Modal Identification Method Research Articles

Key Failure Mode Identification Method of LED System Based on Fuzzy TOPSIS

The premise of the system reliability analysis is to effectively discern the patterns of product failures at all levels. To deal with the problem that the diversity of failure modes of complex products represented by LED systems cannot be analyzed comprehensively, in this paper, a key failure mode identification method based on Fuzzy TOPSIS is proposed. First, the construction method of the failure relevance tree is given to support the comprehensive identification of potential failure modes. Then, the decision rule of node failure weight is mined by the rough set method, the systematic evaluation of the node failure weight is realized with the TOPSIS method, and the key failure modes are identified. Finally, a case validation of the proposed method in this paper is performed.

Evaluation of CNN algorithm at locomotion mode identification for a knee-assisted exoskeleton

In this study, a knee-assisted exoskeleton that can assist the wearer's knee joints at the appropriate phase of the gait cycle is designed. A convolutional neural network (CNN) model is used in the gait control to identify the locomotion modes of the exoskeleton. In order to verify the CNN-based locomotion mode identification method, an offline CNN model was created in MATLAB. The experiment was conducted and seven healthy subjects’ hip, knee, and ankle joint angles were captured and six common locomotion modes were recognized in the experiments. The recorded data were segmented with overlapping sliding windows of varying sizes and were tested using a stratified 5-fold cross-validation technique. The experimental findings showed that the appropriate window size was 1.25T (T stands for the normal adult gait cycle). The lowest and the highest accuracies of the test sets at the window size of 1.25T were 96.47% ± 0.83% and 99.67% ± 0.49% on different subjects. It also showed that the model has strong generalization. Thus, the algorithm had proved useful to extract the characteristics of lower limb joint angles in various locomotion modes and the approach in this paper can be extended to the recognition of motion patterns in lower limb exoskeletons.

Modal Identification of 38 Supertall Buildings and Establishment of Predictive Models

Information on natural frequencies and damping ratios is essential for structural dynamic analysis. To develop predictive models of modal parameters applicable for modern high-rise buildings with increasing heights, an advanced interferometric radar system was employed to measure the displacement responses of 38 supertall buildings with heights ranging from 200 to 600 m under ambient excitations. Then, a combined approach of the stochastic subspace method and an uncertainty bound estimation method are adopted for modal identification, and its accuracy is validated by a numerical simulation study. By applying the modal identification method to the field measurements, modal parameters of the 38 supertall buildings are identified. Based on these results, predictive models for fundamental natural frequency and damping ratio of supertall buildings are accordingly proposed, which are more applicable for the wind-resistant serviceability assessment or design of high-rise buildings with heights over 200 m than existing empirical models.

Lamb mode and damage identification using small-sample dictionary algorithm

ABSTRACT In this paper, Lamb mode identification method based on small-sample dictionary algorithm is proposed and applied for the separation of specific Lamb modes, the reconstruction of Lamb waves upon propagating a certain distance and damage identification. This approach includes the creation of small-sample dictionary and querying procession in a dictionary. Firstly, Lamb wave signals upon propagating at a series of distances are simulated, and signal features, {mode, distance, time of flight (Tof), wavelet energy}, are extracted to create a dictionary; secondly, Tof of the received signal is extracted, and then Lamb modes are identified by searching the dictionary; finally, energy parameters are estimated to reconstruct wavepackets. The feasibility of this algorithm is validated in AAA laminate, and the results are presented. In a 2D-simulation model of a pitch-catch configuration, A0 and S0 modes can be identified and reconstructed effectively when the direct waves and the reflected waves are synchronously received, with the propagation distance of 0.3 m and 0.5 m, respectively. In addition, a Lamb-wave-based delamination location is conducted in three-dimensional AAA laminate. The experimental results show that the delamination can be located relatively by combining the identified damage-scattered S0 waves and the probability-based diagnostic imaging.

An improved stochastic subspace modal identification method considering uncertainty quantification

This paper develops a novel stochastic subspace modal identification method in which the uncertainty quantification of structural modal parameters is considered. On the basis of a 4-degree-of-freedom (4DOF) simulation model, the detailed procedure of the proposed approach is first demonstrated. Then, by conducting a series of numerical simulations on the 4DOF model, the accuracy and effectiveness of the proposed method are verified for the cases of structural responses containing high-level noise and non-stationary properties. Furthermore, the proposed approach is applied to field measurements on a 260-m-high supertall building under ambient excitations, and the estimated modal parameters are further compared with the results determined by the forced vibration test performed on the building. The good agreement between these two sets of estimated modal parameters verifies the applicability and effectiveness of the developed modal identification method for field measurements. The objective of this study is to provide an efficient instrument for the accurate modal identification of civil structures.

