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Failure Diagnosis and Maintenance Decision Auxiliary System of Large-Scale Turbine Compressor Unit
- Source :
- 2017 9th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA).
- Publication Year :
- 2017
- Publisher :
- IEEE, 2017.
-
Abstract
- In order to enhance operation safety of unit, at present, state monitoring of large-scale rotary machine has been universally applied. Failure diagnosis technology receives universal emphasis and it is continually developing from machine maintenance at fixed time to maintenance according to actual condition. With the development in communication and network technology, people are no longer satisfy with diagnosis technology of single machine, and they hope diagnosis technology can be share by all so as to solve more and more machine failure. So networking has become to be the inevitable trend for the development of diagnosis technology. Failure diagnosis and maintenance decision auxiliary system of large-scale turbine unit is one distributed network system towards the whole enterprises, it makes networking on 18 sets of large-scale tribune compressor unit of enterprises. System can not only make online monitoring and failure diagnosis on operation state of theses machines, but also it can provide the basic state and artificial monitoring information at fixed time, it combines with vibration data, process parameter and existing artificial periodic monitoring information together to realize periodic evaluation of machine at normal state and comprehensive failure diagnosis analysis at abnormal state, so it provides important base for the early failure discovery and maintenance decision of machine. It fundamentally changes the backward key unit inspection as well as maintenance management way and increases management level of equipment.
Details
- Database :
- OpenAIRE
- Journal :
- 2017 9th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)
- Accession number :
- edsair.doi...........6356f7fd9bff7c6c9806e22db0556661
- Full Text :
- https://doi.org/10.1109/icmtma.2017.0013