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Journal articles on the topic 'Automated Modal Analysis'

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Author: Grafiati

Published: 10 March 2023

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1

Hasan, M. Danial A., Z. A. B. Ahmad, M. Salman Leong, L. M. Hee, and M. Haffizzi Md. Idris. "Cluster Analysis for Automated Operational Modal Analysis: A Review." MATEC Web of Conferences 255 (2019): 02012. http://dx.doi.org/10.1051/matecconf/201925502012.

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Recent developments in the field of modal-based damage detection and vibration-based monitoring have led to a renewed interest in automated procedures for the operational modal analysis (OMA). The development of automated operational modal analysis (OMA) procedures marked a fundamental step towards the elimination of any user intervention since traditional modal identification requires a lot of interaction by an expert user. A key for effective automation of OMA is depended on well- defined modal indicators for a clear indication about which modes are to be selected as the physical modes. In all modal analysis, the construction of stabilization diagrams is necessary in order to illustrate, and decide, if a mode is physical or not for predefined range of the model order. On the other hand, the use of stabilization diagram tools involves a large amount of user interaction, costly, time-consuming process and certainly unsuited for online applications. Therefore, the development of automatic procedures for the analysis of stabilization diagrams by resembling decision-making process of a human has been carried out in recent years. For the sake of clearness, the automation of the interpretation of stabilization diagrams can generally be divided into two steps in order to speed up the process: a) elimination of noise modes and b) clustering of physical modes in order to obtain the most representative values of the estimated parameters of each clustered mode. In recent years, several alternative procedures have been proposed for clustering techniques. Therefore, this review aims to provide relevant essential information on the recent developments of cluster analysis in automated OMA. A literature review of existing clustering algorithm has been carried out to find best practice criteria for automated modal parameter identification which involving the general concepts of these techniques as well as the pro and cons of applying these clustering techniques are also discussed and summarised.

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2

Reynders, Edwin, Jeroen Houbrechts, and Guido De Roeck. "Fully automated (operational) modal analysis." Mechanical Systems and Signal Processing 29 (May 2012): 228–50. http://dx.doi.org/10.1016/j.ymssp.2012.01.007.

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3

Zheng, Yu Qiao, Rong Zhen Zhao, Shu Zhen Zhang, and Bin Peng. "Dynamic Simulation Analysis of Stacker for Automated Warehouse." Advanced Materials Research 411 (November 2011): 383–87. http://dx.doi.org/10.4028/www.scientific.net/amr.411.383.

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The dynamic characteristics quality of automated warehouse directly affects its reliability and efficiency. It is difficult to get the dynamic properties of automated warehouse with traditional design methods. The physical model of the GWD2005 automatic warehouse is established with the finite element method. Modal analysis is applied to stacker structure. Natural frequency and vibration mode of automated warehouse is confirmed. The instability factors of existing structure vibration are studied. The results show that the analysis method is effective to design the structure of automated warehouse.

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4

Zeng, Jice, and Zhen Hu. "Automated operational modal analysis using variational Gaussian mixture model." Engineering Structures 273 (December 2022): 115139. http://dx.doi.org/10.1016/j.engstruct.2022.115139.

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5

Sim, S. H., B. F. Spencer, M. Zhang, and H. Xie. "Automated decentralized modal analysis using smart sensors." Structural Control and Health Monitoring 17, no. 8 (November 28, 2010): 872–94. http://dx.doi.org/10.1002/stc.348.

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6

Tronci, Eleonora M., Maurizio De Angelis, Raimondo Betti, and Vittorio Altomare. "Semi-Automated Operational Modal Analysis Methodology to Optimize Modal Parameter Estimation." Journal of Optimization Theory and Applications 187, no. 3 (June 12, 2020): 842–54. http://dx.doi.org/10.1007/s10957-020-01694-x.

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7

Zhang, Guowen, Jinghua Ma, Zhuo Chen, and Ruirong Wang. "Automated eigensystem realisation algorithm for operational modal analysis." Journal of Sound and Vibration 333, no. 15 (July 2014): 3550–63. http://dx.doi.org/10.1016/j.jsv.2014.03.024.

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8

Zini, Giacomo, Michele Betti, and Gianni Bartoli. "A quality-based automated procedure for operational modal analysis." Mechanical Systems and Signal Processing 164 (February 2022): 108173. http://dx.doi.org/10.1016/j.ymssp.2021.108173.

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9

Sun, Miao, Mehrisadat Makki Alamdari, and Hamed Kalhori. "Automated Operational Modal Analysis of a Cable-Stayed Bridge." Journal of Bridge Engineering 22, no. 12 (December 2017): 05017012. http://dx.doi.org/10.1061/(asce)be.1943-5592.0001141.

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10

Neu, Eugen, Frank Janser, Akbar A. Khatibi, and Adrian C. Orifici. "Fully Automated Operational Modal Analysis using multi-stage clustering." Mechanical Systems and Signal Processing 84 (February 2017): 308–23. http://dx.doi.org/10.1016/j.ymssp.2016.07.031.