A waveform-based clustering and machine learning method for damage mode identification in CFRP laminates

To gain an insight into the damage mechanism in carbon fiber reinforced polymer, a real-time analytical approach for damage mode identification of composite based on machine learning and acoustic emission is proposed. Firstly, waveform features are extracted from the acoustic emission signals with low information entropy through wavelet packet transform, where the high-dimensional feature vectors represent the main features of the reconstructed signals in the frequency domain. Combined with the autoencoder and k-means ++ algorithm, a waveform-based clustering model is constructed to reveal the relevance between acoustic emission signals and damage modes. Finally, the damage mode recognition of different types of composite laminates is achieved by the developed softmax layer classifier. The identification and the quantitative analysis of damage modes for prefabricated defects specimens demonstrate the robustness of the method. The method is effective and feasible for real-time monitoring of the damage evolution process of carbon fiber reinforced composite components.

Morlet-wave-based modal identification in the time domain

This research focuses on the time-domain identification of modal parameters using impact response excitation from signals with a relatively small dynamic range and high noise contamination (e.g., from high-speed cameras). The information required to identify the modal parameters is limited and is contained mostly at the beginning of the signal. In order to perform an identification from such a response, the following challenges have to be overcome: a good frequency-domain separation (for close modes), a good localization in the time domain and an over-determination (to reduce uncertainty). To overcome these challenges this research introduces the Morlet-wave modal identification method as an extension of the Morlet-wave damping identification method, which has already proven capable of identifying the damping of short signals. Here, the method is extended to the modal parameters and an over-determination approach is proposed to reduce the uncertainty. The method identifies each mode shape separately from 10 to a maximum of 400 oscillations and at damping levels from 0.02% to 2% with a strong presence of noise in the signal. The method is tested on an experimental example and the results are compared to the classical modal identification methodology.

Structural Modal Parameter Identification Method Based on the Delayed Transfer Rate Function under Periodic Excitations

The dynamic response transfer rate function (TRF) is increasingly used in the field of structural modal parameter identification because it does not depend on the white noise assumption of the excitation. In this paper, the interference of periodic excitation on structural modal parameter identification using TRF is analyzed theoretically for a class of civil engineering structures with obvious periodic components in excitation, and then an identification method of structural real modal parameters is proposed. First, a delayed TRF is constructed, and the pseudo-frequency response function is further obtained to identify the periodic spurious poles of the whole system. Then, the effective identification of the real modal parameters of the structure is achieved by comparing the system poles identified via conventional TRF. Finally, the feasibility and robustness of the proposed method were verified using a calculation example with four-degrees-of-freedom system. In addition, the modal parameters of a structure under periodic excitation were effectively identified by taking a pumping station as an example, and the results show that the method accurately identified the structural modal parameters when the excitation contained periodic components, which has wider prospects for technical applications.

Energy consumption mode identification and monitoring method of process industry system under unstable working conditions

Process industry systems under unstable working conditions are prone to potential anomalies, deviating from the original transition trajectory, and taking longer than expected to return to stability due to persistent disturbances from uncertainties and experience-based regulation errors. The energy waste caused by this situation has not received sufficient attention, and cannot be addressed by existing energy consumption monitoring methods. Herein, an energy consumption mode (ECM) identification and monitoring method under unstable working conditions is proposed, consisting of ECM identification model and multi-mode dynamic monitoring model, focusing on the variation rules of the correlation between energy consumption and other states of the system. In the ECM identification stage, the ECM correlation parameters that reflect the comprehensive production information are selected. Then, given the transfer characteristics of ECM, a Hidden Semi-Markov Model (HSMM) is constructed to fit the migration between modes and the duration within modes. The Variational Bayesian Gaussian Mixture Model is introduced to improve the HSMM, which solves the problem of lacking prior knowledge of ECM and achieves the automatic classification and online identification of ECM. In the dynamic monitoring stage of multi-ECMs, a series of dynamic kernel principle component analysis models are established, and the corresponding monitoring thresholds are set for each ECM. By calculating the maximum of the posteriori probability and the mode thresholds, the ECMs under unstable conditions can be accurately identified and automatically monitored. Compared with previous methods, the proposed method reduces the false detection rate and missed detection rate of abnormal ECM identification to 1.04% and 1.31% in the actual slag grinding production process, which proves its effectiveness.

