Fault source location of wind turbine based on heterogeneous nodes complex network

2021 ◽  
Vol 103 ◽  
pp. 104300
Author(s):  
Kai Zhang ◽  
Baoping Tang ◽  
Lei Deng ◽  
Xiaoxia Yu ◽  
Jing Wei
Keyword(s):  
Wind Turbine ◽  
Complex Network ◽  
Source Location ◽  
Heterogeneous Nodes ◽  
Fault Source

scholarly journals Rotating equipment fault noise location based on adaptive beamforming algorithm

2021 ◽  
Vol 2113 (1) ◽  
pp. 012042
Author(s):  
Yongshao Xu ◽  
Bingzheng Liu ◽  
Haotian Shang ◽  
Mingduo Wang
Keyword(s):  
Microphone Array ◽  
State Parameter ◽  
Noise Signal ◽  
Source Location ◽  
Adaptive Beamforming ◽  
Rotating Machinery ◽  
Acoustic Sensor ◽  
Unsteady State ◽  
Beamforming Algorithm ◽  
Fault Source

Abstract Rotating machinery often produces continuous impact during operation due to the change of load and speed, which shows the characteristics of unsteady state and time-varying. Its working state can not be comprehensively judged by a single vibration state parameter. Therefore, this paper proposes to use acoustic sensors to collect the fault noise signal of rotating machinery, and use the whole column of sensors to detect the fault noise signal. Based on the microphone array, this paper studies the adaptive beamforming algorithm (MVDR) to locate the fault source of rotating machinery in space. The effect of fault source location is verified by simulation and equipment measurement experiments. The acoustic sensor does not in contact with the equipment, which will not damage the generator set, but also provide more effective information for fault source location and fault diagnosis and analysis.

Acoustic Emission Attenuation and Source Location of Resin Matrix for Wind Turbine Blade Composites

2014 ◽  
Vol 912-914 ◽  
pp. 36-39 ◽  
Author(s):  
Yan Rong Pang ◽  
Zhi Hui Lv ◽  
Xiao Min Liang ◽  
Han Chang Chai ◽  
Ruo Chen Liu ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Source Location ◽  
Wind Turbine Blade ◽  
Resin Matrix ◽  
Destructive Testing ◽  
Testing Technology ◽  
Ae Signal ◽  
The One

In recent years, acoustic emission (AE) testing technology is the one of the most important non-destructive testing (NDT) methods. The characteristics can be described by AE signals, including the location, nature and severity. In order to obtain the basic data for monitoring the wind turbine blade composite structure, the experiment adopted Φ0.5 mm lead pencil as artificial acoustic emission source and measured AE parameters, attenuation and source location of resin matrix for wind turbine blade. This paper introduced linear location and two-dimensional positioning technology of time arrival location method about the burst AE signal. The result shows that the location of AE source basically reflects the location of stimulation AE source, the location of AE source for resin matrix can agree well with the simulated location of AE source, the more close to the middle area, the more accurate location.

scholarly journals Quantifying the Robustness of Complex Networks with Heterogeneous Nodes

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2769
Author(s):  
Prasan Ratnayake ◽  
Sugandima Weragoda ◽  
Janaka Wansapura ◽  
Dharshana Kasthurirathna ◽  
Mahendra Piraveenan
Keyword(s):  
Complex Network ◽  
Real World ◽  
Fitness Function ◽  
Co2 Exchange ◽  
Network Simulator ◽  
Network Efficiency ◽  
Scale Free ◽  
Robustness Measure ◽  
Heterogeneous Nodes ◽  
Exchange Network

The robustness of a complex network measures its ability to withstand random or targeted attacks. Most network robustness measures operate under the assumption that the nodes in a network are homogeneous and abstract. However, most real-world networks consist of nodes that are heterogeneous in nature. In this work, we propose a robustness measure called fitness-incorporated average network efficiency, that attempts to capture the heterogeneity of nodes using the `fitness’ of nodes in measuring the robustness of a network. Further, we adopt the same measure to compare the robustness of networks with heterogeneous nodes under varying topologies, such as the scale-free topology or the Erdős–Rényi random topology. We apply the proposed robustness measure using a wireless sensor network simulator to show that it can be effectively used to measure the robustness of a network using a topological approach. We also apply the proposed robustness measure to two real-world networks; namely the CO2 exchange network and an air traffic network. We conclude that with the proposed measure, not only the topological structure, but also the fitness function and the fitness distribution among nodes, should be considered in evaluating the robustness of a complex network.

In a spin [wind turbine industry]

2003 ◽  
Vol 17 (4) ◽  
pp. 16
Author(s):  
S. Peace
Keyword(s):  
Wind Turbine
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