scholarly journals Identification of Mode Shapes Based on Ambient Signals and the IA-VMD Method

2021 ◽  
Vol 11 (2) ◽  
pp. 530
Author(s):  
Fang Liu ◽  
Sisi Lin ◽  
Chonggang Chen ◽  
Kangzhi Liu ◽  
Runmin Zou ◽  
...  
Keyword(s):  
Oscillation Mode ◽  
Engineering Application ◽  
Frequency Domain Analysis ◽  
Power Grids ◽  
Modal Identification ◽  
Normal Operation ◽  
Mode Shapes ◽  
Mode Decomposition ◽  
Interconnected Power Grids ◽  
Prony Algorithm

The paper presents a multiaspect analysis of multivalues and the broadband nature of system oscillation. By analyzing the ambient signal caused by random small disturbances during the normal operation of interconnected power grids, many system operation characteristics can be obtained. The traditional signal processing method cannot extract the information from ambient signals effectively. Aiming at the problem of broadband oscillation mode superposition and the difficulty of extracting information from ambient signals, an iterative adaptive variational mode decomposition (IA-VMD) method is proposed based on frequency domain analysis and signal energy. Additionally, the IA-VMD method, combined with a bandpass filter and the Prony algorithm, is used to realize the modal identification of broadband oscillation and ambient signals. Simulation experiments show that the IA-VMD method has good adaptability, antinoise characteristics, and a certain significant engineering application value as well.

A clustering-based strategy for automated structural modal identification

2017 ◽  
Vol 17 (2) ◽  
pp. 201-217 ◽  
Author(s):  
Rharã de Almeida Cardoso ◽  
Alexandre Cury ◽  
Flávio Barbosa
Keyword(s):  
Practical Importance ◽  
Modal Identification ◽  
Normal Operation ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Civil Infrastructures ◽  
Detection Algorithms ◽  
Structural Problems ◽  
Structural Evaluation ◽  
Development And Validation

Structural health monitoring of civil infrastructures has great practical importance for engineers, owners and stakeholders. Numerous researches have been carried out using long-term monitoring, such as the Rio–Niterói Bridge in Brazil, the former Z24 Bridge in Switzerland and the Millau Bridge in France. In fact, some structures are continuously monitored to supply dynamic measurements that can be used for the identification of structural problems such as the presence of cracks, excessive vibration or even to perform a quite extensive structural evaluation concerning its reliability and life cycle. The outputs of such an analysis, commonly entitled modal identification, are the so-called modal parameters, that is, natural frequencies, damping rations and mode shapes. Therefore, the development and validation of tools for the automatic modal identification during normal operation is fundamental, as the success of subsequent damage detection algorithms depends on the accuracy of the modal parameters’ estimates. This work proposes a novel methodology to perform, automatically, the modal identification based on the modes’ estimates data generated by any parametric system identification method. To assess the proposed methodology, several tests are conducted using numerically generated signals, as well as experimental data obtained from a simply supported beam and from a motorway bridge.

Compressed sensing based on dictionary learning for underdetermined modal identification

2020 ◽  
Vol 64 (1-4) ◽  
pp. 129-136
Author(s):  
Wei Guan ◽  
Longlei Dong ◽  
Jinxiong Zhou
Keyword(s):  
Compressed Sensing ◽  
Dictionary Learning ◽  
Engineering Application ◽  
Modal Identification ◽  
Mode Shapes ◽  
Engineering Structures ◽  
New Approach ◽  
Learned Dictionary ◽  
Complete Measurement ◽  
Practical Engineering

With the engineering structures becoming more complicated, it is difficult to obtain complete measurement responses with limited sensors. Thus, carrying out the underdetermined modal identification will have practical engineering application values. In this paper, a new approach for underdetermined blind modal identification based on dictionary learning in the framework of compressed sensing (CS) is proposed. The principal idea is to estimate modal shapes using a clustering technique, and recover modal responses combing the estimated mode shapes matrix and the learned dictionary. The experiment results on a typical cantilever beam structure illustrate that the proposed method can perform accurate dynamic parameters identification whether in underdetermined case or determined case.

