Rotary machinery fault feature extraction method based on self-adaptive wavelet energy

Abstract

The invention provides a rotary machinery fault feature extraction method based on self-adaptive wavelet energy. According to the invention, firstly rotor vibration signals are collected on a machinery fault comprehensive simulation experiment table, and then for the problem that signal energy features are influenced by the rotating speed and the sampling frequency, a new self-adaptive wavelet energy feature extraction method is adopted to complete effective feature extraction of a rotor imbalance fault, a misalignment fault and a bearing pedestal loosening fault of a rotor of the rotary machinery. In addition, in order to reserve more original features of the rotary machinery vibration signals and overcome the problems of a conventional denoising method that constant deviation and discontinuity exist at the threshold and parameters are not adjustable, a novel threshold function is embedded in the method for carrying out denoising. By adopting the method, a relatively good effect is obtained on the machinery fault simulation experiment table, and different fault types can be effectively distinguished by the extracted energy features.

Description

technical field [0001] The invention relates to the technical field of fault feature extraction of rotating machinery, in particular to a fault feature extraction method for wind turbines. Background technique [0002] With the rapid development of science and technology, rotating machinery is not only widely used in industries such as machinery, energy, petrochemical, metallurgy, electric power, aerospace and national defense, but also closely related to people's daily life. For example, in the urban rail transit system, the ventilation of the underground environment must be guaranteed by large rotating equipment, namely ventilators. Rotor systems and bearings are irreplaceable key parts of rotating machinery, and play a pivotal role under high-speed conditions. Because of its long-term high-speed, full-load operation, it is very prone to failure. Real-time fault diagnosis and monitoring of rotating machinery equipment not only guarantees the safe and reliable operation o...

AI Technical Summary

Problems solved by technology

[0009] The present invention first collects the rotor vibration signal on the mechanical failure comprehensive simulation experiment platform, and then aims at the problem that the signal energy feature is affected by the rotational speed and sampling frequency, and adopts a new self-adaptive wavelet energy feature extraction method to complete the rotor vibration analysis of the rotating machine. Effective Feature Extraction for Balance Fault, Misalignment Fault, Loose Housing Fault

At the same time, in order to retain more original features of the vibration signal of rotating machinery, and to overcome the problems of constant deviation and discontinuity at the threshold and non-adjustable parameters in traditional denoising methods, a new type of threshold function is embedded in this method for denoising. noise

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