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11

Savchenko, V. V., and S. N. Poddubko. "Concept of transferring control to a driver in highly automated cars." Doklady of the National Academy of Sciences of Belarus 64, no. 5 (November 5, 2020): 624–31. http://dx.doi.org/10.29235/1561-8323-2020-64-5-624-631.

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From an interdisciplinary point of view, on the basis of a systematic approach, the concept of transferring control to a driver in highly automated vehicles was first formulated, which allows one to determine the potential possibility of regaining control of a highly automated car based on its awareness of the situational situation along the route of movement and monitoring of the current functional state, including readiness for emergency actions, and individual features of the driver. The classification of cross-modal information flows in highly automated and autonomous cars is presented. The definition of cross-modal interaction in highly automated vehicles is given, and the goal of analyzing heterogeneous information flows in on-board vehicle systems is formulated, where automatic analysis of heterogeneous information flows gives a synergistic effect. Examples of relevant driver information are provided.

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12

Dreher, Nathali Rolon, Gustavo Chaves Storti, and Tiago Henrique Machado. "Automated Operational Modal Analysis for Rotating Machinery Based on Clustering Techniques." Sensors 23, no. 3 (February 2, 2023): 1665. http://dx.doi.org/10.3390/s23031665.

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Many parameters can be used to express a machine’s condition and to track its evolution through time, such as modal parameters extracted from vibration signals. Operational Modal Analysis (OMA), commonly used to extract modal parameters from systems under operating conditions, was successfully employed in many monitoring systems, but its application in rotating machinery is still in development due to the distinct characteristics of this system. To implement efficient monitoring systems based on OMA, it is essential to automatically extract the modal parameters, which several studies have proposed in the literature. However, these algorithms are usually developed to deal with structures that have different characteristics when compared to rotating machinery, and, therefore, work poorly or do not work with this kind of system. Thus, this paper proposes, and has as its main novelty in, a new automated algorithm to carry out modal parameter identification on rotating machinery through OMA. The proposed technique was applied in two different datasets to enable the evaluation of the robustness to different systems and test conditions. It is revealed that the proposed algorithm is suitable for the accurate extraction of frequencies and damping ratios from the stabilization diagram, for both the rotor and the foundation, and only one user defined parameter is required.

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13

Yaghoubi, Vahid, Majid K. Vakilzadeh, and Thomas J. S. Abrahamsson. "Automated modal parameter estimation using correlation analysis and bootstrap sampling." Mechanical Systems and Signal Processing 100 (February 2018): 289–310. http://dx.doi.org/10.1016/j.ymssp.2017.07.004.

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14

Ponsioen, Sten, Tiemo Pedergnana, and George Haller. "Automated computation of autonomous spectral submanifolds for nonlinear modal analysis." Journal of Sound and Vibration 420 (April 2018): 269–95. http://dx.doi.org/10.1016/j.jsv.2018.01.048.

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15

BOWMAN, L. E., M. N. SPILDE, and J. J. PAPIKE. "Automated energy dispersive spectrometer modal analysis applied to the diogenites." Meteoritics & Planetary Science 32, no. 6 (November 1997): 869–75. http://dx.doi.org/10.1111/j.1945-5100.1997.tb01577.x.

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16

Teng, Jun, De-Hui Tang, Xiao Zhang, Wei-Hua Hu, Samir Said, and Rolf Rohrmann. "Automated Modal Analysis for Tracking Structural Change during Construction and Operation Phases." Sensors 19, no. 4 (February 22, 2019): 927. http://dx.doi.org/10.3390/s19040927.

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The automated modal analysis (AMA) technique has attracted significant interest over the last few years, because it can track variations in modal parameters and has the potential to detect structural changes. In this paper, an improved density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean the abnormal poles in a stabilization diagram. Moreover, the optimal system model order is also discussed to obtain more stable poles. A numerical simulation and a full-scale experiment of an arch bridge are carried out to validate the effectiveness of the proposed algorithm. Subsequently, the continuous dynamic monitoring system of the bridge and the proposed algorithm are implemented to track the structural changes during the construction phase. Finally, the artificial neural network (ANN) is used to remove the temperature effect on modal frequencies so that a health index can be constructed under operational conditions.

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Wu, Gangrou, Min He, Peng Liang, Chunsheng Ye, and Yue Xu. "Automated Modal Identification Based on Improved Clustering Method." Mathematical Problems in Engineering 2020 (April 25, 2020): 1–16. http://dx.doi.org/10.1155/2020/5698609.

