Systematic evaluation of PHEMT large signal characteristics using time domain measurements

2002 ◽  
Author(s):  
D.G. Morgan ◽  
P.J. Tasker ◽  
G.D. Edwards ◽  
A. Phillips
Keyword(s):  
Time Domain ◽  
Systematic Evaluation ◽  
Large Signal ◽  
Signal Characteristics

MESFET large-signal model based on small-signal measurements for time-domain CAD

1988 ◽  
Vol 24 (15) ◽  
pp. 973 ◽  
Author(s):  
A. Ouslimani ◽  
G. Vernet ◽  
J.C. Henaux ◽  
P. Crozat ◽  
R. Adde
Keyword(s):  
Time Domain ◽  
Small Signal ◽  
Large Signal ◽  
Signal Model ◽  
Model Based

An Ensemble Dynamic Time Domain Averaging Method for Gear Fault Detection without Tachometer

2013 ◽  
Vol 572 ◽  
pp. 439-442
Author(s):  
Hui Fang Xiao ◽  
Xiao Jun Zhou ◽  
Yi Min Shao
Keyword(s):  
Fault Detection ◽  
Time Domain ◽  
Time Warping ◽  
Mode Decomposition ◽  
Signal Characteristics ◽  
Gear Fault ◽  
Speed Fluctuation ◽  
Signal Simulation ◽  
Time Domain Averaging ◽  
Dynamic Time

Time Domain Averaging (TDA) has been widely used for fault detection. However, it cannot reveal signal characteristics accurately in conditions of speed fluctuation and no tachometer. Empirical mode decomposition (EMD) helps to extract physically meaningful components from the singles. Dynamic Time Warping (DTW) can solve inconsistence in signal lengths per rotation due to speed fluctuation. Utilizing the advantages of EMD, DTW and TDA, an ensemble dynamic-time domain averaging (ED-TDA) algorithm is proposed for gear fault detection without tachometer. First, the selected intrinsic mode function (IMF) and the envelop signals are equal-spaced intercepted. Then, the phase accumulation error among the envelop signal segments are estimated by the DTW, which are further used to compensate the IMF segments. Finally, the compensated IMF segments are averaged to obtain the feature signal. Simulation and experimental results validate the efficiency of the algorithm in extracting feature signal from collected speed fluctuating signal without tachometer.

Time-Domain Fault Diagnosis Method of Mechanical and Electrical Equipment Based Improved Dynamic Time Wraping

2016 ◽  
Vol 693 ◽  
pp. 1539-1544 ◽  
Author(s):  
Zhi Wu Shang ◽  
Zhen Wu Liu ◽  
Ya Feng Li ◽  
Tai Yong Wang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Time Domain ◽  
Dynamic Time Warping ◽  
Statistical Parameter ◽  
Electrical Equipment ◽  
Time Warping ◽  
Trend Prediction ◽  
Signal Characteristics ◽  
Dynamic Time

Dynamic time warping used in speech recognition widely was migrated to fault feature extraction and diagnosis in time domain. Integration of phase compensation, slope weighted, derivative, sliding window connection, fast dynamic time planning method is applied to dynamic time warping method. And a new method of time-domain signal feature extraction and fault diagnostic based on improved dynamic time warping method of mechanical and electrical equipment was proposed. Identification and localization of fault signal characteristics may be done by improving dynamic time warping method to obtain a residual signal sequences with fault characterized sidebands and selecting the statistical characteristic parameters such as peak, RMS, kurtosis spectrum to complete identification and localization of fault signal characteristics. New time-domain fault trend prediction method of mechanical and electrical equipment was established based on new statistical parameter Thikat. A new idea and target was provided for fault diagnosis of mechanical and electrical equipment.

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