Feature extraction method of pipeline signal based on parameter optimized vocational mode decomposition and exponential entropy

2021 ◽  
pp. 014233122110294
Author(s):  
Yina Zhou ◽  
Yong Zhang ◽  
Jingyi Lu ◽  
Fan Yang ◽  
Hongli Dong ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Working Conditions ◽  
Acoustic Signals ◽  
Normal Operation ◽  
Local Characteristic ◽  
Support Vector ◽  
K Value ◽  
Feature Vectors ◽  
Mode Decomposition ◽  
Exponential Entropy

Pipeline leakage is the main reason that affects normal operation of the pipeline. In this paper, a feature recognition method for pipeline acoustic signals based on vocational mode decomposition (VMD) and exponential entropy (EE) is investigated, which could extract the characteristics of pipeline signals and further accurately identify the pipeline acoustic signals under different working conditions. First, the VMD is used to decompose the collected acoustic signals into a number of mode components, during which process the optimal mode number (i.e., K-value) is determined by combining local characteristic scale decomposition (LCD) and correlation analysis methods. Then, the characteristic content of each mode component is analyzed with the help of the determined correlation coefficient (CC) threshold. If the correlation coefficient of a mode component is greater than the threshold, then the mode component is selected as the feature component. Subsequently, the EE values of the selected feature components are calculated to form the feature vectors corresponding to different kinds of pipeline signals. Finally, the feature vectors are input into support vector machine (SVM) to classify and recognize the different pipeline states. The experimental results demonstrate that the proposed method can identify the pipeline signals under different working conditions, and the recognition accuracy is up to [Formula: see text]. By analyzing and comparing with methods of EE-SVM, original data-SVM, VMD-singular spectrum entropy (SSE) and VMD-information entropy (IE), it is further verified that the proposed method is feasible and superior to the methods.

scholarly journals Localization of Increased Noise at Operating Speed of a Passenger Wagon

2021 ◽  
Vol 13 (2) ◽  
pp. 453
Author(s):  
Ján Ďungel ◽  
Peter Zvolenský ◽  
Juraj Grenčík ◽  
Lukáš Leštinský ◽  
Ján Krivda
Keyword(s):  
Natural Frequencies ◽  
Internal Noise ◽  
High Volume ◽  
Acoustic Properties ◽  
Normal Operation ◽  
Mode Shapes ◽  
Railway Transport ◽  
Noise Sources ◽  
Sound Fields ◽  
Real Picture

Noise generated by railway wagons in operation is produced by large numbers of noise sources. Although the railway transport is considered to be environmental friendly, especially in production of CO2 emissions, noise is one of problems that should be solved to keep the railway transport competitive and sustainable in future. In the EU, there is a strong permanent legislation pressure on interior and exterior noise reduction in railway transport. In the last years in Slovakia, besides modernization of existing passenger wagons fleet as a cheaper option of transport quality improvement, quite a number of coaches have been newly manufactured, too. The new design is usually aimed at increased speed, higher travel comfort, in which reduction of noise levels is expected. However, not always the new designs meet all expectations. Noise generation and propagation is a complex system and should be treated such from the beginning. There are possibilities to simulate the structural natural frequencies to predict vibrations and sound generated by these vibrations. However, the real picture about sound fields can be obtained only by practical measurements. Simulations of the wagon’s natural frequencies and mode shapes and measurements in real operation using a digital acoustic camera Soundcam have been done, which showed that for the calculated speeds the largest share of noise from the chassis was not radiated through the floor of the wagon, as was expected, but through the ceiling of the wagon. To improve the acoustic properties of the wagon at higher speed, it was proposed to use high-volume textile insulation in the ceiling of the wagon. The paper briefly presents modern research approaches in the search for ways to reduce internal noise in selected wagons used in normal operation on the Slovak railways.