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The automated modal identification has been playing an important role in online structural damage detection and condition assessment. This paper proposes an improved hierarchical clustering method to identify the precise modal parameters by automatically interpreting the stabilization diagram. Two major improvements are provided in the whole clustering process. The modal uncertainty is first introduced in the first stage to eliminate as many as possible mathematical modal data to produce more precise clustering threshold, which helps to produce more precise clustering results. The boxplot is introduced in the last stage to assess the precision of the clustering results from a statistical perspective. Based on an iterative analysis of boxplot, the outliers of the clustering results are found out and eliminated and the precise modal results are finally produced. The Z24 benchmark experiment data are utilized to validate the feasibility of the proposed method, and comparison between the previous method and the improved method is also provided. From the result, it can be concluded that the modal uncertainty is more effective than the other modal criteria in distinguishing the mathematical modal data. The modal results by clustering process are not precise in statistic and the boxplot can find out the outliers of the clustering results and produce more precise modal results. The improved automated modal identification method can automatically extract the physical modal data and produce more precise modal parameters.

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18

Rainieri, Carlo, Filipe Magalhaes, and Filippo Ubertini. "Automated Operational Modal Analysis and Its Applications in Structural Health Monitoring." Shock and Vibration 2019 (November 6, 2019): 1–3. http://dx.doi.org/10.1155/2019/5497065.

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19

Chen, Zhi-Wei, Kui-Ming Liu, Wang-Ji Yan, Jian-Lin Zhang, and Wei-Xin Ren. "Two-Stage Automated Operational Modal Analysis Based on Power Spectrum Density Transmissibility and Support-Vector Machines." International Journal of Structural Stability and Dynamics 21, no. 05 (February 26, 2021): 2150068. http://dx.doi.org/10.1142/s0219455421500681.

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Power spectrum density transmissibility (PSDT) functions have attracted widespread attention in operational modal analysis (OMA) because of their robustness to excitations. However, the selection of the peaks and stability axes are still subjective and requires further investigation. To this end, this study took advantage of PSDT functions and support-vector machines (SVMs) to propose a two-stage automated modal identification method. In the first stage, the automated identification of peaks is achieved by introducing the peak slope (PS) as a critical index and determining its threshold using the SVM classifier. In the second stage, the automated identification of the stability axis is achieved by introducing the relative difference coefficients (RDCs) of the modal parameters as indicators and determining their thresholds using the SVM classifier. To verify its feasibility and accuracy, the proposed method was applied to an ASCE-benchmark structure in the laboratory and in a high-rise building installed with a structural health monitoring system (SHMS). The results showed that the automated identification method could effectively eliminate spurious modes and accurately identify the closely spaced modes. The proposed method can be automatically applied without manual intervention, and it is robust to noise. It is promising for application to the real-time condition evaluation of civil structures installed with SHMSs.

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20

Ellinger, Johannes, Leopold Beck, Maximilian Benker, Roman Hartl, and Michael F. Zaeh. "Automation of Experimental Modal Analysis Using Bayesian Optimization." Applied Sciences 13, no. 2 (January 10, 2023): 949. http://dx.doi.org/10.3390/app13020949.

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The dynamic characterization of structures by means of modal parameters offers many valuable insights into the vibrational behavior of these structures. However, modal parameter estimation has traditionally required expert knowledge and cumbersome manual effort such as, for example, the selection of poles from a stabilization diagram. Automated approaches which replace the user inputs with a set of rules depending on the input data set have been developed to address this shortcoming. This paper presents an alternative approach based on Bayesian optimization. This way, the possible solution space for the modal parameter estimation is kept as widely open as possible while ensuring a high accuracy of the final modal model. The proposed approach was validated on both a synthetic test data set and experimental modal analysis data of a machine tool. Furthermore, it was benchmarked against a similar tool from a well-known numerical computation software application.

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21

Devriendt, Christof, Filipe Magalhães, Wout Weijtjens, Gert De Sitter, Álvaro Cunha, and Patrick Guillaume. "Structural health monitoring of offshore wind turbines using automated operational modal analysis." Structural Health Monitoring 13, no. 6 (November 2014): 644–59. http://dx.doi.org/10.1177/1475921714556568.

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This article will present and discuss the approach and the first results of a long-term dynamic monitoring campaign on an offshore wind turbine in the Belgian North Sea. It focuses on the vibration levels and modal parameters of the fundamental modes of the support structure. These parameters are crucial to minimize the operation and maintenance costs and to extend the lifetime of offshore wind turbine structure and mechanical systems. In order to perform a proper continuous monitoring during operation, a fast and reliable solution, applicable on an industrial scale, has been developed. It will be shown that the use of appropriate vibration measurement equipment together with state-of-the art operational modal analysis techniques can provide accurate estimates of natural frequencies, damping ratios, and mode shapes of offshore wind turbines. The identification methods have been automated and their reliability has been improved, so that the system can track small changes in the dynamic behavior of offshore wind turbines. The advanced modal analysis tools used in this application include the poly-reference least squares complex frequency-domain estimator, commercially known as PolyMAX, and the covariance-driven stochastic subspace identification method. The implemented processing strategy will be demonstrated on data continuously collected during 2 weeks, while the wind turbine was idling or parked.

