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Vibration Sensor Based Intelligent Fault Diagnosis System for Large Machine Unit in Petrochemical Industries

Authors :
Qinghua Zhang
Aisong Qin
Lei Shu
Guoxi Sun
Longqiu Shao
Source :
International Journal of Distributed Sensor Networks, Vol 11 (2015)
Publication Year :
2015
Publisher :
Hindawi - SAGE Publishing, 2015.

Abstract

Fault diagnosis is an area which is gaining increasing importance in rotating machinery. Along with the continuous advance of science and technology, the structures of rotating machinery become increasingly of larger scale and higher speed and more complicated, which result in higher probability of various failure in practice. In case one of the most critical components of machinery or equipment breaks down, it cannot only cause enormous economic loss, but also easily cause the loss of many people's lives. It is important to enable reliable, safe, and efficient operation of large-scale and critical rotating machinery, which requires us to achieve accurate and fast diagnosis of fault which has occurred. Aiming at dynamic real-time vibration monitoring and vibration signal analysis for large machine unit in petrochemical industry, which cannot realize real-time, online, and fast fault diagnosis, an intelligent fault diagnosis system using artificial immune algorithm and dimensionless parameters is developed in this paper, innovated with a focus on reliability, remote monitoring, and practicality and applied to the third catalytic flue gas turbine in a petrochemical enterprise, with good effects.

Details

Language :
English
ISSN :
15501477
Volume :
11
Database :
Directory of Open Access Journals
Journal :
International Journal of Distributed Sensor Networks
Publication Type :
Academic Journal
Accession number :
edsdoj.87b0fd8682f24d24bc0cae8e93b31ba0
Document Type :
article
Full Text :
https://doi.org/10.1155/2015/239405