scholarly journals Chaos Synchronization Based Novel Real-Time Intelligent Fault Diagnosis for Photovoltaic Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Chin-Tsung Hsieh ◽  
Her-Terng Yau ◽  
Jen Shiu
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Chaos Synchronization ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Diagnosis System ◽  
Fault Features ◽  
Voltage Signal ◽  
Fault Diagnosis System ◽  
Solar Photovoltaic System

The traditional solar photovoltaic fault diagnosis system needs two to three sets of sensing elements to capture fault signals as fault features and many fault diagnosis methods cannot be applied with real time. The fault diagnosis method proposed in this study needs only one set of sensing elements to intercept the fault features of the system, which can be real-time-diagnosed by creating the fault data of only one set of sensors. The aforesaid two points reduce the cost and fault diagnosis time. It can improve the construction of the huge database. This study used Matlab to simulate the faults in the solar photovoltaic system. The maximum power point tracker (MPPT) is used to keep a stable power supply to the system when the system has faults. The characteristic signal of system fault voltage is captured and recorded, and the dynamic error of the fault voltage signal is extracted by chaos synchronization. Then, the extension engineering is used to implement the fault diagnosis. Finally, the overall fault diagnosis system only needs to capture the voltage signal of the solar photovoltaic system, and the fault type can be diagnosed instantly.

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.

The Fault Recognition of Motor Based on the Fusion of Neural Network and D-S Evidence Theory

2012 ◽  
Vol 157-158 ◽  
pp. 861-864 ◽  
Author(s):  
Hai Lian Du ◽  
Zhan Feng Wang ◽  
Feng Lv ◽  
Tao Xin
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Real Time ◽  
Evidence Theory ◽  
Automatic Identification ◽  
Fusion Algorithm ◽  
Independent Evidence ◽  
Diagnosis System ◽  
Fault Recognition ◽  
Fault Diagnosis System

In order to reflect the motor from various aspects and realize the motor system state failure mode automatic identification and accurate diagnosis, neural network combined with the D-S evidence theory to form the motor fault diagnosis system. In data fusion level, fault characteristic is classified; and then the fault feature is extracted by the BP neural network and the local fault of the motor is diagnosed, as a result, the independent evidence is obtained; at last the D-S evidence theory fusion algorithm is used on the evidence to achieve the fault of the motor accurate diagnosis.Broken test proved that the diagnosis system improves the motor of the fault diagnosis of accuracy, and can meet the needs of real-time diagnosis. The diagnostic test proved that the diagnosis system improves the accuracy of motor fault diagnosis, and can satisfy the diagnosis in real-time.

Study and Implementation of Fault Diagnostic Algorithms on Hydraulic Power Cart for Aircraft

2011 ◽  
Vol 383-390 ◽  
pp. 1536-1541
Author(s):  
Huai Bin Zhang ◽  
Hua Yang
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Real Time ◽  
Time Analysis ◽  
Hydraulic Power ◽  
Diagnosis System ◽  
Diagnostic Algorithms ◽  
Real Time Analysis ◽  
Fault Diagnosis System ◽  
Fault Diagnostic

According to the lack of simple, backward and low precision in fault diagnosis on hydraulic power cart for aircraft, an efficient vehicle fault diagnosis system on hydraulic power cart for aircraft is developed based on embedded technology. This system can identify the cause of the faults quickly and accurately according to the data collected in the spot and real-time analysis using expert system, the results show it greatly improves the efficiency and accuracy of fault diagnosis on hydraulic power cart for aircraft.

Design and Implementation of Online Monitoring and Remote Diagnostic System for CNC Machine Tools

2013 ◽  
Vol 819 ◽  
pp. 136-139
Author(s):  
Lu Yang Jing ◽  
Tai Yong Wang ◽  
Dong Xiang Chen ◽  
Jing Xiang Fang
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Machine Tools ◽  
Online Monitoring ◽  
Acquisition System ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Diagnosis System ◽  
Fault Diagnosis System ◽  
Remote Fault Diagnosis

With the development of network technology and fault diagnosis technology, monitoring and diagnosis methods for the CNC machine tools had a great change. In this paper, an online monitoring and remote diagnosis system for CNC machine tools was built. The system was consisted of the multi-channel online acquisition system and remote fault diagnosis system. The online acquisition system achieved a real-time monitoring for CNC machine tools. The remote fault diagnosis system provided the management of devices and assistant for experts to analyze data which was uploaded from acquisition system. The system offered real-time state information of CNC machine tools and reduced downtime of machine effectively.

Design of the Real-Time Monitoring and Fault Diagnosis System of Equipment

2013 ◽  
Vol 718-720 ◽  
pp. 1472-1475
Author(s):  
Hao Zhou Wang ◽  
Jian Min Zhang ◽  
Hui Dong Li
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Liquid Crystal Display ◽  
Sensor Technology ◽  
Real Time Monitoring ◽  
Low Loss ◽  
Diagnosis System ◽  
Subject Knowledge ◽  
New Type ◽  
Fault Diagnosis System

This paper studies on a new type design of a real-time monitoring and fault diagnosis system of equipment. The system uses subject knowledge, such as sensor technology, neural network and embedded systems. It regards system voltage and current as the monitoring goals at the same time and shows the text of the system malfunction on the liquid crystal display. The paper introduces a new system monitoring approach combined with text, sound, light alarm to achieve the goal that the process of operating the equipment is safe, stable, balanced and low loss.

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