Rotary machine fault feature extraction method based on FrFT-EWT principle

Abstract

The invention discloses a rotary machine fault feature extraction method based on FrFT-EWT. According to the method, the capability of processing multi-component chirp signals of Fractional Fourier Transform (FrFT) and the capability of adaptively extracting signal characteristic components of Empirical Wavelet Transform (EWT) are comprehensive utilized;; empirical wavelet transform is popularizedto a fractional Fourier transform domain; adaptive decomposition of non-stationary signals is realized, and the application range of the non-stationary signals is remarkably expanded; the self-adaptive extraction of the characteristic components of the start-stop vibration signals of a rotating shaft is realized; and an effective way is provided for the information self-adaptive processing of therotating shaft in the start-stop stage and the detection and identification of early weak faults.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of mechanical equipment, and in particular relates to a method for extracting fault features of rotating machinery based on the principle of FrFT-EWT. Background technique [0002] Large-scale rotating machinery is the key equipment in many industries such as petroleum, chemical industry, energy, metallurgy, etc. It is of great significance to ensure the safe, stable, continuous and high-quality operation of these key equipment for safe production and improving the economic benefits of enterprises. As a key component of rotating mechanical equipment, the rotating shaft is a high-incidence part of failure, and its operating status often directly affects the safety and stability of the operating status of the equipment. [0003] Compared with the stable working condition, the vibration signal during the start-stop process of the shaft contains more abundant state information, and it is more in-depth a...

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Problems solved by technology

[0005] The purpose of the present invention is to overcome the shortcomings of the existing fractional-order Fourier transform filtering method, which has a large amount of computation and no adaptive processing capability, and provides a method for extracting fault features of rotating machinery based on FrFT-EWT. The combination of Fourier transform and empirical wavelet transform has the ability to adaptively process non-stationary signals in the process of starting and stopping

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