scholarly journals Tensile Damage Study of Wind Turbine Tower Material Q345 Based on an Acoustic Emission Method

Materials ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2120
Author(s):  
Xiao Tang ◽  
Lida Liao ◽  
Bin Huang ◽  
Cong Li
Keyword(s):  
Acoustic Emission ◽  
Wind Turbine ◽  
Energy Parameter ◽  
Sensitivity Factor ◽  
Cumulative Energy ◽  
Emission Signal ◽  
Damage Degree ◽  
Cumulative Duration ◽  
Wind Turbine Towers ◽  
Tendency Analysis

As essential load-bearing equipment to support the nacelle and blades, the tower is subjected to the whole wind turbine loading. This study proposes a new method of combining acoustic emission and normalized accumulation parameters to characterize wind turbine towers Q345 steel damage. First of all, tendency analysis of the acoustic emission signal parameter was conducted to determine damage degree during the damage stage. Secondly, we normalized the accumulation of amplitude and other parameters to compare the proportion of each accumulation parameter at different stages, while studying the spectra of common acoustic emission signals. Finally, comparing the differences and similarities of the normalized accumulation parameters between three different rates, we analyze the effect of rate on the normalized accumulation parameters. These results indicate that the normalized cumulative duration parameter is suitable for characterizing the yield damage occurrence, the normalized cumulative energy parameter is very sensitive to the fracture stage, the normalized cumulative energy parameter is least influenced by the loading rate, and the energy parameter is a sensitivity factor for normalized expression, which to realizes the stage of damage judgment.

Cumulative acoustic emission energy for damage detection in composites reinforced by carbon fibers within low-cycle fatigue regime at various displacement amplitudes and rates

2021 ◽  
pp. 096739112098570
Author(s):  
Mohammad Azadi ◽  
Mohsen Alizadeh ◽  
Seyed Mohammad Jafari ◽  
Amin Farrokhabadi
Keyword(s):  
Acoustic Emission ◽  
Carbon Fibers ◽  
Polymer Matrix Composites ◽  
Low Cycle Fatigue ◽  
Energy Parameter ◽  
Fatigue Tests ◽  
Matrix Cracking ◽  
Matrix Composites ◽  
Cycle Fatigue ◽  
Cumulative Energy

In the present article, acoustic emission signals were utilized to predict the damage in polymer matrix composites, reinforced by carbon fibers, in the low-cycle fatigue regime. Displacement-controlled fatigue tests were performed on open-hole samples, under different conditions, at various displacement amplitudes of 5.5, 6.0, 6.5 and 7.0 mm and also under various displacement rates of 25, 50, 100 and 200 mm/min. After acquiring acoustic emission signals during cycles, two characteristic parameters were used, including the energy and the cumulative energy. Obtained results implied that the energy parameter of acoustic emission signals could be used only for the macroscopic damage, occurring at more than 65% of normalized fatigue cycles under different test conditions. However, the cumulative energy could properly predict both microscopic and macroscopic defects, at least two failure types, including matrix cracking at first cycles and the fiber breakage at last cycles. Besides, scanning electron microscopy images proved initially such claims under all loading conditions.

Damage mode identification of composite wind turbine blade under accelerated fatigue loads using acoustic emission and machine learning

2019 ◽  
Vol 19 (4) ◽  
pp. 1092-1103 ◽  
Author(s):  
Pengfei Liu ◽  
Dong Xu ◽  
Jingguo Li ◽  
Zhiping Chen ◽  
Shuaibang Wang ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
Mode Identification ◽  
Emission Signal ◽  
Composite Blade ◽  
Acoustic Emission Sensors ◽  
Safety Precaution ◽  
Fatigue Loads

This article studies experimentally the damage behaviors of a 59.5-m-long composite wind turbine blade under accelerated fatigue loads using acoustic emission technique. First, the spectral analysis using the fast Fourier transform is used to study the components of acoustic emission signals. Then, three important objectives including the attenuation behaviors of acoustic emission waves, the arrangement of sensors as well as the detection and positioning of defect sources in the composite blade by developing the time-difference method among different acoustic emission sensors are successfully reached. Furthermore, the clustering analysis using the bisecting K-means method is performed to identify different damage modes for acoustic emission signal sources. This work provides a theoretical and technique support for safety precaution and maintaining of in-service blades.

scholarly journals Identifying critical failure information of thermal damaged sandstone through acoustic emission signal

