Abstract
To accurately extract the fault characteristics of vibration signals of rotating machinery is of great significance to the unit online monitoring and evaluation. However, because the current feature extraction methods are mainly for single channel, the results of feature extraction are often inaccurate. To this end, a coupling fault feature extraction method based on bivariate empirical mode decomposition (BEMD) and full spectrum is proposed for rotating machinery. Firstly, the two-dimensional orthogonal signal obtained by orthogonal sampling technique is decomposed by bivariate empirical mode decomposition to obtain the intrinsic mode function with phase information. In order to obtain the sensitive modal components, the sensitivity coefficients are constructed on the basis of mutual information. Then, the sensitivity coefficient of each intrinsic mode function is calculated, and the intrinsic mode function with the larger sensitive coefficient is selected as the sensitive component. Finally, the full spectrum of the sensitive component is obtained using the full vector envelope technique, so as to get a comprehensive and accurate characteristic component. The results of simulations experiment and an application example show that this method can extract the fault characteristic component of the rotating machinery comprehensively and accurately. It is of great significance to realize the accurate diagnosis of coupling faults of rotating machinery.
Highlights
Rotating machinery is one of the key parts of large mechanical equipment, such as hydraulic turbine, steam turbine, and wind turbine, which have found wide application in various industrial fields [1]
We proposed a coupling fault feature extraction method based on bivariate empirical mode decomposition and full spectrum for rotating machinery
This paper proposed a method to extract a feature from orthogonal signals in two-direction sensors for the condition monitoring of rotating machinery based on bivariate empirical mode decomposition (BEMD) and full spectrum
Summary
Rotating machinery is one of the key parts of large mechanical equipment, such as hydraulic turbine, steam turbine, and wind turbine, which have found wide application in various industrial fields [1]. In order to ensure the safe and reliable operation of the equipment, the equipment condition monitoring and fault diagnosis technology needs to have higher precision. Whether it can accurately extract the characteristic signal of coupling fault is the key to the condition monitoring and fault diagnosis of rotating machinery [3, 4].
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have