AUTHOR=Wang Lu , Liu Hui , Liang Jie , Zhang Lijuan , Ji Qingchang , Wang Jianqiang 

TITLE=Research on the Rotor Fault Diagnosis Method Based on QPSO-VMD-PCA-SVM

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.944961

DOI=10.3389/fenrg.2022.944961

ISSN=2296-598X

ABSTRACT=Rotor system is the core device of rotating machinery equipment. Its safe and reliable operation state directly affects the economic benefits of equipment and personal safety of users. In order to fully explore the complex feature mapping relationship between rotor vibration signals and fault types, this paper studies rotor vibration signals under different working conditions from the perspectives of feature parameter construction and feature information mining. Firstly, VMD algorithm was used to decompose the vibration signals, and quantum behavior particle swarm optimization (QPSO) was used to minimize the mean envelope entropy of IMF components to determine the optimal combination of modal number and penalty coefficient. Secondly, principal component analysis (PCA) was used to reduce the dimensionality of IMF components of vibration signals. Finally, support vector machine (SVM) was used to mine the feature mapping relationship between vibration data after dimensionality reduction and rotor operation state so as to accurately identify rotor fault types. The proposed method was used to analyze the measured vibration signals of the rotor system .The experimental results showed that the proposed method can effectively extract the characteristic information of the rotor running state from the vibration data, and the four kinds of fault diagnosis accuracy reaches 100%, 88.89%, 100% and 100% respectively. In addition, compared with other comparison models, the accuracy of this paper was the best.