Modal Parameter Identification of Civil Engineering Structure Based on XGBoost Algorithm

In order to prevent the structural problems of buildings and related civil engineering from becoming potential risks, the modal parameter identification method of civil engineering structures is studied based on XGBoost algorithm. First of all, it is necessary to design and extract the modal parameters of civil engineering structures, and then detect civil engineering structures according to the modal parameter identification model, identify the existing problems of the structures, discuss and analyze them, verify the effect of the detection method of civil engineering structures based on modal parameter identification, and make a comparative analysis with the traditional modal parameter identification methods such as SSI. By analyzing the basic principle and algorithm of XGBoost algorithm, this study analyzes the research characteristics and present situation of the method, identifies the implementation and feasibility of modal parameters of civil engineering structures, and provides theoretical basis and data reference for the development of related professions and related industries.

Identificação dinâmica de uma passarela estaiada utilizando um sistema de aquisição de dados de baixo custo

Abstract This study describes the dynamic identification of a cable-stayed footbridge using an alternative data acquisition system and output-only modal identification methods. Data were collected during dynamic tests performed using an acquisition system based on the Arduino platform, consisting of low-cost devices with on-board Micro-Electro-Mechanical System (MEMS) accelerometers. The Peak-Picking (PP) method was used for the stay cables. The Frequency Domain Decomposition (FDD) and Stochastic Subspace Identification - Unweighted Principal Components (SSI-UPC) methods were used for the complete structure. The proposed data acquisition system efficiently recorded the time series required for Operational Modal Analysis and the acceleration acquisition process provided stable results. At least four mode shapes were identified in all tests. A minimum of four high energy peaks in the 0 - 24 Hz frequency range of the spectrum were obtained by the acquisition system in all of the cable tests and selected by the method. In the complete structure tests, the low-cost data acquisition system and the identification methods provided the first four flexural mode shapes, within the 0 - 9 Hz frequency range. Results for the frequency domain method showed a maximum difference of 2.37% in the first experimental frequency when compared to a 3D finite element numerical model, while in the other frequencies the difference was between 1 and 2%. For the time domain method, the maximum difference was 1.74% in the fourth frequency, with differences of between 0.1 and 0.7% in the other frequencies. The mode shapes were evaluated using the Modal Assurance Criterion (MAC) index, and the results varied between 0.8513 and 0.9990.

Research on Breeze Vibration Law and Modal Identification Method of Conductor Considering Anti-Vibration Hammer Damage

Research on Breeze Vibration Law and Modal Identification Method of Conductor Considering Anti-Vibration Hammer Damage

Robust Decomposition of Kernel Function-Based Nonlinear Robust Multimode Process Monitoring

With the rapid development of modern industry, actual production processes generally have a variety of complex characteristics including nonlinearity, multimodality, and contamination. Those characteristics, as well as the faults, bring great challenges to traditional process monitoring. To deal with all the above-mentioned three problems simultaneously, this paper develops a robust nonlinear multimode process monitoring scheme. First, the robust decomposition of kernel function (RDKF) algorithm is proposed to detect outliers. Then, a nonlinear mode identification method is presented by combining block diagonal kernel function matrix and spectral clustering. For the online sample, a mode indicator is derived from kernel function to judge whether it belongs to a fault or a certain mode. Finally, the effectiveness of the proposed method is validated by two cases in terms of both mode identification and fault detection.

