scholarly journals Neural Network Based Fault Detection and Diagnosis System for Three-Phase Inverter in Variable Speed Drive with Induction Motor

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Furqan Asghar ◽  
Muhammad Talha ◽  
Sung Ho Kim
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Induction Motor ◽  
Fault Detection And Diagnosis ◽  
Diagnosis System ◽  
Multiple Faults ◽  
Phase Inverter ◽  
Three Phase ◽  
Detection And Diagnosis ◽  
Three Phase Inverter

Recently, electrical drives generally associate inverter and induction machine. Therefore, inverter must be taken into consideration along with induction motor in order to provide a relevant and efficient diagnosis of these systems. Various faults in inverter may influence the system operation by unexpected maintenance, which increases the cost factor and reduces overall efficiency. In this paper, fault detection and diagnosis based on features extraction and neural network technique for three-phase inverter is presented. Basic purpose of this fault detection and diagnosis system is to detect single or multiple faults efficiently. Several features are extracted from the Clarke transformed output current and used in neural network as input for fault detection and diagnosis. Hence, some simulation study as well as hardware implementation and experimentation is carried out to verify the feasibility of the proposed scheme. Results show that the designed system not only detects faults easily, but also can effectively differentiate between multiple faults. These results prove the credibility and show the satisfactory performance of designed system. Results prove the supremacy of designed system over previous feature extraction fault systems as it can detect and diagnose faults in a single cycle as compared to previous multicycles detection with high accuracy.

Fault Diagnosis System of Engineering Equipment's Electrical System Using Dedicated Interface Adapter Unit

2013 ◽  
Vol 567 ◽  
pp. 155-160
Author(s):  
Yan Xi Ren ◽  
Xiao Qiang Yang ◽  
Qing Xia Li ◽  
Jun Da Chen
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Object Oriented Programming ◽  
Circuit Board ◽  
Electrical System ◽  
Software Module ◽  
Master Program ◽  
Diagnosis System ◽  
Detection And Diagnosis ◽  
Signal Interface

The development of fault detection and diagnosis system is accomplished with the application of PXI interface technology, modular instrument and signal processing technology. The total technical scheme of host computer, portable test platform, signal adapter unit, test interface and cable together with peripheral components is introduced in the presented system. Consequently, the hardware includes master computer (fault test and diagnosis platform), PXI-bus data acquisition system, signal interface adapter, power supply system, interface unit, connection cable and peripheral dedicated test equipments. And the software is developed by C and LabWindows/CVI based on Win32 operating system. In addition, the modular and object-oriented programming are adopted in the software development. The software consists of three parts: the master program running on test and diagnosis platform, the client software module on signal adapter unit as well as the remote interface software module. It can implement fault detection of electrical system on replaceable circuit board and block of the hydraulic system or electrical system. So it can help equipment repairmen and operator perform quick repairs and maintenance to the electrical system for engineering equipment.

Towards the Integration of Mobile Augmented Reality within an Aluminium Process Fault Detection and Diagnosis System

2013 ◽  
Vol 845 ◽  
pp. 703-707 ◽  
Author(s):  
Abd Majid Nazatul Aini ◽  
Haslina Arshad
Keyword(s):  
Augmented Reality ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Industrial Plant ◽  
Mobile Augmented Reality ◽  
Design Quality ◽  
Diagnosis System ◽  
Aluminium Smelting ◽  
Detection And Diagnosis ◽  
Mobile Ar

Mobile Augmented Reality (AR), which mixes the real world and the virtual world on hand-held devices, is a growing area of the manufacturing industry. Since mobile AR can be used to augment a users view of an industry plant, it provides alternative solutions for design, quality control, monitoring and control, service, and maintenance in complex process industries, such as the aluminium smelting industry. The objective of this paper is to discuss the integration of mobile AR within an aluminium industrial plant, in order to achieve effective fault detection and diagnosis. The possible integration of mobile AR within an aluminium fault detection and diagnosis system is shown with regard to four main functions, namely (1) plant information system, (2) fault history, (3) interactive troubleshooting, and (4) statistical analysis results. This paper opens up possible future works, where the potential use of mobile AR can be explored as an additional user interface component, for increasing the effectiveness of process monitoring within the aluminium smelting process.

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