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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.

Aiming at the shortcomings of the traditional frequency domain analysis method, such as failure to find early faults, the misjudgement and omission of fault types, and failure to diagnose complex faults, a new approach is developed, which is different from the existing technical route in the field of fault diagnosis, by closely following real-time online, intelligent and accurate requirements in the field of monitoring and fault diagnosis of large rotating machinery. Combining immune mechanism, dimensionless index, support vector machine and other artificial intelligence technologies, linked with the particularity of fault diagnosis problems, a fault diagnosis classification algorithm based on memory sequence is proposed, and an intelligent fault diagnosis system based on a dimensionless immune detector and support vector machine was developed. Finally, the system was applied to a compressor unit in a petrochemical enterprise and good results were achieved. [ABSTRACT FROM AUTHOR]