Research on Extraction Method of Fatigue State Magneto Acoustic Emission Characteristic Parameters Based on CEEMD

Author(s):  
Sha Wu ◽  
Gongtian Shen ◽  
Zenghua Liu ◽  
Yongna Shen ◽  
Zhinong Li
2013 ◽  
Vol 318 ◽  
pp. 108-113
Author(s):  
Ji Yong Xu ◽  
Jun Li Zhao ◽  
Bing Zhao ◽  
Ying Qing Shao

Crack position of metal drawing parts molding was analyzed by the BP neural network. First analysis of the drawing parts forming process may crack in different position. The BP neural network location identification was introduced in the basic process. 11 characteristic parameters from the drawing parts may crack position were gathered by acoustic emission signal acquisition system of deep drawing process. Then the BP neural network was designed rational, and carried out appropriate conduct to train and test. Establishing deep drawing parts of the relations between the different positions crack acoustic emission characteristic parameters and the corresponding position. Crack location was identified, in order to achieve the purpose of positioning the work piece forming process. The better method of acoustic emission location issues are resolved, metal deep drawing forming of crack location identification for basis. Provide the basis for metal drawing parts forming crack location identification.


2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Minbo Zhang ◽  
Li Cui ◽  
Wenjun Hu ◽  
Jinlei Du ◽  
Zhen Zhang ◽  
...  

In this study, triaxial load failure experiments of coal samples under different strain rates and different confining pressure unloading rates were carried out using an RTX-1000 rock triaxial apparatus, and the acoustic emission characteristic parameters of a Micro-II acoustic emission imaging acquisition instrument were used to study the acoustic emission characteristics and damage deformation law of coals under different conditions. Damage models were constructed on the basis of the characteristic parameters to analyze the damage law of coal samples. Experimental results show that the acoustic emission (AE) counts and AE energy of the coal samples decrease, but the peak AE counts and peak AE energy increase with the increase in strain rates. The cumulative AE counts decrease from 9902 times to 6899 times, the peak counts increase from 209 times to 431 times, the cumulative AE energy decreases from 6986 aJ to 3786 aJ, and the peak AE energy increases from 129 aJ to 312 aJ. The overall level of the AE count rates and the AE energy of the coal samples decrease, but the peak AE counts and peak AE energy increase with the increase in unloading rates. The cumulative AE counts decrease from 18,689 times to 16,842 times, the peak AE count rates increase from 245 times/s to 535 times/s, the cumulative AE energy decreases from 9846 aJ to 7430 aJ, and the peak energy increases from 257 aJ to 587 aJ. The damage models are constructed on the basis of AE counts, and the comparative experimental and theoretical curves are analyzed to obtain a higher fitness close to 1. The damage threshold increases from 0.30 to 0.50 and from 0.34 to 0.55, and the damage amount increases from 0.50 to 0.60 and from 0.34 to 0.62 with the increase in strain rates and unloading rates. The research results have practical significance for revealing the mechanism of disaster occurrence in actual engineering excavation and proposing engineering measures to prevent coal rock damage and disaster occurrence.


Measurement ◽  
2017 ◽  
Vol 103 ◽  
pp. 311-320 ◽  
Author(s):  
Kuanfang He ◽  
Xiangnan Liu ◽  
Qing Yang ◽  
Yong Chen

2020 ◽  
Vol 10 (22) ◽  
pp. 8240
Author(s):  
Jiaoyan Huang ◽  
Aiguo Xia ◽  
Shenao Zou ◽  
Cong Han ◽  
Guoan Yang

Effective and accurate diagnosis of engine health is key to ensuring the safe operation of engines. Inlet distortion is due to the flow or the pressure variations. In the paper, an acoustic emission (AE) online monitoring technique, which has a faster response time compared with the ordinary vibration monitoring technique, is used to study the inlet distortion of an engine. The results show that with the deterioration of the inlet distortion, the characteristic parameters of AE signals clearly evolve in three stages. Stage I: when the inlet distortion J ≤ 30%, the characteristic parameters of the AE signal increase as J increases and the amplitude saturates at J = 23%, faster than the other three parameters (the strength, the root mean square (RMS), and the average signal level (ASL)). Stage II: when the inlet distortion 30% < J ≤ 43.64%, all the parameters saturate with only slight fluctuations as J increases and the engine works in an unstable statue. Stage III: when the inlet distortion J > 43.64%, the engine is prone to surge. Furthermore, an intelligent recognition method of the engine inlet distortion based on a unit parameter entropy and the back propagation (BP) neural network is constructed. The recognition accuracy is as high as 97.5%, and this method provides a new approach for engine health management.


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