Portable noise time‐frequency characteristic monitoring system for environmental assessment of power transformer rooms

2021 ◽  
Author(s):  
Yadong Fan ◽  
Bing Xu ◽  
Jianguo Wang ◽  
Jinglu Wu ◽  
Yang Ding ◽  
...  
Keyword(s):  
Monitoring System ◽  
Environmental Assessment ◽  
Frequency Characteristic ◽  
Power Transformer ◽  
Time Frequency

scholarly journals Time-Frequency Analysis and Application of a Vibration Signal of Tunnel Excavation Blasting Based on CEEMD-MPE-HT

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chen-yang Ma ◽  
Li Wu ◽  
Miao Sun ◽  
Qing Yuan
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Band ◽  
Frequency Analysis ◽  
Frequency Characteristic ◽  
Permutation Entropy ◽  
Natural Vibration Frequency ◽  
Characteristic Parameters ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

The traditional empirical mode decomposition method cannot accurately extract the time-frequency characteristic parameters contained in the noisy seismic monitoring signals. In this paper, the time-frequency analysis model of CEEMD-MPE-HT is established by introducing the multiscale permutation entropy (MPE), combining with the optimized empirical mode decomposition (CEEMD) and Hilbert transform (HT). The accuracy of the model is verified by the simulation signal mixed with noise. Based on the project of Loushan two-to-four in situ expansion tunnel, a CEEMD-MPE-HT model is used to extract and analyze the time-frequency characteristic parameters of blasting seismic signals. The results show that the energy of the seismic wave signal is mainly concentrated in the frequency band above 100 Hz, while the natural vibration frequency of the adjacent existing tunnel is far less than this frequency band, and the excavation blasting of the tunnel will not cause the resonance of the adjacent existing tunnel.

Denoising Vibration Signals of Food Refrigerant Air Compressor Based on Wavelet Transform

2010 ◽  
Vol 439-440 ◽  
pp. 1037-1041 ◽  
Author(s):  
Yan Jue Gong ◽  
Zhao Fu ◽  
Hui Yu Xiang ◽  
Li Zhang ◽  
Chun Ling Meng
Keyword(s):  
Wavelet Transform ◽  
Frequency Characteristic ◽  
Vibration Signal ◽  
Wavelet Denoising ◽  
Signal Denoising ◽  
Denoising Method ◽  
Vibration Signals ◽  
Air Compressor ◽  
Time Frequency ◽  
Soft Threshold

On the basis of wavelet denoising and its better time-frequency characteristic, this paper presents an effective vibration signal denoising method for food refrigerant air compressor. The solution of eliminating strong noise is investigated with the combination of soft threshold and exponential lipschitza. The good denoising results show that the presented method is effective for improving the signal noise ratio and builds the good foundation for further extraction of the vibration signals.

Construction of the Health Monitoring System of a Machine Structure

2009 ◽  
Author(s):  
Masanori Shintani ◽  
Keita Masaki
Keyword(s):  
Monitoring System ◽  
Health Monitoring ◽  
Normal Vibration ◽  
Wigner Distribution ◽  
Random Noise ◽  
Unsteady State ◽  
High Frequency Region ◽  
Health Monitoring System ◽  
Time Frequency ◽  
Vibration Wave

When big power like an earthquake acts at the place that the machine is normally operating, abnormalities may occur to a machine. If the machine is operated without finding abnormally, danger may attain to mechanical fatal damage and a mechanical work pursuer. Therefore, detecting in the situation where mechanical abnormalities are operated is very important as a health monitoring system. In this research, the system that takes in the vibration wave on the rotation part of the machine currently rotated is constructed. A vibration wave is analyzed using time-frequency analysis (STFT, the Wigner distribution, wave let analysis) From the result, the system by which normal vibration and abnormal vibration are evaluated is constructed from random noise. As a result of comparing normal vibration with abnormal vibration, the peak may have occurred in the high frequency region. It turned out that the analysis result of an unsteady state has a peak 2000Hz–3000Hz of frequency domains, and 4000Hz–5000Hz also in STFT and Wigner distribution. I think that this becomes the important tool which distinguishes the stationary state and unsteady state in health monitoring.

Research on the Vibration Condition On-Line Monitoring System for EHV Power Transformer

2015 ◽  
Vol 734 ◽  
pp. 675-679
Author(s):  
Wan Qing Li ◽  
Wei Wang ◽  
Le Ting Lin ◽  
Bei Min Xie ◽  
Ming Chao Xia ◽  
...  
Keyword(s):  
Data Analysis ◽  
Monitoring System ◽  
Power Transformer ◽  
Vibration Signal ◽  
Acceleration Signal ◽  
Transformer Winding ◽  
On Line ◽  
Characteristic Values ◽  
Harmonic Components ◽  
Extra High Voltage

The paper introduces a design scheme for the Extra High Voltage (EHV) transformer condition on-line monitoring system, which is based on the collection and analysis of the transformer winding and core vibration signals. This system is composed of vibration acceleration signal sensors and the signal analyzing computer where the collected vibration signal is saved and processed. The analyzing computer can accomplish the missions of data acquisition control, data analysis and the historical data query. Vibration characteristic values of transformer winding and core include peak to peak value, spectrum, kurtosis, and the amplitude 100Hz component and its higher harmonic components. They are extracted, and the characteristic trend curves are drawn by data analysis, so that EHV transformer on-line monitoring and fault diagnosis are accomplished.

Sign in / Sign up

Export Citation Format

Share Document