scholarly journals A Fast and Robust Linear State Estimator for Very Large Scale Interconnected Power Grids

2018 ◽  
Vol 9 (5) ◽  
pp. 4975-4982 ◽  
Author(s):  
Chenxi Xu ◽  
Ali Abur
Keyword(s):  
Large Scale ◽  
Power Grids ◽  
State Estimator ◽  
Interconnected Power Grids ◽  
Linear State

scholarly journals Induction Motors Dynamic Eccentricity Fault Diagnosis Based on the Combined Use of WPD and EMD-Simulation Study

2018 ◽  
Vol 8 (10) ◽  
pp. 1709 ◽  
Author(s):  
Kun Tian ◽  
Tao Zhang ◽  
Yibo Ai ◽  
Weidong Zhang
Keyword(s):  
Wavelet Packet ◽  
Frequency Domain Analysis ◽  
Severity Factor ◽  
Mode Decomposition ◽  
Fault Severity ◽  
Combined Use ◽  
Dynamic Eccentricity ◽  
Fault Severity Factor ◽  
Low Degrees ◽  
Cage Induction Motor

The frequency-domain analysis using the fast Fourier transform (FFT) for diagnosis of eccentricity fault has been widely used in squirrel-cage induction motor (IM). However, with the restriction of sampling frequency and time acquisition, FFT analysis could not provide ideal results under low levels of dynamic eccentricity (DE). In this paper, a combined use of the wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) method is presented to diagnose the IM fault under low degrees of purely DE. The proposed method is based on the decomposition of apparent power signal and extracts the characteristic component. The fault severity factor (FSF) has been defined to evaluate the eccentricity severity. Simulation results using the finite element method (FEM) are tested to verify the effectiveness of the presented method under different load conditions.

scholarly journals Looseness localization for bolted joints using Bayesian operational modal analysis and modal strain energy

2018 ◽  
Vol 10 (11) ◽  
pp. 168781401880869 ◽  
Author(s):  
Yu-Jia Hu ◽  
Wei-Gong Guo ◽  
Cheng Jiang ◽  
Yun-Lai Zhou ◽  
Weidong Zhu
Keyword(s):  
Modal Analysis ◽  
Strain Energy ◽  
Damage Index ◽  
Operational Modal Analysis ◽  
Modal Identification ◽  
Bolted Joints ◽  
Mode Shapes ◽  
Bolted Connections ◽  
Modal Strain Energy ◽  
Beam Structures

Bayesian operational modal analysis and modal strain energy are employed for determining the damage and looseness of bolted joints in beam structures under ambient excitation. With this ambient modal identification technique, mode shapes of a damaged beam structure with loosened bolted connections are obtained based on Bayesian theory. Then, the corresponding modal strain energy can be calculated based on the mode shapes. The modal strain energy of the structure with loosened bolted connections is compared with the theoretical one without bolted joints to define a damage index. This approach uses vibration-based nondestructive testing of locations and looseness of bolted joints in beam structures with different boundary conditions by first obtaining modal parameters from ambient vibration data. The damage index is then used to identify locations and looseness of bolted joints in beam structures with single or multiple bolted joints. Furthermore, the comparison between damage indexes due to different looseness levels of bolted connections demonstrates a qualitatively proportional relationship.

scholarly journals A new automated procedure of modal identification in operational conditions

2018 ◽  
Vol 211 ◽  
pp. 21003 ◽  
Author(s):  
Gabriele Marrongelli ◽  
Carmelo Gentile
Keyword(s):  
Parametric Identification ◽  
Modal Identification ◽  
Mode Shapes ◽  
Parameter Estimates ◽  
Reference List ◽  
Modal Parameter ◽  
Operational Conditions ◽  
Historic Masonry ◽  
Stabilization Diagram ◽  
Using Data