Thermo-rheological reduced order models for non-Newtonian fluid flows with power-law viscosity via the modal identification method

In the framework of melted polymer flows characterization, this study deals with the formulation, construction and validation of thermo-rheological Reduced Order Models (ROMs) for incompressible flows of pseudoplastic fluids. The dynamic viscosity is described by a shear rate power law defined by consistency index K and pseudoplastic index n. The flow dynamics are assumed to be quasi-static whereas the thermal state is unsteady. Viscous dissipation acts as a heat source term in the energy equation. ROMs are built through the Modal Identification Method (MIM). First, their general form is derived from governing local conservation equations and the viscosity power-law, with an original approach to handle the issues related to the pseudoplastic index n. Then ROMs are identified using Particle Swarm Optimization and Ordinary Least Squares, from simulations coming from a reference Full Order Model (FOM). The approach is applied to a polymer flow in an annular duct, corresponding to an experimental lab apparatus. ROMs allow computing temperature at chosen locations of interest whatever the applied inputs, here parameters K, n and inlet flowrate as well as a time-varying volumetric heat source power in the central axis. Compared to the reference Finite Elements FOM, computing time is considerably reduced with limited loss of accuracy.

Modal Identification of Low-Frequency Oscillations in Power Systems Based on Improved Variational Modal Decomposition and Sparse Time-Domain Method

Power systems have an increasing demand for operational condition monitoring and safety control aspects. Low-frequency oscillation mode identification is one of the keys to maintain the safe and stable operation of power systems. To address the problems of low accuracy and poor anti-interference of the current low-frequency oscillation mode identification method for power systems, a low-frequency oscillation mode feature identification method combining the adaptive variational modal decomposition and sparse time-domain method is proposed. Firstly, the grey wolf optimization algorithm (GWO) is used to find the optimal number of eigenmodes and penalty factor parameters of the variational modal decomposition (VMD). And the improved method (GWVMD) is used to decompose the measured signal with low-frequency oscillations and then reconstruct the signal to achieve a noise reduction. Next, the processed signal is used as a new input for the identification of the oscillation modes and their parameters using the sparse time-domain method (STD). Finally, the effectiveness of the method is verified by the actual low-frequency oscillation signal identification in the Hengshan power plant and numerical signal simulation experiments. The results show that the proposed method outperforms the conventional methods such as Prony, ITD, and HHT in terms of modal discrimination. Meanwhile, the overall reduction in the frequency error is 34, 44, and 21%, and the overall reduction in the damping error is 37, 41, and 18%, compared with the recently proposed methods such as the EFEMD-HT, RDT-ERA, and TLS-ESPRIT. The effectiveness of the methods in suppressing the modal confusion and noise immunity is demonstrated.

Stochastic propeller force and moment reconstruction at a shaft end based on an improved Kalman filter

Measuring the unsteady hydrodynamic excitation of a marine propeller at a shaft end accurately from multiple directions is crucial for minimizing the unwanted vibration and noise of ships. Due to the synchronous loading of high static thrust and torque, an innovative piezoelectric multicomponent balance embedded into a hollow shaft end is designed. However, complex coupling between the balance and shaft structure is formed. This leads to crosstalk disturbance and a modal dynamic amplification effect on all measuring channels. To overcome this, an improved Kalman filter algorithm based on the subspace modal identification (SMI) method is established, thus allowing us to reconstruct and locate equivalent unsteady excitations accurately in multiple directions with response-only networks. The effectiveness of proposed framework is test on a simulated nonrotating shaft end system with twelve force and three acceleration response channels. The selection of the smoothing delay parameter and whether crosstalk is considered are two main factors that influence the reconstruction accuracy.

The effect of constituent units on the vibration reduction of bamboo engineering materials: The synergistic vibration reduction mechanism of bamboo stiffness and wood damping

Determination of the influence and mechanism of constituent units on the vibration reduction performance of bamboo engineering materials is required to optimize application of these materials in the fields of construction and transportation. Testing was performed of typical bamboo engineering materials of Bamboo scrimber (BS), bamboo-wood composites (BWC), and Bamboo Glued laminated timber (GLT), all with different constituent units. Vibration reduction performance and parameters of vibration models were determined and compared using point-by-point multi-point tests and modal parameter identification methods. On this basis, equivalent stiffness was approximated based on inherent frequency. The results show that, BWC had the best vibration reduction performance, followed by BS, and GLT. The first three-order damping ratios of BWC were the largest, 2.14 %, 2.52 %, and 2.43 %, and those of GLT were the smallest, 1.40 %, 1.58 % and 1.60 %. Overall, the equivalent stiffness of the tested materials was in the order of: BS>BWC>GLT. The first three-order modal shape resembled the typical first-order quadrilateral torsional bump, the second-order middle-position bending bump, and the third-order bending-torsional bump. The resonance bands were mostly distributed at the middle position for GLT and BS, and at both ends of BWC. Vibration reduction of GLT was mainly attributed to the vibration reduction of solid bamboo, that of BS was mainly due to the high stiffness vibration reduction of bamboo fibers after recombination, and that of BWC resulted from the synergistic effect of the rigid surface layers and the even lamination of the low density and high porosity damping core layer after bamboo veneers. These results provide scientific reference for the vibration reduction design and optimization of bamboo engineering materials for applications in the fields of construction and transportation.