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22

Volkmar, Robin, Keith Soal, Yves Govers, and Marc Böswald. "Experimental and operational modal analysis: Automated system identification for safety-critical applications." Mechanical Systems and Signal Processing 183 (January 2023): 109658. http://dx.doi.org/10.1016/j.ymssp.2022.109658.

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23

Li, Jianming, Tengfei Bao, and Carlos E. Ventura. "An automated operational modal analysis algorithm and its application to concrete dams." Mechanical Systems and Signal Processing 168 (April 2022): 108707. http://dx.doi.org/10.1016/j.ymssp.2021.108707.

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24

Cheema, P., M. Makki Alamdari, G. A. Vio, F. L. Zhang, and C. W. Kim. "Infinite mixture models for operational modal analysis: An automated and principled approach." Journal of Sound and Vibration 491 (January 2021): 115757. http://dx.doi.org/10.1016/j.jsv.2020.115757.

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25

Bertini, L., P. Neri, C. Santus, and A. Guglielmo. "Automated Experimental Modal Analysis of Bladed Wheels with an Anthropomorphic Robotic Station." Experimental Mechanics 57, no. 2 (October 17, 2016): 273–85. http://dx.doi.org/10.1007/s11340-016-0223-5.

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26

Gioia, Nicoletta, Cédric Peeters, Patrick Guillaume, and Jan Helsen. "Identification of Noise, Vibration and Harshness Behavior of Wind Turbine Drivetrain under Different Operating Conditions." Energies 12, no. 17 (September 3, 2019): 3401. http://dx.doi.org/10.3390/en12173401.

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Noise, vibration and harshness (NVH) problems are critical issues to be tackled for wind turbine drivetrains. Tracking the behavior of modal parameters of the machines’ fundamental modes during operation it is of high interest to validate complex simulation models. A powerful approach for this purpose is represented by operational modal analysis (OMA). This paper describes the investigation of an automated technique for continuously tracking the modes of a rotating mechanical system running in normal operating conditions. The modal estimation procedure is based on an automatic version of the pLSCF (poly-reference Least-Square Complex Frequency-Domain) algorithm. The latter is coupled with a method that automatically tracks the modal parameters along different data sets. The use of OMA on a rotating component of the wind turbine creates the need to deal with harmonics in order to satisfy one of the assumptions of OMA. For this purpose, the use of a cepstrum editing procedure is analyzed and implemented. Modal estimates obtained from an automated analysis on stand still data and normal operating conditions data are compared, to test the added value of the cepstrum editing procedure and the robustness of the method when used on real data. To illustrate and validate the implemented methodology, data acquired during a long-term monitoring campaign of a wind turbine drivetrain are used.

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27

Rainieri, Carlo, Giovanni Fabbrocino, and E. Cosenza. "Automated Operational Modal Analysis as Structural Health Monitoring Tool: Theoretical and Applicative Aspects." Key Engineering Materials 347 (September 2007): 479–84. http://dx.doi.org/10.4028/www.scientific.net/kem.347.479.

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The aim of structural health monitoring for civil structures is not only detection of sudden or progressive damages but also monitoring their performance under operational conditions or under some particular environmental issues such as earthquakes. Seismic protection of buildings at risk can be reached increasing the knowledge of the structural behavior of existing constructions. This circumstance points out the opportunity of monitoring the performance of civil structures over their operational lives. The present paper deals with automated Structural Health Monitoring (SHM) technologies adopted for the School of Engineering Main Building at the University of Naples “Federico II”. In particular, the attention is focused on the development of an automated procedure based on the Operational Modal Analysis (OMA) that must ensure the continuous monitoring and extraction of the modal parameters of the building. Some numerical examples are then discussed in order to point out effectiveness of the algorithm and relevant issues that need to be improved.

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28

de Almeida Cardoso, Rharã, Alexandre Cury, and Flávio Barbosa. "A clustering-based strategy for automated structural modal identification." Structural Health Monitoring 17, no. 2 (January 31, 2017): 201–17. http://dx.doi.org/10.1177/1475921716689239.

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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.

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29

Tran, Thanh T. X., and Ekin Ozer. "Automated and Model-Free Bridge Damage Indicators with Simultaneous Multiparameter Modal Anomaly Detection." Sensors 20, no. 17 (August 22, 2020): 4752. http://dx.doi.org/10.3390/s20174752.

