A Feature Extraction Method for Rotor Rubbing Fault Based on Complex Local Mean Decomposition

Abstract

The invention relates to a rotor rub-impact fault feature extraction method based on complex local mean decomposition, and belongs to the technical field of mechanical vibration. According to the method, firstly vibration signals in mutual vertical directions of the same section are obtained, signals of the two channels form a complex signal, then through complex local mean decomposition, the complex signal is decomposed into a sum of a series of complex product functions, and a complex envelope signal is obtained according to the complex product functions; and the complex envelope signals are transformed by adopting complex Fourier transformation, and then real parts and imaginary parts of complex Fourier transformation results are fused to obtain a corresponding vector envelope spectrum, thereby effectively extracting fault features of a rotor. The rotor rub-impact fault feature extraction method based on complex local mean decomposition in the invention fuses vibration information of two channels, directly processes and analyzes two-dimensional signals of the two channels through complex local mean decomposition, and extracted fault feature information is clearer and more comprehensive, thereby providing a basis for rub-impact diagnosis of the rotor.

Description

technical field [0001] The invention relates to a method for extracting rotor rubbing fault features based on complex local mean value decomposition, and belongs to the technical field of mechanical vibration. Background technique [0002] In recent years, fault diagnosis technology has received significant attention at home and abroad. Extracting fault symptoms from operating dynamic signals is a necessary condition for mechanical fault diagnosis. Since most of the fault vibration signals are nonlinear signals, some advanced digital signal processing methods, such as wavelet transform, second-generation wavelet transform, Empirical Mode Decomposition (EMD), local Mean Decomposition (Local Mean Decomposition, LMD) and other methods extract symptoms or characteristics reflecting the fault, and provide technical support for equipment fault diagnosis. Wavelet transform, second-generation wavelet transform, and multi-wavelet transform can be said to be based on the inner produc...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a rotor rubbing fault feature extraction method based on complex local mean value decomposition to solve the problems of low efficiency and inaccurate fault identification caused by one-dimensional signal processing in the current vibration signal fault identification process

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