• Smart Structures and Systems
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• 제15권2호
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• pp.447-468
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• 2015

Modal identification of civil engineering structures based on ambient vibration measurement has been widely investigated in the past decades, and a variety of output-only operational modal identification methods have been proposed. However, vibration modes, even fundamental low-order modes, are not always identifiable for large-scale structures under ambient vibration excitation. The identifiability of vibration modes, deficiency in modal identification, and criteria to evaluate robustness of the identified modes when applying output-only modal identification techniques to ambient vibration responses were scarcely studied. In this study, the mode identifiability of the cable-stayed Ting Kau Bridge using ambient vibration measurements and the influence of the excitation intensity on the deficiency and robustness in modal identification are investigated with long-term monitoring data of acceleration responses acquired from the bridge under different excitation conditions. It is observed that a few low-order modes, including the second global mode, are not identifiable by common output-only modal identification algorithms under normal ambient excitations due to traffic and monsoon. The deficient modes can be activated and identified only when the excitation intensity attains a certain level (e.g., during strong typhoons). The reason why a few low-order modes fail to be reliably identified under weak ambient vibration excitations and the relation between the mode identifiability and the excitation intensity are addressed through comparing the frequency-domain responses under normal ambient vibration excitations and under typhoon excitations and analyzing the wind speeds corresponding to different response data samples used in modal identification. The threshold value of wind speed (generalized excitation intensity) that makes the deficient modes identifiable is determined.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2014년도 춘계학술대회 논문집
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• pp.69-70
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• 2014

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2012년도 추계학술대회 논문집
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• pp.504-505
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• 2012

• 터널과지하공간
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• 제11권1호
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• pp.14-19
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• 2001

발파진동을 계측하여 예측방법의 타당성을 검토하였고, 환경규제 기준으로의 변환방식을 규명하였다. 진동레벨과 진동속도의 환산거리 설계 적용성은 진동속도가 더 좋았다. 따라서 설계나 시공은 진동속도로 관리하고 이를 법적 기준인 진동레벨로 변환할 필요성이 있었다. 기존의 변환식 중에서 충격진동 데이터로만 구성하여 변환식을 구하였고, 동시에 측정된 진동속도와 진동레벨의 상관식에 의한 변환식을 구하였으며, 퓨리에 변환을 하여 각 주파수 별로 감각보정하여 진동레벨을 구하였다. 세 가지 방법을 이용하여 변환한 결과 모두 오차가 있으므로 변환에 의한 피해 보상의 판정에는 무리가 있었으나, 그 중에서 발파시 동시에 측정된 수직방향 성분 PPV와 진동레벨의 변환식이 가장 실용적으로 판단되었다.

• 센서학회지
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• 제27권4호
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• pp.254-258
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• 2018

A Wi-Fi signal network (WSN) system is introduced in this paper. This system consists of several data-transmitting sensor modules and a data-receiving server. Each sensor module and the server contain a unique intranet IP address. A piezoelectric accelerometer with a bandwidth of 12 kHz, a 24-bit analog-digital converter with a sampling rate of 15.625 kS/s, a 32-bit microprocessor unit, and a 1-Mbps Wi-Fi module are used in the data-transmitting sensor module. A 300-Mbps router and a PC are used in the server. The system is verified using an accelerometer calibrator. The voltage output from the sensor is converted into 24-bit digital data and transmitted via the Wi-Fi module. These data are received by a Wi-Fi router connected to a PC. The input frequencies of the accelerometer calibrator (320 Hz, 640 Hz, and 1280 Hz) are used in the data transfer verification. The received data are compared to the data retrieved directly from the analog-to-digital converter used in the sensor module. The comparison shows that the developed system represents the original data considerably well. Theoretically, the system can acquire vibration signals from 600 sensor modules at an accelerometer bandwidth of 15.625 kHz. However, delay exists owing to software processes, multiplexing between sensor modules, and the use of non-real time operating system. Hence, it is recommended that this system may be used to acquire vibration signals with up to 10 kHz, which is approximately 70% of the theoretical maximum speed of the system. The system can be upgraded using parts with higher performance