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This paper pursues a simultaneous modal parameter anomaly detection paradigm to structural damage identification inferred from vibration-based structural health monitoring (SHM) sensors, e.g., accelerometers. System Realization Using Information Matrix (SRIM) method is performed in short duration sweeping time windows for identification of state matrices, and then, modal parameters with enhanced automation. Stable modal poles collected from stability diagrams are clustered and fed into the Gaussian distribution-based anomaly detection platform. Different anomaly thresholds are examined both on frequency and damping ratio terms taking two testbed bridge structures as application means, and simplistic Boolean Operators are performed to merge univariate anomalies. The first bridge is a reinforced concrete bridge subjected to incremental damage through a series of seismic shake table experiments conducted at the University of Nevada, Reno. The second bridge is a steel arch structure at Columbia University Morningside Campus, which reflects no damage throughout the measurements, unlike the first one. Two large-scale implementations indicate the realistic performance of automated modal analysis and anomaly recognition with minimal human intervention in terms of parameter extraction and learning supervision. Anomaly detection performance, presented in this paper, shows variation according to the designated thresholds, and hence, the information retrieval metrics being considered. The methodology is well-fitted to SHM problems which require sole data-driven, scalable, and fully autonomous perspectives.

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30

Kähler, O., S. Hochstöger, G. Kemper, and J. Birchbauer. "AUTOMATING POWERLINE INSPECTION: A NOVEL MULTISENSOR SYSTEM FOR DATA ANALYSIS USING DEEP LEARNING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 25, 2020): 747–54. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-747-2020.

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Abstract. Powerline infrastructure provides the backbone for the electricity supply of industrial, administrative and private sectors. Its maintenance requires regular inspections, that are still largely carried out manually. In this work, we propose an automated inspection system instead. We review current inspection processes as a baseline, give an overview of relevant inspection criteria, propose a suitable multi-modal sensor system, and discuss methods to automate the inspection tasks. In our system, we particularly focus on the high-level organization of the sensor data and inspection results to form a Digital Twin of the power line, that allows operators to browse through the recorded data in a meaningful way and review the status of their powerline from the desk.

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31

Sun, Hongwei, Chao Liu, Benshun Zhang, Yiming Chen, Sibo Zhao, and Chengdong Li. "Static and modal analysis of industrial robots." Journal of Physics: Conference Series 2174, no. 1 (January 1, 2022): 012086. http://dx.doi.org/10.1088/1742-6596/2174/1/012086.

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Abstract As an important automation equipment in contemporary manufacturing industry, 5-axis industrial robot is an important part of automated production. It improves the quality of work and production efficiency, reduces the workload of workers and lowers production costs. The development of industrial robots has a profound impact on China’s goal of being among the world’s manufacturing powerhouses in the first half of the 21st century. This paper presents a finite element analysis of the overall system of a five-axis intelligent robot for industrial use. The main focus is on optimizing the mechanical structure of the five-axis intelligent robot for industrial use, and the external structure of the key support components of the robot is simplified to create the model. Hazardous stress states were selected for the system analysis, a static analysis was implemented for each component to obtain the displacement and stresses corresponding to them, the maximum deformation of the key parts of the robot was obtained, and finally its stiffness was evaluated. Then the modal analysis is performed on this robot model, and the modal vibration clouds of the first 6 orders are selected to study the vibration patterns to obtain the corresponding frequencies, which provides data support for the robot to avoid working at resonant frequencies and provides a theoretical basis for improving its overall structure.

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32

Gouskir, Mohamed, Belaid Bouikhalene, Hicham Aissaoui, and Benachir Elhadadi. "Automatic Diagnosis of Brain Magnetic Resonance Images based on Riemannian Geometry." Journal of Electronic Commerce in Organizations 13, no. 2 (April 2015): 30–40. http://dx.doi.org/10.4018/jeco.2015040103.

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Automated brain tumor detection and segmentation, from medical images, is one of the most challenging. The authors present, in this paper, an automatic diagnosis of brain magnetic resonance image. The goal is to prepare the image of the human brain to locate the existence of abnormal tissues in multi-modal brain magnetic resonance images. The authors start from the image acquisition, reduce information, brain extraction, and then brain region diagnosis. Brain extraction is the most important preprocessing step for automatic brain image analysis. The authors consider the image as residing in a Riemannian space and they based on Riemannian manifold to develop an algorithm to extract brain regions, these regions used in other algorithm to brain tumor detection, segmentation and classification. Riemannian Manifolds show the efficient results to brain extraction and brain analysis for multi-modal resonance magnetic images.

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33

Charpagne, M. A., J. C. Stinville, P. G. Callahan, D. Texier, Z. Chen, P. Villechaise, V. Valle, and T. M. Pollock. "Automated and quantitative analysis of plastic strain localization via multi-modal data recombination." Materials Characterization 163 (May 2020): 110245. http://dx.doi.org/10.1016/j.matchar.2020.110245.

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34

McCulloch, C. F., P. Vanhonacker, and E. Dascotte. "Validating and Updating Finite Element Models Using Experimental Measurements of Dynamics." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 204, no. 1 (January 1990): 45–50. http://dx.doi.org/10.1243/pime_proc_1990_204_208_02.

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A method is proposed for updating finite element models of structural dynamics using the results of experimental modal analysis, based on the sensitivities to changes in physical parameters. The method avoids many of the problems of incompatibility and inconsistency between the experimental and analytical modal data sets and enables the user to express confidence in measured data and modelling assumptions, allowing flexible but automated model updating.