Trajectory-as-a-Sequence: A novel travel mode identification framework

Identifying travel modes from GPS tracks, as an essential technique to understand the travel behavior of a population, has received widespread interest over the past decade. While most previous Travel Mode Identification (TMI) methods separately identify the mode of each track segment of a GPS trajectory, in this paper, we propose a sequence-based TMI framework that constructs a feature sequence for each GPS trajectory and sent it to a sequence-to-sequence (seq2seq) model to obtain the corresponding travel mode label sequence, named Trajectory-as-a-Sequence (TaaS). The proposed seq2seq model consists of a Convolutional Encoder (CE) and a Recurrent Conditional Random Field (RCRF), where the CE extracts high-level features from the point-level trajectory features and the RCRF learns the context information of trajectories at both feature and label levels, thus outputting accurate and reasonable travel mode label sequences. To alleviate the lack of data, we adopted a two-stage model training strategy. Additionally, we design two novel bus-related features to assist the seq2seq model distinguishing different high-speed travel modes (i.e., bus, car, and railway) in the sequence. Besides the classical performance metrics such as accuracy, we propose a new metric that evaluates the rationality of the travel mode label sequence at the trajectory level. Comprehensive evaluations corresponding to the real-world TMI applications show that the sequence-based TaaS outperforms the segment-based models in practice. Furthermore, the results of ablation studies demonstrate that the elements integrated into the TaaS framework are helpful to improve the efficiency and accuracy of TMI.

Working Mode Identification Method for High Arch Dam Discharge Structure Based on Improved Wavelet Threshold–EMD and RDT Algorithm

Prototype vibration response data of high arch dam discharge structures inevitably mix various noises under the discharge excitation, which adversely affects the accuracy of the working modal identification of the structure. To effectively filter noise and reduce modal aliasing for better identification accuracy, this study proposes an improved modal threshold identification method based on an improved wavelet threshold–empirical mode decomposition (EMD) and random decrement technique (RDT) algorithm for high arch dam discharge structures. On the basis of the measured vibration response data of the dam, the wavelet threshold is adopted to filter out most of the high-frequency white noise and to reduce the boundary accumulation effect of EMD decomposition. Detrended fluctuation analysis (DFA) is utilized to filter white noise and low-frequency flow noise after EMD decomposition. The natural frequency and damping ratio of the structure system are obtained by the improved RDT algorithm. The engineering examples show that the proposed method can accurately filter the measured vibration response signal noise of the discharge structure, retain the signal characteristic information, improve the accuracy of working modal recognition of the structural vibration response, avoid the complex ordering process of the system, and ease the working modal parameter identification of high arch dam discharge structures. This method can be applied to the mode identification of other large structures, as well.

An improved NExT method for modal identification with tests validation

The Natural Excitation Technique (NExT) is widely used in structural modal parameter identification. However, the cross-correlation function is susceptible to the mode shape of each order, which is obtained by the traditional NExT method combined with other modal identification methods between the two measuring points, resulting in large errors of the identified frequency and damping ratio of various modes. Therefore, in this study, to obtain the modal responses of the target order correctly and eliminate interaction of various modes, a new combined NExT method with improved Empirical Mode Decomposition (EMD) method is developed. The improved NExT method is introduced firstly, then, the structural modal parameters are identified by five different methods, which are Ibrahim Time Domain Technique, Sparse Time Domain Algorithm, Autoregressive Moving Average timing method, Least Square Complex Exponential and Eigensystem Realization Algorithm respectively. The correction and improvement of the new method are verified through a simple-supported beam experiment and a shaking table test of a 12-story benchmark tall building at Tongji University. The structural modal parameters are identified and compared through adopting traditional NExT method and the proposed new NExT algorithm. Results illustrate that the improved NExT algorithm in this paper has a higher accuracy and effectiveness than the traditional one in structural modal parameter identification.