for defining a Dynamic Quantum Operator (DQO) model of the system defined by its data stream. The transport of the quantum of compressed data is modeled between the time interval bins during the movement of the sliding time window. The DQO model is identified from the samples of the real-time flow of data over the sliding time window. A least-square-fit identification method is used for evaluating the parameters of the quantum operator model, utilizing the repeated use of the sampled data through a number of time steps. The method is tested to analyze, and forward-predict air temperature variations accessed from weather data as well as methane concentration variations obtained from measurements of an operating mine. The results show efficient forward prediction capabilities, surpassing those using neural networks and other methods for the same task.

Dynamic Load Identification Method of Strongly Coupled Structures

Dynamic Load Identification Method of Strongly Coupled Structures

Research on dynamic parameter identification method of shallow reservoir based on kalman filter

Research on dynamic parameter identification method of shallow reservoir based on kalman filter