Intelligent fault diagnosis method for magnetic suspension rotor system

Abstract

The invention discloses an intelligent fault diagnosis method for a magnetic suspension rotor system. The intelligent fault diagnosis method comprises the following steps: acquiring an axis trajectorydiagram of the magnetic suspension rotor system under different fault conditions; carrying out purification and denoising processing on each axis trajectory diagram; converting each axis trajectory diagram into seven corresponding invariant moments through a Hu invariant moment theory, wherein the seven invariant moments form a feature vector; screening and simplifying the feature vectors of theaxis track graphs through an empire competition algorithm, and removing unqualified axis track graphs; carrying out classification training on the directed acyclic graph support vector machine model by using the simplified feature vectors of the axis trajectory graph under multiple different fault conditions; and monitoring the track of the magnetic suspension rotor system in real time, extractingan axis track diagram of the magnetic suspension rotor system in a fault, inputting the extracted axis track diagram into the trained support vector machine model for classification and identification, and judging a corresponding fault type according to a classification and identification result. The identification of the axis track of the magnetic suspension rotor system in the fault is more accurate.

Description

technical field [0001] The invention relates to the field of bearing protection, in particular to an intelligent fault diagnosis method for a magnetic levitation rotor system. Background technique [0002] Compared with the traditional bearing-rotor system, the active magnetic bearing-rotor system (Active Magnetic Bearing, AMB) has many advantages such as no friction and low energy consumption. The AMB system is highly nonlinear, and the chaotic phenomenon of the rotor at the critical speed is a frontier issue. Therefore, how to provide a stable control algorithm and accurate chaos analysis method for the maglev-rotor system has become one of the hot research issues of the system. one. [0003] For the control of the nonlinear bearing-rotor system, it can be realized by the traditional proportional-derivative control algorithm and sliding mode control algorithm, but the unstable factors such as bifurcation caused by the time delay will adversely affect the stability of the ...

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Problems solved by technology

[0005] However, the trajectory data collected during the experiment will have a lot of redundancy, which will affect the classification effect of machine learning.

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