scholarly journals Development of real-time nanoscale contaminant particle characteristics diagnosis system in vacuum condition

2015 ◽  
Vol 2 (3) ◽  
pp. 11-15
Author(s):  
Sang-Woo Kang ◽  
Taesung Kim
Keyword(s):  
Real Time ◽  
Vacuum Condition ◽  
Diagnosis System ◽  
Particle Characteristics ◽  
Contaminant Particle

Real-time Diagnosis System Development of Common Rail Diesel Based on Expert System

2005 ◽  
Author(s):  
Guomin Song ◽  
Fuyuan Yang ◽  
Minggao Ouyang ◽  
Jun Li ◽  
Linfeng Hu
Keyword(s):  
Expert System ◽  
Real Time ◽  
System Development ◽  
Common Rail ◽  
Diagnosis System ◽  
Time Diagnosis

Based on the Online Monitoring and Fault Diagnosis System of the Main Hoist for Mine

2010 ◽  
Vol 42 ◽  
pp. 250-254
Author(s):  
Zhi Yong Pan ◽  
Quan Cai Wang ◽  
Wei Hong Ren
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Online Monitoring ◽  
Signal Acquisition ◽  
Diagnosis System ◽  
Current Voltage ◽  
Fault Diagnosis System ◽  
Real Time Database ◽  
Signal Acquisition And Processing ◽  
Main Hoist

According to the reality, an online monitoring and fault diagnosis system of the main hoist for Mine was designed in this article. The system adopts the signal acquisition and processing, fault diagnosis, Web visualization, network real-time database and other related technologies, Real-time monitoring the current, voltage, temperature, speed, vibration and other parameters of the main elevators to Achieve the goals that Increasing efficiency by downsizing, protecting the safe operation of equipment, reducing the maintenance costs.

scholarly journals A real-time fault diagnosis system for high-speed power system protection based on machine learning algorithms

2020 ◽  
Vol 10 (6) ◽  
pp. 6122
Author(s):  
Elmahdi Khoudry ◽  
Abdelaziz Belfqih ◽  
Tayeb Ouaderhman ◽  
Jamal Boukherouaa ◽  
Faissal Elmariami
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Power System ◽  
Real Time ◽  
High Speed ◽  
Learning Algorithms ◽  
Time Estimation ◽  
Machine Learning Algorithms ◽  
Diagnosis System ◽  
Fault Diagnosis System

This paper puts forward a real-time smart fault diagnosis system (SFDS) intended for high-speed protection of power system transmission lines. This system is based on advanced signal processing techniques, traveling wave theory results, and machine learning algorithms. The simulation results show that the SFDS can provide an accurate internal/external fault discrimination, fault inception time estimation, fault type identification, and fault location. This paper presents also the hardware requirements and software implementation of the SFDS.

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