Effect of static errors in time-domain analyzers on the accuracy of determination of S-parameters

1989 ◽  
Vol 32 (7) ◽  
pp. 632-635
Author(s):  
A. A. -B. Akhmadov ◽  
E. M. Levin
Keyword(s):  
Time Domain ◽  
S Parameters

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.

scholarly journals Signal Processing Technique for Time Domain Measurement of S-Parameters

2016 ◽  
Vol 15 (2) ◽  
pp. 105-114 ◽  
Author(s):  
Isabela M. Nobre ◽  
Julio L. Nicolini ◽  
Joaquim D. Garcia ◽  
Marbey Mosso
Keyword(s):  
Signal Processing ◽  
Time Domain ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
S Parameters

scholarly journals Determination of the time origin by 
the maximum entropy method in 
time-domain terahertz emission spectroscopy

2010 ◽  
Vol 18 (15) ◽  
pp. 15853 ◽  
Author(s):  
Takeya Unuma ◽  
Yusuke Ino ◽  
Makoto Kuwata-Gonokami ◽  
Erik M. Vartiainen ◽  
Kai-Erik Peiponen ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Time Domain ◽  
Emission Spectroscopy ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
Terahertz Emission ◽  
Terahertz Emission Spectroscopy ◽  
Time Origin
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