A comparative analysis on two gear tooth materials for low speed and high torque transmission

2018 ◽  
Vol 73 (3) ◽  
pp. 79-83 ◽  
Author(s):  
Sushovan Chatterjee ◽  
Krishanu Gupta
Keyword(s):  
Comparative Analysis ◽  
Gear Tooth ◽  
Low Speed ◽  
Torque Transmission ◽  
High Torque

Use of Small-Pin Distraction and Low-Speed, High-Torque Burring for Talar Osteochondral Allografts: Technique Tip

2021 ◽  
pp. 107110072110031
Author(s):  
Ryan O’Leary ◽  
Ian M. Foran ◽  
David J. Dalstrom
Keyword(s):  
Expert Opinion ◽  
Level Of Evidence ◽  
Low Speed ◽  
Evidence Level ◽  
High Torque ◽  
Osteochondral Allografts

Level of Evidence: Level V, expert opinion.

Comparative analysis of the magnetoelectric drive with linear drives of low-speed single-stage piston units

2020 ◽  
Author(s):  
A. A. Tatevosyan ◽  
S. S. Busarov ◽  
R. E. Kobyl'skiy ◽  
T. A. Zamiralova
Keyword(s):  
Comparative Analysis ◽  
Single Stage ◽  
Low Speed

The Feature Extraction Method of Gear Magnetic Memory Signal

2013 ◽  
Vol 819 ◽  
pp. 206-211
Author(s):  
Yong Gang Xu ◽  
Zhi Cong Xie ◽  
Lin Li Cui ◽  
Jing Wang
Keyword(s):  
Gear Tooth ◽  
Low Frequency ◽  
Magnetic Memory ◽  
Magnetic Signal ◽  
Heavy Load ◽  
Low Speed ◽  
Feature Extraction Method ◽  
Test Technology ◽  
Time Scale Decomposition ◽  
Memory Signals

Magnetic memory test technology is a new nondestructive testing technique, which is able to detect of the stress concentration area and potential fault of low speed and heavy load gear. Because the magnetic memory signals are easy to be disturbed by various sources of noises, a new method based on the intrinsic time-scale decomposition (ITD) is proposed to achieve the extraction of magnetic memory signal. Firstly, the magnetic memory signals are decomposed into several proper rotation components (PRC) and a trend component by ITD. Then reconstruct the first four order PRCs to eliminate the low frequency cyclic composition of magnetic memory signal and magnetic noise. Finally, the magnetic signal strengths of each gear tooth root are extracted using cycle average and local statistic method. The results of Experiments show that the method is suitable to pick up effective ingredients of signal to extract signal feature and has important application value in potential fault diagnosis of low speed and heavy load gearbox.

scholarly journals Low Speed Estimation of Sensorless DTC Induction Motor Drive Using MRAS with Neuro Fuzzy Adaptive Controller

2018 ◽  
Vol 8 (5) ◽  
pp. 2691
Author(s):  
Mini R ◽  
Shabana Backer P. ◽  
B. Hariram Satheesh ◽  
Dinesh M. N
Keyword(s):  
Comparative Analysis ◽  
Induction Motor ◽  
Reactive Power ◽  
Adaptive Controller ◽  
Operating Conditions ◽  
Estimation Methods ◽  
Speed Estimation ◽  
Loop Model ◽  
Low Speed ◽  
Rotor Flux

<p>This paper presents a closed loop Model Reference Adaptive system (MRAS) observer with artificial intelligent Nuero fuzzy controller (NFC) as the adaptation technique to mitigate the low speed estimation issues and to improvise the performance of the Sensorless Direct Torque Controlled (DTC) Induction Motor Drives (IMD). Rotor flux MRAS and reactive power MRAS with NFC is explored and detailed analysis is carried out for low speed estimation. Comparative analysis between rotor flux MRAS and reactive power MRAS with PI as well as NFC as adaptive controller is performed and results are presented in this paper. The comparative analysis among these four speed estimation methods shows that reactive power MRAS with NFC as adaptation mechanism shows reduced speed estimation error and actual speed error at steady state operating conditions when the drive is subjected to low speed operation. Simulation carried out using MATLAB-Simulink software to validate the performance of the drive especially at low speeds with rated and variable load conditions.</p>

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