Motor rotor eccentricity fault diagnosis method based on completely self-adaptive matrix pencil

Abstract

The invention provides a motor rotor eccentricity fault diagnosis method based on a completely self-adaptive matrix pencil, solves the defects of a conventional matrix pencil algorithm on the aspect of motor rotor eccentricity fault diagnosis, and has double effects of self-adaptive threshold truncation and self-adaptive fundamental wave trap filtering. The singular value truncation threshold of the matrix pencil is self-adaptively adjusted by calculating the maximum likelihood estimation of a singular value matrix; meanwhile, a completely self-adaptive trap fundamental wave current component can be realized by utilizing the singular value matrix in the matrix pencil. The required current acquisition time length is quite low and is less than 1 second (10kHz sampling frequency) so that the method is suitable for the application occasion of slow changing of motor loads; and self-adaptability to the trap current fundamental wave component can be realized, and precision of eccentricity fault diagnosis characteristics is high without possibility of misjudgment basically. The truncation threshold of the singular value matrix is self-adaptively set according to different rotating speed and different loads of a motor without possibility of failure of the diagnosis method.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of motor rotors, and in particular relates to a method for diagnosing motor rotor eccentric faults with fully self-adaptive notch matrix beams. Background technique [0002] Motors exist in almost all industrial occasions, and most of the equipment is driven by motors. Electric motors, especially large ones, are the key to the normal functioning of society. Due to the extensive use of motors, failures will inevitably occur; especially in some occasions where the application environment is harsh and the load impact is large, the failure rate of the motor is high. In a complex production line, if a key motor fails to be found and maintained in time, the product quality of the entire production line will be affected; serious motor failures can cause industrial accidents and economic losses. In manned motor applications, the importance of motor status detection and fault diagnosis technology is self-e...

AI Technical Summary

Problems solved by technology

Its characteristic is that by calculating the maximum likelihood estimation of the singular value matrix, the singular value truncation threshold of the matrix bundle can be adaptively adjusted according to the different working conditions of the motor, so that the rotor eccentricity fault diagnosis method provided by the present invention will not Failure under any speed and load; at the same time, in view of the problem that the motor rotor eccentricity fault features are weak and the identification accuracy is difficult to guarantee, the present invention uses the singular value matrix inside the matrix bundle, and through several steps of operation, it can realize a fully adaptive notch base The wave current component does not need to specify conventional notch filter parameters such as notch frequency, and has an ideal adaptive notch function, thereby highlighting fault characteristics, improving the identification accuracy of rotor eccentric fault characteristics, and reducing the possibility of misjudgment

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