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35

Hu, Wei-Hua, De-Hui Tang, Ming Wang, Jun-Le Liu, Zuo-Hua Li, Wei Lu, Jun Teng, Samir Said, and Rolf G. Rohrmann. "Resonance Monitoring of a Horizontal Wind Turbine by Strain-Based Automated Operational Modal Analysis." Energies 13, no. 3 (January 26, 2020): 579. http://dx.doi.org/10.3390/en13030579.

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A strain-based automated operational modal analysis algorithm is proposed to track the long-term dynamic behavior of a horizontal wind turbine under operational conditions. This algorithm is firstly validated by a scaled wind turbine model, and then it is applied to the dynamic strain responses recorded from a 5 MW wind turbine system. We observed variations in the fundamental frequency and 1f, 3f excitation frequencies due to the mass imbalance of the blades and aerodynamic excitation by the tower dam or tower wake. Inspection of the Campbell diagram revealed that the adverse resonance phenomenon and Sommerfeld effect causing excessive vibrations of the wind tower.

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36

Rainieri, C. "Perspectives of Second-Order Blind Identification for Operational Modal Analysis of Civil Structures." Shock and Vibration 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/845106.

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Innovative methods for output-only estimation of the modal properties of civil structures are based on blind source separation techniques. In the present paper attention is focused on the second-order blind identification (SOBI) algorithm and the influence of its analysis parameters on computational time and accuracy of modal parameter estimates. These represent key issues in view of the automation of the algorithm and its integration within vibration-based monitoring systems. The herein reported analyses and results provide useful hints for reduction of computational time and control of accuracy of estimates. The latter topic is of interest in the case of single modal identification tests, too. A criterion for extraction of accurate modal parameter estimates is identified and applied to selected experimental case studies. They are representative of the different levels of complexity that can be encountered during real modal tests. The obtained results point out that SOBI can provide accurate estimates and it can also be automated, confirming that it represents a profitable alternative for output-only modal analysis and vibration-based monitoring of civil structures.

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Bin Zahid, Fahad, Zhi Chao Ong, Shin Yee Khoo, and Mohd Fairuz Mohd Salleh. "Implementation of BCI based semi-automated impact device for performing Impact Synchronous Modal Analysis." Measurement 208 (February 2023): 112454. http://dx.doi.org/10.1016/j.measurement.2023.112454.

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38

Hasan, M. Danial A., Z. A. B. Ahmad, M. Salman Leong, and L. M. Hee. "Automated Denoising Technique for Random Input Signals using Empirical Mode Decomposition (EMD)-Stabilization Diagram." MATEC Web of Conferences 255 (2019): 01004. http://dx.doi.org/10.1051/matecconf/201925501004.

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The present paper deals with the novel approach of filtering technique using hybrid of empirical mode decomposition technique with stabilization diagram, that autonomously implemented within Matlab. Noise or unwanted signal is always present in the data and a bad signal-to-noise can cause a severe error in modal parameter extraction. With the recent developments of automated procedures without user interaction for the operational modal analysis (OMA), the corrupted input signals turn out to be a big issue in obtaining reliable results of automated modal parameter identification. The appearance of noise or unwanted modes due to environmental effects could affect the actual structural modes selection. There is a significant issue regarding “noise” (or spurious) modes and eliminating them from the raw signal remains to be solved and requires a lot of interaction with an expert user. In the parametric modal analysis, oversizing of a modal model is usually performed to minimize the bias on modal estimates by getting all physical modes in the frequency range of interest and help to obtain a good model fit to the data. However, this will introduce noise modes. Thus, authors take advantage of tools, such as the stabilization diagram, to illustrate, and decide, if a mode is physical or not. This selection is not a trivial task, but it may be difficult and time consuming depending on the quality of data, the performance of the estimator and the experience of the user. Since the extensive interaction between tools and user is inappropriate for monitoring purposes, image clustering tool is introduced to separate the physical poles from the others with short response time and low computational efforts compared to the available clustering algorithm. Meanwhile, Empirical mode decomposition (EMD) is then introduced to break down a signal into various components without leaving the time domain and purposely used for filtering. These are a great combination as well as an effective procedure in producing a good input signal that free from unwanted modes which can cause disruptive decision making for the actual modes selection.

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Bajrić, Anela, Jan Høgsberg, and Finn Rüdinger. "Evaluation of damping estimates by automated Operational Modal Analysis for offshore wind turbine tower vibrations." Renewable Energy 116 (February 2018): 153–63. http://dx.doi.org/10.1016/j.renene.2017.03.043.

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Tarpø, Marius, Tobias Friis, Peter Olsen, Martin Juul, Christos Georgakis, and Rune Brincker. "Automated reduction of statistical errors in the estimated correlation function matrix for operational modal analysis." Mechanical Systems and Signal Processing 132 (October 2019): 790–805. http://dx.doi.org/10.1016/j.ymssp.2019.07.024.

