An intelligent diagnostic system for the condition monitoring of AC motors

Author(s):  
Muhammad Irfan ◽  
Nordin Saad ◽  
Rosdiazli Ibrahim ◽  
Vijanth S. Asirvadam
2011 ◽  
Vol 63-64 ◽  
pp. 449-452 ◽  
Author(s):  
Jun Fa Leng ◽  
Shuang Xi Jing

In this research, a new method based on EMD and SVM for mine fan fault diagnosis is introduced. With EMD, fault feature can be extracted quickly and accurately, and taken as the input samples for SVM with the outstanding non-linear pattern classification performances. 5 two-class SVM classifiers are designed in order to classify and diagnosis 5 typical fault patterns of mine fan. The result of this research shows that the integrative method of EMD and SVM is very fit for the intelligent diagnosis and fault patterns recognition, and it will lead to the possible development of an automated and on-line mine fan condition monitoring and diagnostic system.


1975 ◽  
Author(s):  
J. R. Passalacqua

This paper describes the development, operation and performance of an automatic engine condition monitoring system by Hamilton Standard Division of United Aircraft. This development is a direct outgrowth of Airborne Integrated Data Systems (AIDS), which have been developed for commercial and military aviation. Application of this technology to an installation at Hartford Electric Light Company’s South Meadow facility led to the development of the system currently being installed at several major utilities and marketed by Hamilton Standard. Field results of the HELCO testing are presented herein. As current installation information becomes available it will be made available to industry.


2021 ◽  
Author(s):  
Marcantonio Catelani ◽  
Lorenzo Ciani ◽  
Giulia Guidi ◽  
Gabriele Patrizi

Author(s):  
Peter W. Tse ◽  
Ling S. He

Abstract Failure of equipment not only leads to loss of production, but also, in some serious situations, causes human casualty. Hence, the need of equipment condition monitoring becomes crucial for reliable operations. Since expensive hardware instruments are needed for condition monitoring, with the current powerful PCs, software based virtual instruments are possible to replace hardware instruments. However, the software must be maintained for each PC, and a technician must still visit the site of the PC and its monitored equipment. By connecting the PC to the Internet, remote sensing and equipment fault diagnosing are possible. As only one copy of software for each type of virtual instrument is required on the server, the maintenance of virtual instrument is much easier and substantial cost of hardware instruments is reduced. Moreover, remote collaborative maintenance becomes feasible as worldwide experts can provide just-in-time advice to technicians via the Web. With the new standards of IEEE 1451.2 for smart transducers, the new sensors are self-Web-ready. Sensor manufacturers, such as Hewlett Packard and Bruel & Kjaer, have proclaimed their next generation sensors are self-Web-ready. Therefore, the development of a Web-based maintenance is a must in the near future. This paper presents the design and development of a solution and a platform for the Web-based remote collaborative diagnosis and maintenance. It consists of remote data sensing and logging, signal analysis using virtual instruments, and intelligent fault diagnosis and prognosis.


Author(s):  
Christof Nagel

Interest in online turbine condition monitoring has increased among utilities in order to minimize unforeseen standstills and for better planning of overhauls or repair work. The AMODIS® (ALSTOM Monitoring and Diagnostic System) Steam Turbine Condition Monitoring system monitors steam turbines locally or remotely via long distances [1]. The system also collects all data to compare current events with past events. This monitoring system is not an expert system recommending how to solve malfunctions. It is more a system which helps operators to take measures before the standard alarm or turbine trip is activated. An interlock of the process parameters generates early warning alarms which are based on the OEM experience and help operators to get a clear picture of an arising problem and to react early enough to avoid forced outages. Additional sensors for additional process parameters have to be installed. The system is part of the AMODIS plant monitoring system and consists of six separately available modules: • Steam inlet valves: To detect increased friction in the actuator and the steam valve guide. • Jacking oil and turning gear: To detect malfunction in the jacking oil and turning gear systems. • Bearing supervision: To detect possible tilting of the bearing pedestal or abnormal oil consumption. • Thermal expansion: To detect extreme or abnormal differential expansion and to detect expansion hindrance. • Thermal efficiency: To detect loss of internal efficiency at an early stage. • Lube oil condition monitoring: To monitor the oil with an online particle counter and a sensor for content of water. All modules can be supplied separately. Modules to check vibration and performance are also available in an AMODIS system but are not covered in this paper.


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