scholarly journals Recent Developments in Fault Detection and Power Loss Estimation of Electrolytic Capacitors

2010 ◽  
Vol 25 (1) ◽  
pp. 33-43 ◽  
Author(s):  
A. Braham ◽  
A. Lahyani ◽  
P. Venet ◽  
N. Rejeb
Keyword(s):  
Fault Detection ◽  
Power Loss ◽  
Loss Estimation ◽  
Electrolytic Capacitors ◽  
Recent Developments

Correction: Cryogenic Characterization and Modeling of Silicon Superjunction MOSFET for Power Loss Estimation

2020 ◽  
Author(s):  
Md Maksudul Hossain ◽  
Arman Ur Rashid ◽  
Rosten Sweeting ◽  
Yuqi Wei ◽  
Haider Mhiesan ◽  
...  
Keyword(s):  
Power Loss ◽  
Loss Estimation

Distribution networks nontechnical power loss estimation: A hybrid data-driven physics model-based framework

2020 ◽  
Vol 186 ◽  
pp. 106397
Author(s):  
Arturo S. Bretas ◽  
Aquiles Rossoni ◽  
Rodrigo D. Trevizan ◽  
Newton G. Bretas
Keyword(s):  
Power Loss ◽  
Distribution Networks ◽  
Loss Estimation ◽  
Data Driven ◽  
Model Based ◽  
Hybrid Data ◽  
Physics Model

Simplified Power Loss Model for Aluminum Electrolytic Capacitors in Single-Phase Inverters

2020 ◽  
Vol 35 (5) ◽  
pp. 4452-4456
Author(s):  
Yi Liu ◽  
Huai Wang ◽  
Meng Huang ◽  
Xiaoming Zha ◽  
Guorong Zhu
Keyword(s):  
Power Loss ◽  
Single Phase ◽  
Loss Model ◽  
Electrolytic Capacitors ◽  
Aluminum Electrolytic Capacitors

Modelling HTc superconductors for AC power loss estimation

1997 ◽  
Vol 33 (2) ◽  
pp. 1568-1571 ◽  
Author(s):  
J.K. Sykulski ◽  
R.L. Stoll ◽  
A.E. Mahdi ◽  
C.P. Please
Keyword(s):  
Power Loss ◽  
Loss Estimation ◽  
Ac Power ◽  
Htc Superconductors

Vibration Analysis Based Feature Extraction for Bearing Fault Detection

2012 ◽  
Vol 197 ◽  
pp. 124-128
Author(s):  
Jie Liu ◽  
Chun Sheng Yang ◽  
Qing Feng Lou
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Rolling Element Bearings ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Rotary Machine ◽  
Catastrophic Failures ◽  
Rolling Element ◽  
Significant Research ◽  
Recent Developments

Rolling element bearings are widely used in various rotary machines. Most rotary machine failures are attributed to unexpected bearing faults. Accordingly, reliable bearing fault detection is critically needed in industries to prevent these machines’ performance degradation, malfunction, or even catastrophic failures. Feature extraction plays an important role in bearing fault detection and significant research efforts have thus far been devoted to this subject from both academia and industry. This paper intends to provide a brief review of the recent developments in feature extraction for bearing fault detection, and the focus will be placed on the advances in methods for dealing with the nonstationary characteristics of bearing fault signatures.

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