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Zhang, Yilan, Masahiro Kurata, and Jerome P. Lynch. "Long-Term Modal Analysis of Wireless Structural Monitoring Data from a Suspension Bridge under Varying Environmental and Operational Conditions: System Design and Automated Modal Analysis." Journal of Engineering Mechanics 143, no. 4 (April 2017): 04016124. http://dx.doi.org/10.1061/(asce)em.1943-7889.0001198.

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Rainieri, Carlo, Filipe Magalhaes, Danilo Gargaro, Giovanni Fabbrocino, and Alvaro Cunha. "Predicting the variability of natural frequencies and its causes by Second-Order Blind Identification." Structural Health Monitoring 18, no. 2 (February 23, 2018): 486–507. http://dx.doi.org/10.1177/1475921718758629.

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Structural aging, degradation phenomena, and damage due to hazardous events are common causes of failure in civil structures and infrastructures. The increasing need of extending the structure lifespan for sustainability and economic reasons motivated the rapid development of remote, fully automated structural health monitoring systems. Different approaches have been developed for damage detection based on the incoming data. Modal-based damage detection is probably one of the most popular procedures for structural health monitoring of civil structures, also thanks to the development of robust automated operational modal analysis algorithms in the last decade. However, the sensitivity of modal parameter estimates and the associated damage features to environmental and operational factors represents a significant drawback to the extensive application of this technology. Thus, effective damage detection cannot skip the preliminary compensation of the effect of those variables on modal properties. Different approaches to compensate the environmental influence on modal property estimates are reported in the literature. In this article, the use of Second-Order Blind Identification is proposed. It is applied to a number of case studies in order to validate its effectiveness in the presence of one or more environmental or operational variables. Results demonstrate that it can model the variability of natural frequency estimates in operational conditions and, above all, it can give a fundamental insight in determining the causes of such variability.

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Pyrovolakis, Konstantinos, Paraskevi Tzouveli, and Giorgos Stamou. "Multi-Modal Song Mood Detection with Deep Learning." Sensors 22, no. 3 (January 29, 2022): 1065. http://dx.doi.org/10.3390/s22031065.

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The production and consumption of music in the contemporary era results in big data generation and creates new needs for automated and more effective management of these data. Automated music mood detection constitutes an active task in the field of MIR (Music Information Retrieval). The first approach to correlating music and mood was made in 1990 by Gordon Burner who researched the way that musical emotion affects marketing. In 2016, Lidy and Schiner trained a CNN for the task of genre and mood classification based on audio. In 2018, Delbouys et al. developed a multi-modal Deep Learning system combining CNN and LSTM architectures and concluded that multi-modal approaches overcome single channel models. This work will examine and compare single channel and multi-modal approaches for the task of music mood detection applying Deep Learning architectures. Our first approach tries to utilize the audio signal and the lyrics of a musical track separately, while the second approach applies a uniform multi-modal analysis to classify the given data into mood classes. The available data we will use to train and evaluate our models comes from the MoodyLyrics dataset, which includes 2000 song titles with labels from four mood classes, {happy, angry, sad, relaxed}. The result of this work leads to a uniform prediction of the mood that represents a music track and has usage in many applications.

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Koh, Edwin J. Y., Eiman Amini, Geoffrey J. McLachlan, and Nick Beaton. "Utilising convolutional neural networks to perform fast automated modal mineralogy analysis for thin-section optical microscopy." Minerals Engineering 173 (November 2021): 107230. http://dx.doi.org/10.1016/j.mineng.2021.107230.

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Marrongelli, Gabriele, Filipe Magalhães, and Álvaro Cunha. "Automated Operational Modal Analysis of an arch bridge considering the influence of the parametric methods inputs." Procedia Engineering 199 (2017): 2172–77. http://dx.doi.org/10.1016/j.proeng.2017.09.170.

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MARTARELLI, M., G. M. REVEL, and C. SANTOLINI. "AUTOMATED MODAL ANALYSIS BY SCANNING LASER VIBROMETRY: PROBLEMS AND UNCERTAINTIES ASSOCIATED WITH THE SCANNING SYSTEM CALIBRATION." Mechanical Systems and Signal Processing 15, no. 3 (May 2001): 581–601. http://dx.doi.org/10.1006/mssp.2000.1336.

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Kropp, Andreas, and Daniel Heiserer. "Efficient Broadband Vibro-Acoustic Analysis of Passenger Car Bodies Using an FE-Based Component Mode Synthesis Approach." Journal of Computational Acoustics 11, no. 02 (June 2003): 139–57. http://dx.doi.org/10.1142/s0218396x03001870.

