THE INFLUENCE OF CHANGES IN THE ROTATIONAL SPEED OF THE SHAFT JOURNAL SLIDE BEARING ON THE ACOUSTIC EMISSION SIGNAL

2016 ◽  
Vol 23 (2) ◽  
pp. 121-128
Author(s):  
Grzegorz Gesella ◽  
Lech Murawski
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Rotational Speed ◽  
Emission Signal ◽  
Slide Bearing ◽  
Shaft Journal

scholarly journals IMPACT OF ROTATIONAL SPEED OF COMPOSITE FRICTION PAIR ON ENERGY ACCUMULATION IN ACOUSTIC EMISSION SIGNAL FORMATION

Aviation ◽  
2013 ◽  
Vol 17 (4) ◽  
pp. 129-136
Author(s):  
Sergey Filonenko ◽  
Aleksandr Kosmach
Keyword(s):  
Acoustic Emission ◽  
Composite Material ◽  
Total Energy ◽  
Acoustic Emission Signal ◽  
Rotational Speed ◽  
Friction Pair ◽  
Average Energy ◽  
Energy Accumulation ◽  
Emission Signal

The paper considers the results of modelling acoustic emission signals that form under variations in the rotational speed of a friction pair made of a composite material. It is found that the energy accumulation in acoustic emission signals that form relative to time takes place in accordance with linear law under normal wear of a friction pair. If the rotational speed of a friction pair increases, the rates of accumulated average energy and accumulated total energy of the acoustic emission signals that form also grow. The resulting acoustic emission signals have both accumulated average energy and accumulated total energy. A comparison of these two types of energy is conducted. It is shown that the application of data averaging reduces the rate of the accumulated energy of the acoustic emission signals that form.

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]

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