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

A technology of rotating machinery and wavelet energy, which is applied in the testing of mechanical components, measuring devices, testing of machine/structural components, etc., and can solve problems such as constant deviation, non-adjustable, and misalignment

Inactive Publication Date: 2016-06-08
BEIJING UNIV OF TECH
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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 F

Method used

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

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Embodiment Construction

[0043] The inventive method has following realization steps:

[0044] Step 1: Vibration signal acquisition.

[0045] Use the MFS mechanical failure comprehensive simulation test bench to simulate the operation of the fan, such as figure 2 As shown, an acceleration sensor is installed on the bearing seat, and a photoelectric tachometer is installed on the rotating shaft, and the NI9234 module and the NI9171 portable chassis are used to cooperate with the slave sensor to collect vibration signals. The sampling rate is f s The normal vibration signal of the rotor and the three types of rotor fault signals of unbalance, misalignment and loose bearing seat are collected respectively, and the rotor rotation frequency is f n .

[0046] Step 2: Resample the signal.

[0047] First, the input signal x(t) is scaled by f n transformation, for x(f n t) in time interval Δt s Sampling is equivalent to the original signal x(t) with Δt s / f n sampling at intervals. According to the ...

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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...

Claims

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Application Information

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IPC IPC(8): G01M13/00G01H17/00G01M3/14
CPCG01M13/00G01H17/00G01M3/14
Inventor 王普温峥高学金
Owner BEIJING UNIV OF TECH
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