• 신재생에너지
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• 제16권1호
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• pp.25-30
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• 2020

In this study, an SW for the analysis of the wind-turbine vibration characteristics was developed as an application of SaaS cloud infrastructure. A measurement system for power-performance, mechanical load, and gearbox vibration as type-test class was installed at a target MW-class wind turbine, and structural meta and raw data were then acquired into the cloud. Data processing algorithms were developed to provide cloud data to the SW. To operate the SW continuously, raw data was downloaded consistently based on the algorithms. During the SW test, an intermittent long time-delay occurred due to the communication load associated with frequent access to the cloud. To solve this, a compression service for the target raw data was developed in the cloud and more stable data processing was confirmed. Using the compression service, stable big data processing of wind turbines, including gearbox vibration analysis, is expected.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2000년도 춘계 학술발표회 논문집 Proceedings of EESK Conference-Spring
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• pp.218-225
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• 2000

This paper presents intermediate results of an on-going research for identification of the modal and the stiffness parameters of a bridge based on the ambient vibration data caused by the traffic loadings. The main algorithms consist of the random decrement method incorporating band-pass filters for estimation of the free vibration signals the cross spectral density method for identification of the modal parameters and the neural networks technique for estimation of the element-level stiffness changes. An experimental study is carried out on a scaled bridge model with a composite section subjected to various moving vehicle loadings. Vertical accelerations are measured at several locations on the girder. The estimated frequencies and mode shapes are found to be well-compared with those obtained from the impact tests. The estimated stiffness changes using the neural networks are found to be very good for the case with the simulated data. However the accuracy is found to be not quite satisfactory for the case with the experimental data particularly for the small value of the stiffness changes.

• Journal of Electrical Engineering and Technology
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• 제2권3호
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• pp.353-357
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• 2007

The bearing diagnostics method is presented in this paper using fuzzy inference based on vibration data. Both time-domain and frequency-domain features are used as input data for bearing fault detection. The Adaptive Network based Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) have been proposed to select the fuzzy model input and output parameters. Training results give the optimized fuzzy inference system for bearing diagnosis based on measured vibration data. The result is also tested with other sets of bearing data to illustrate the reliability of the chosen model.

• 한국포장학회지
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• 제4권2호
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• pp.39-44
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• 1998

We used recording device(EDR-3) to monitor the packages and the vehicle during shipment. Provided we did this enough times, we began to gain statistically valid information which could be used to describe the particular channel of distribution. The event was obviously changed from trip to trip, but in general we could have an idea of what to expect. Considerable amount of time and money were invested to record field data. Although not ideal, it was the best suited approach to gain information regarding a specific distribution channel. Based on the recorded field data, we could make our own packaging vibration testing specification through MIL-STD-810D(Guidance for development of laboratory dynamic test specification). This test specification was proved several times through field tests. As a result, we gained confidence in this revised vibration specification and come to know the development procedures of a laboratory dynamic test specification.

• Smart Structures and Systems
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• 제25권2호
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• pp.123-133
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• 2020

Compressive sensing (CS) is a newly developed data acquisition and processing technique that takes advantage of the sparse structure in signals. Normally signals in their primitive space or format are reconstructed from their compressed measurements for further treatments, such as modal analysis for vibration data. This approach causes problems such as leakage, loss of fidelity, etc., and the computation of reconstruction itself is costly as well. Therefore, it is appealing to directly work on the compressed data without prior reconstruction of the original data. In this paper, a direct approach for modal analysis of damped systems is proposed by decomposing the compressed measurements with an appropriate dictionary. The damped free vibration function is adopted to form atoms in the dictionary for the following sparse decomposition. Compared with the normally used Fourier bases, the damped free vibration function spans a space with both the frequency and damping as the control variables. In order to efficiently search the enormous two-dimension dictionary with frequency and damping as variables, a two-step strategy is implemented combined with the Orthogonal Matching Pursuit (OMP) to determine the optimal atom in the dictionary, which greatly reduces the computation of the sparse decomposition. The performance of the proposed method is demonstrated by a numerical and an experimental example, and advantages of the method are revealed by comparison with another such kind method using POD technique.