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The costliest part of computationally optimizing automobiles for Noise, Vibration and Harshness (NVH) is the frequency response calculation of very large finite element models. In general, NVH is treated as a fluid-structure interaction between the body and the car interior fluid volume. In this paper, some features of the current approach to NVH computations at BMW are elucidated. First, the equations of motion for a typical NVH problem will be derived using three different formulations, differential, algebraic and modal. Next, three numerical approaches for solving the large systems of equations (direct, modal and Automated Multi-Level Substructuring (AMLS)) will be shown. Finally, the numerical precision and the efficiency of these approaches will be assessed.

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Hsu, Ting-Yu, Cheng-Chin Chien, Shen-Yuan Shiao, Chun-Chung Chen, and Kuo-Chun Chang. "Analysis of Environmental and Typhoon Effects on Modal Frequencies of a Power Transmission Tower." Sensors 20, no. 18 (September 10, 2020): 5169. http://dx.doi.org/10.3390/s20185169.

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The structural health monitoring of power transmission towers (PTTs) has drawn increasing attention from researchers in recent years; however, no long-term monitoring of the dynamic parameters of PTTs has previously been reported in the literature. This study performed the long-term monitoring of an instrumented PTT. An automated subspace identification technique was used to extract the dynamic parameters of the PTT from ambient vibration measurements taken over approximately ten months in 2017. Ten target modal frequencies were selected to explore the effects of environmental factors, such as temperature and wind speed, as well as the root-mean-square (RMS) acceleration response of the PTT. Variations in the modal frequencies of approximately 2% to 8% were observed during the study period. In general, among the environmental factors, the temperature was found to be the primary cause of decreases in the modal frequencies, except in the case of some of the higher modes. Typhoon Nesat, which affected the PTT on July 29th, 2017, seems to have decreased the modal frequencies of the PTT, especially for the higher modes. This reduction in the modal frequencies seems to have lasted for approximately two and a half months, after which they recovered to their normal state, probably due to a seasonal cool down in temperature. The reduction percentages in the modal frequencies due to Typhoon Nesat were quantified as approximately −0.89% to −1.34% for the higher modes, but only −0.07% to −0.46% for the remaining lower modes. Although the unusual reductions in the modal frequencies are reported in this study, the reason for this phenomenon is not clear yet. Further studies would be required in the future in order to find the cause.

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Abu Hasan, Muhammad Danial Bin, Zair Asrar Bin Ahmad, Mohd Salman Leong, and Lim Meng Hee. "Automated Harmonic Signal Removal Technique Using Stochastic Subspace-Based Image Feature Extraction." Journal of Imaging 6, no. 3 (March 5, 2020): 10. http://dx.doi.org/10.3390/jimaging6030010.

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This paper presents automated harmonic removal as a desirable solution to effectively identify and discard the harmonic influence over the output signal by neglecting any user-defined parameter at start-up and automatically reconstruct back to become a useful output signal prior to system identification. Stochastic subspace-based algorithms (SSI) methods are the most practical tool due to the consistency in modal parameters estimation. However, it will be problematic when applied to structures with rotating machines and the presence of harmonic excitations. Difficulties arise when automating this procedure without any human interaction and the problem is still unresolved because stochastic subspace-based algorithms (SSI) methods still require parameters (the maximum within-cluster distance) that are compulsory to be defined at start-up for each analysis of the dataset. Thus, the use of image-based feature extraction for clustering and classification of harmonic components and structural poles directly from a stabilization diagram and for modal system identification is the focus of the present paper. As a fundamental necessary condition, the algorithm has been assessed first from computed numerical responses and then applied to the experimental dataset with the presence of harmonic excitation. Results of the proposed approach for estimating modal parameters demonstrated very high accuracy and exhibited consistent results before and after removing harmonic components from the response signal.

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Bucchi, Francesco, Francesco Frendo, Federico Bavaresco, and Giuseppe Conte. "Multibody simulation of a rope-driven automated people mover." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 232, no. 8 (March 14, 2018): 2173–85. http://dx.doi.org/10.1177/0954409718764533.

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In this paper, a multibody model of the automated people mover, PisaMover, is presented. PisaMover is a rope-driven small train, composed of a few cabins, which makes use of train-inspired bogies. The aim of the model was to support the design of the architecture of the suspensions and to select the proper characteristics of the elastic and damping elements in order to fulfill comfort needs of passengers and to resolve the constraints related to the layout of the vehicle and the guide-ways. For this purpose, attention was especially focussed on the definition of the railway path and the modeling of the forces of the supporting and guidance wheels. A simplified model of the rope was implemented, which neglects the rope elasticity and computes the rope force direction taking account of the different positions of the sheaves along the path. The multibody simulation allowed to select the most appropriate suspension system and to properly define the elastic and damping characteristics of the shock absorbers, with respect to the technical constraints. A modal analysis was performed, and several dynamic on-track simulations were carried out to infer the effect of dampers’ design on the comfort of passengers. Within the validity of the simplifying assumptions, the multibody simulation also allowed to obtain a fairly good estimate of the loads necessary for the design of the main structural components.

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