Vibration Sensor Based Intelligent Fault Diagnosis System for Large Machine Unit in Petrochemical Industries - Qinghua Zhang; Aisong Qin; Lei Shu; Guoxi Sun; Longqiu Shao (International Journal of Distributed Sensor Networks)
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.