2021 ◽  
Vol 18 (4) ◽  
pp. 558-566
Author(s):  
Weiqiang Zhang ◽  
Zhoujian Shi ◽  
Zuoquan Wang ◽  
Shaoteng Zhang
Keyword(s):  
Acoustic Emission ◽  
Critical Point ◽  
High Temperatures ◽  
Acoustic Emission Signal ◽  
Large Scale ◽  
B Value ◽  
Energy Curve ◽  
Cumulative Energy ◽  
Temperature Range ◽  
Emission Signal

Abstract The changes in the acoustic emission signals of sandstone after treatment at different high temperatures are examined in this study. The results show that there is a critical point on the cumulative energy curve of the acoustic emission signals (almost between 60 and 90% of the ratio of the loading time and the total loading time), which can be used to identify the failure of sandstone that has been damaged by exposure to a temperature of 900°C. As the temperature increases, the position of the critical point gradually changes, which indicates that high temperatures increase the plasticity of rock, and this gradually reduces the brittleness. The changes in b-value of acoustic emission shows that the transition behavior of rock from brittleness to plasticity is more obvious at temperatures higher than 600°C, and the large-scale micro cracking takes place at that temperature range, which is the main reason for the weakening and brittleness and the strengthening of plasticity of the sandstone.

Study on Monitoring Damage about Material of Blade in Wind Turbine Based on Acoustic Emission

2011 ◽  
Vol 201-203 ◽  
pp. 2753-2758 ◽  
Author(s):  
Xin Guang Zhao ◽  
Chang Zheng Chen ◽  
Bo Zhou ◽  
Xin Hong Liu
Keyword(s):  
Acoustic Emission ◽  
Wind Turbine ◽  
Damage Identification ◽  
Vibration Signal ◽  
Research Area ◽  
Time Frequency ◽  
Emission Signal ◽  
The Time Domain ◽  
New Research ◽  
Large Wind

The monitoring about key components of large wind turbine is important, especially blade. Damages in the material of blade in large wind turbine will cause strain and thus can produce acoustic emission. And this study is presently a new research area in china. This paper refers to the preliminary exploration of common fracture characteristic. Acoustic emission can be used to identify and diagnose the damage. Analyzing the acoustic emission characteristics of the damage in the wind turbine system is emphasized by the experiment. At first, the acoustic emission signals of two different damages and vibration signal of those are compared and analyzed to demonstrate the superiority of the time domain of acoustic emission signals based method for the damage identification. Then, the wavelet is introduced and its characteristics are illustrated. Finally, the wavelet is used to analyze and discuss the time-frequency features of the damage acoustic emission. Some frequency band of wavelets is recomposed, and the results can efficiently extract the features of acoustic emission signal and identify the damage. It offers a valuable reference to monitor the damage about blade of large wind turbine.

Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Low Temperatures ◽  
Cold Resistance ◽  
Emission Signal ◽  
Impact Bending ◽  
Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

scholarly journals Prediction of shallow bit position based on vibration signal monitoring of bit broken rock

2021 ◽  
Vol 17 (1) ◽  
pp. 155014772199170
Author(s):  
Jinping Yu ◽  
Deyong Zou
Keyword(s):  
Acoustic Emission ◽  
Time Domain ◽  
Vibration Signal ◽  
Rock Breaking ◽  
Drill String ◽  
Mean Square ◽  
Drilling Force ◽  
Emission Signal ◽  
Force Signal ◽  
The Mean

The speed of drilling has a great relationship with the rock breaking efficiency of the bit. Based on the above background, the purpose of this article is to predict the position of shallow bit based on the vibration signal monitoring of bit broken rock. In this article, first, the mechanical research of drill string is carried out; the basic changes of the main mechanical parameters such as the axial force, torque, and bending moment of drill string are clarified; and the dynamic equilibrium equation theory of drill string system is analyzed. According to the similarity criterion, the corresponding relationship between drilling process parameters and laboratory test conditions is determined. Then, the position monitoring test system of the vibration bit is established. The acoustic emission signal and the drilling force signal of the different positions of the bit in the process of vibration rock breaking are collected synchronously by the acoustic emission sensor and the piezoelectric force sensor. Then, the denoised acoustic emission signal and drilling force signal are analyzed and processed. The mean value, variance, and mean square value of the signal are calculated in the time domain. The power spectrum of the signal is analyzed in the frequency domain. The signal is decomposed by wavelet in the time and frequency domains, and the wavelet energy coefficients of each frequency band are extracted. Through the wavelet energy coefficient calculated by the model, combined with the mean, variance, and mean square error of time-domain signal, the position of shallow buried bit can be analyzed and predicted. Finally, by fitting the results of indoor experiment and simulation experiment, it can be seen that the stress–strain curve of rock failure is basically the same, and the error is about 3.5%, which verifies the accuracy of the model.

Sign in / Sign up

Export Citation Format

Share Document