Structural Health Monitoring (SHM) strategies are aimed at the assessment of structural performance, using data acquired by sensing systems. Among the different available approaches, vibration-based methods - involving the automation of the modal parameter estimation (MPE) and modal tracking (MT) procedures - are receiving increasing attention. In the context of vibration-based monitoring, this paper presents an automated procedure of modal identification in operational conditions. The presented algorithms can be used to effectively manage the results obtained by any parametric identification method that involves the construction and the interpretation of stabilization diagrams. The implemented approach introduces improvements related to both the MPE and the MT tasks. The MPE procedure consists of three key steps aimed at: (1) filtering a high number of spurious poles in the stabilization diagram; (2) clustering the remaining poles that share same characteristics in term of modal parameters; (3) improving the accuracy of the modal parameter estimates. In the MT procedure the use of a simple statistical approach to define adaptive thresholds together with continuously updated dynamic reference list guarantee an efficient tracking of the most representative structural modes. The advantages obtained through the proposed procedures are exemplified using data continuously collected on the historic masonry tower of San Gottardo in Corte, located in the centre of Milan, Italy. In addition, the ability of the automated algorithms to identify contributions inherent to different vibration modes, even if they are characterized by closely-spaced frequencies and a low discriminant between mode shapes, will be described in details.

Online Monitoring of Thermoacoustic Eigenmodes in Annular Combustion Systems Based on a State-Space Model

2016 ◽  
Vol 139 (2) ◽  
Author(s):  
D. Rouwenhorst ◽  
J. Hermann ◽  
W. Polifke
Keyword(s):  
State Space ◽  
Online Monitoring ◽  
Stability Margin ◽  
Surrogate Data ◽  
Modal Identification ◽  
Mode Shapes ◽  
Space Representation ◽  
Azimuthal Mode ◽  
Noise Characteristics ◽  
Combustion Systems

Thermoacoustic instabilities have the potential to restrict the operability window of annular combustion systems, primarily as a result of azimuthal modes. Azimuthal acoustic modes are composed of counter-rotating wave pairs, which form traveling modes, standing modes, or combinations thereof. In this work, a monitoring strategy is proposed for annular combustors, which accounts for azimuthal mode shapes. Output-only modal identification has been adapted to retrieve azimuthal eigenmodes from surrogate data, resembling acoustic measurements on an industrial gas turbine. Online monitoring of decay rate estimates can serve as a thermoacoustic stability margin, while the recovered mode shapes contain information that can be useful for control strategies. A low-order thermoacoustic model is described, requiring multiple sensors around the circumference of the combustor annulus to assess the dynamics. This model leads to a second-order state-space representation with stochastic forcing, which is used as the model structure for the identification process. Four different identification approaches are evaluated under different assumptions, concerning noise characteristics and preprocessing of the signals. Additionally, recursive algorithms for online parameter identification are tested.

scholarly journals Automated band selection for Bayesian FFT modal identification Based on RetinaNet

2021 ◽  
Vol 2078 (1) ◽  
pp. 012058
Author(s):  
Chen Wang ◽  
Zhilin Xue ◽  
Yipeng Su ◽  
Binbin Li
Keyword(s):  
Operation Time ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Mode Shapes ◽  
Band Selection ◽  
Data Sets ◽  
Civil Structures ◽  
Frequency Bands ◽  
Operational Conditions ◽  
Vibration Data

Abstract Bayesian FFT algorithm is a popular method to identify modal parameters, e.g., modal frequencies, damping ratios, and mode shapes, of civil structures under operational conditions. It is efficient and provides the identification uncertainty in terms of posterior distribution. However, in utilizing the Bayesian FFT algorithm, it is tedious to manually select frequency bands and initial frequencies. This step requires professional knowledge and costs most of time, which prevents the automation of Bayesian FFT algorithm. Regarding the band selection as an object detection problem, we design a band selection network based on the RetinaNet to automatically select frequency bands and a peak prediction network to predict the initial frequencies. The designed networks are trained using the singular value spectrum of measured ambient vibration data and verified by various data sets. It can achieve the human accuracy with much less operation time, and thus provides a corner stone for the automation of Bayesian FFT algorithm.

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