An intelligent monitoring and fault detection and diagnosis system in steel rolling mill

2002 ◽  
Author(s):  
Zhong Ji ◽  
Wang Zhongmin ◽  
Wang Qin ◽  
Tu Xuyan
Keyword(s):  
Fault Detection ◽  
Rolling Mill ◽  
Fault Detection And Diagnosis ◽  
Intelligent Monitoring ◽  
Steel Rolling ◽  
Diagnosis System ◽  
Detection And Diagnosis

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.

Fault Diagnosis System Based on UN-150ZB Mobile Rail Flash Butt Welding

2012 ◽  
Vol 472-475 ◽  
pp. 1289-1293
Author(s):  
Hong Guang Zhao ◽  
Chao He ◽  
Xi Zhang ◽  
Qing Bing Lv
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Practical Application ◽  
Diagnosis System ◽  
Butt Welding ◽  
Input And Output ◽  
Fault Diagnosis System ◽  
Detection And Diagnosis ◽  
Output Information ◽  
Welding Productivity

As the fault of the UN5-150ZB rail flash butt welding machine was difficult to be detected and diagnosed, a self-diagnosis system based on communication between PC and PLC was developed to realize fault detection and diagnosis for site operation. The system was implemented by transporting input and output information which including digital and analog, and states of PLC to PC. The computer diagnosis software could judge different fault types automatically on different diagnosis interface. Diagnosis results were printed on screen in text and graph that could provide information of fault types and phase. With the help of diagnosis results, welding productivity can be improved and problems of the equipment can be solved rapidly. Practical application showed that the self-diagnosis system provided a good solution to the fault detection and diagnosis.

Sensorless Early Detection of Mechanical Faults: Developments in Smart Rotating Machines

2001 ◽  
Author(s):  
Alexander G. Parlos ◽  
Kyusung Kim ◽  
Raj M. Bharadwaj
Keyword(s):  
Fault Detection ◽  
Failure Modes ◽  
Fault Detection And Diagnosis ◽  
False Alarms ◽  
Detection Accuracy ◽  
Driving Dynamic ◽  
Diagnosis System ◽  
Electric Power Supply ◽  
Detection And Diagnosis ◽  
High Level

Abstract Practical early fault detection and diagnosis systems must exhibit high level of detection accuracy and while exhibiting acceptably low false alarm rates. Such designs must have applicability to a large class of machines, require installation of no additional sensors, and require minimal detailed information regarding the specific machine design. Electromechanical systems, such as electric motors driving dynamic loads like pumps and compressors, often develop incipient failures that result in downtime. There is a large number of such failure modes, with a large majority being of mechanical nature. The precise signatures of these failure modes depend on numerous machine-specific factors, including variations in the electric power supply and driven load. In this paper the development and experimental demonstration of a sensorless, detection and diagnosis system is presented for incipient machine faults. The developed fault detection and diagnosis system uses recent developments in dynamic recurrent neural networks in implementing an empirical model-based approach, and multi-resolution signal processing for extracting fault information from transient signals. The signals used by the system are only the multi-phase motor current and voltage sensors, whereas the transient mechanical speed is estimated from these measurements using a recently developed speed filter. The effectiveness of the fault diagnosis system is demonstrated by detecting stator, rotor and bearing failures at early stages of development and during different levels of deterioration. Experimental test results from small machines, 2.2 kW, and large machines, 373 kW and 597 kW, are presented demonstrating the effectiveness of the proposed approach. Furthermore, the ability of the diagnosis system to discriminate between false alarms and actual incipient failure conditions is demonstrated.

scholarly journals Model-Based Fault Detection and Diagnosis for Spacecraft with an Application for the SONATE Triple Cube Nano-Satellite

Aerospace ◽  
2019 ◽  
Vol 6 (10) ◽  
pp. 105 ◽  
Author(s):  
Kirill Djebko ◽  
Frank Puppe ◽  
Hakan Kayal
Keyword(s):  
Fault Detection ◽  
Detection System ◽  
Fault Detection And Diagnosis ◽  
Generic Model ◽  
Diagnosis System ◽  
Counter Measures ◽  
Model Based ◽  
Human Operators ◽  
Detection And Diagnosis ◽  
Mission Operation

The correct behavior of spacecraft components is the foundation of unhindered mission operation. However, no technical system is free of wear and degradation. A malfunction of one single component might significantly alter the behavior of the whole spacecraft and may even lead to a complete mission failure. Therefore, abnormal component behavior must be detected early in order to be able to perform counter measures. A dedicated fault detection system can be employed, as opposed to classical health monitoring, performed by human operators, to decrease the response time to a malfunction. In this paper, we present a generic model-based diagnosis system, which detects faults by analyzing the spacecraft’s housekeeping data. The observed behavior of the spacecraft components, given by the housekeeping data is compared to their expected behavior, obtained through simulation. Each discrepancy between the observed and the expected behavior of a component generates a so-called symptom. Given the symptoms, the diagnoses are derived by computing sets of components whose malfunction might cause the observed discrepancies. We demonstrate the applicability of the diagnosis system by using modified housekeeping data of the qualification model of an actual spacecraft and outline the advantages and drawbacks of our approach.

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