Determination of fracture initiation in hydride blisters using acoustic emission

1988 ◽  
Vol 19 (9) ◽  
pp. 2247-2257 ◽  
Author(s):  
M. P. Puls
Keyword(s):  
Acoustic Emission ◽  
Fracture Initiation

Determination of Initial Crack Strength of Silicon Die Using Acoustic Emission Technique

2015 ◽  
Vol 44 (7) ◽  
pp. 2497-2506 ◽  
Author(s):  
Pei-Chi Chen ◽  
Yen-Fu Su ◽  
Shin-Yueh Yang ◽  
Steven Y. Liang ◽  
Kuo-Ning Chiang
Keyword(s):  
Acoustic Emission ◽  
Initial Crack ◽  
Acoustic Emission Technique

Classification of journal bearing friction states based on acoustic emission signals

2018 ◽  
Vol 85 (6) ◽  
pp. 434-442 ◽  
Author(s):  
Noushin Mokhtari ◽  
Clemens Gühmann
Keyword(s):  
Acoustic Emission ◽  
Time Domain ◽  
Journal Bearing ◽  
Prediction Method ◽  
Support Vector ◽  
Mechatronic Systems ◽  
Prediction Time ◽  
Ae Signals ◽  
Bearing Friction

Abstract For diagnosis and predictive maintenance of mechatronic systems, monitoring of bearings is essential. An important building block for this is the determination of the bearing friction condition. This paper deals with the possibility of monitoring different journal bearing friction states, such as mixed and fluid friction, and examines a new approach to distinguish between different friction intensities under several speed and load combinations based on feature extraction and feature selection methods applied on acoustic emission (AE) signals. The aim of this work is to identify separation effective features of AE signals to subsequently classify the journal bearing friction states. Furthermore, the acquired features give information about the mixed friction intensity, which is significant for remaining useful lifetime (RUL) prediction. Time domain features as well as features in the frequency domain have been investigated in this work. To increase the sensitivity of the extracted features the AE signals were transformed to the frequency-time-domain using continuous wavelet transform (CWT). Significant frequency bands are determined to separate different friction states more effective. A support vector machine (SVM) is used to classify the signals into three different friction classes. In the end the idea for an RUL prediction method by using the already determined information is given and explained.

Technique for the Determination of the Critical Points under Acoustic Emission Tribological Tests

2017 ◽  
Vol 53 (15) ◽  
pp. 1506-1512 ◽  
Author(s):  
I. A. Rastegaev ◽  
D. L. Merson ◽  
A. Yu. Vinogradov ◽  
A. V. Danyuk
Keyword(s):  
Acoustic Emission ◽  
Critical Points ◽  
Tribological Tests

Determination of Damage Levels of Composite Plates after Low Velocity Impacts Using Acoustic Emission

2006 ◽  
pp. 253-258
Author(s):  
Timothy P. Bradshaw ◽  
M.J. Eaton ◽  
Rhys Pullin ◽  
Sam L. Evans ◽  
C.A. Featherston
Keyword(s):  
Acoustic Emission ◽  
Composite Plates ◽  
Low Velocity
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