Reciprocating compressor bearing fault diagnosis method based on improved local mean value decomposition

Abstract

The invention discloses a reciprocating compressor bearing fault diagnosis method based on improved local mean value decomposition, which solves the problem that the traditional method fault information is less prominent and the accuracy is not high. The reciprocating compressor bearing fault diagnosis method comprises steps of using a sensor and a data collection system to test the reciprocating compressor vibration signal, using the improved local mean value decomposition method to decompose the vibration signal of the reciprocating compressor to form a series of PF components and decouple the fault information, adopting a correlation coefficient method to screen out the PF component containing the main state information from the massive PF components, and finally, calculating the transient amplitude spectrum of the chosen PF component, extracting the fault characteristic frequency, and determining the fault state. Compared with the diagnosis method based on the traditional improved local mean value decomposition, the fault characteristic frequency extracted by the improved method is more prominent and more accurate diagnosis on the fault of the reciprocating compressor bearing is realized.

Description

technical field [0001] The invention relates to the field of fault diagnosis of mechanical equipment, in particular to a fault diagnosis method for reciprocating compressor bearings based on local mean value decomposition. Background technique [0002] Reciprocating compressors are gas compression equipment widely used in petroleum and chemical industries. The transmission mechanism is an important part of its power transmission and motion conversion. The connecting rod and various components in the mechanism are usually connected by sliding bearings. After the equipment has been in operation for a long time, the sliding bearings often have excessive clearance failure due to wear, which will cause the machine body to vibrate violently and shut down. Therefore, in order to improve the service life of equipment and ensure safe production, it is necessary to implement fault diagnosis for reciprocating compressor bearings. [0003] Vibration signals are rich in equipment state ...

AI Technical Summary

Problems solved by technology

However, because the second-order derivative of cubic spline interpolation is continuous, the envelope curve produces over-envelope or under-envelope phenomenon while ensuring smoothness, and this phenomenon is especially important in the analysis of vibration signals of reciprocating compressors with strong non-stationary characteristics. Signific

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