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A Feature Extraction Method of Blade Unbalance Fault Electrical Signal Based on Hilbert Transform

A balance fault and feature extraction technology, applied in static/dynamic balance test, machine/structural component test, instrument, etc., can solve the problem that electrical signals are difficult to accurately extract blade unbalance fault features, etc., to achieve algorithm or decision-making Easy application and high stability effect

Active Publication Date: 2021-02-23
SHANGHAI MARITIME UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] In order to solve the problem that the traditional fault detection method in the state monitoring system is difficult to accurately extract the characteristics of the blade unbalance fault by using electrical signals, the invention proposes a method for extracting the characteristics of the blade unbalance fault electrical signal based on Hilbert transform

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  • A Feature Extraction Method of Blade Unbalance Fault Electrical Signal Based on Hilbert Transform
  • A Feature Extraction Method of Blade Unbalance Fault Electrical Signal Based on Hilbert Transform
  • A Feature Extraction Method of Blade Unbalance Fault Electrical Signal Based on Hilbert Transform

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specific Embodiment approach 1

[0022] Specific implementation mode one: combine figure 1 Description of this embodiment, a method for extracting electrical signal features of blade unbalance fault based on Hilbert transform, the specific process is as follows:

[0023] Step 1: Hilbert transform the input electrical signal to obtain the instantaneous frequency signal f corresponding to the electrical signal e (t) and the corresponding instantaneous frequency f of bearing rotation under ideal conditions r (t);

[0024] Step 2: Calculate the time series S corresponding to the peak value of the electrical signal peak (k);

[0025] Step 3: Calculate the mean frequency f corresponding to each electrical signal cycle e,mean (i) and the mean frequency f corresponding to each bearing rotation cycle r,mean (j);

[0026] Step 4: For the f obtained in Step 3 e,mean (i) and f r,mean (j) Perform cubic spline interpolation to obtain an interpolated signal with the same sampling frequency as the original signal a...

specific Embodiment approach 2

[0045] Specific embodiment 2: This embodiment is a further description of specific embodiment 1. In step 1, Hilbert transform is performed on the input monitoring electrical signal, and the instantaneous frequency signal f corresponding to the electrical signal is obtained. e (t) and the corresponding instantaneous frequency f of bearing rotation under ideal conditions r The process of (t) is:

[0046] Set the original input electrical signal as x(t), time t=1,2,...,N,

[0047] Step a: Perform discrete convolution on the original electrical signal x(t) to obtain its Hilbert transform y(t), as shown in the following formula:

[0048]

[0049] Step b: Calculate the envelope amplitude a(t) of the analytical signal c(t)+jy(t) of the original electrical signal, as shown in the following formula:

[0050]

[0051] Step c: Calculate the phase angle θ(t) of the analytical signal c(t)+jy(t) and the instantaneous frequency f corresponding to the electrical signale (t), as shown ...

specific Embodiment approach 3

[0055] Specific embodiment three: This embodiment is a further description of specific embodiment one. In step two, the time series S corresponding to the peak value of the original electrical signal is calculated. peak The process of (k) is:

[0056] Step a: search for all peak points in the original electrical signal;

[0057] Step b: Calculate the time point information corresponding to all the peak points in step a, and obtain the time series S peak (k), where k=1,2,...,K, k represents the kth peak point, and K represents the total number of peak points contained in the original electrical signal.

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Abstract

The invention discloses a blade imbalance fault electric signal feature extraction method based on Hilbert transform. The method comprises the following steps: performing Hilbert transform on an inputelectric signal to obtain a transient frequency signal corresponding to the electric signal and corresponding bearing rotation transient frequency under an ideal condition; computing a time sequencecorresponding to a peak of the electric signal; computing a mean frequency corresponding to each electric signal cycle and mean frequency corresponding to each bearing rotary cycle; performing trice spline interpolation on the electric signal mean frequency and the bearing rotary mean frequency acquired in the step three to obtain interpolation signals same as the sampling frequency of the original signal; five, solving the difference of the interpolation signals acquired in the step four to obtain a fault feature signal. The method relates to the key technology of the new energy generation state monitoring field, the problem that the blade imbalance fault feature is hard to be precisely extracted by using the electric signal of the generator in the traditional fault detection is solved.

Description

Technical field: [0001] The invention relates to key technologies in the field of state monitoring of new energy power generation, in particular to a method for extracting electrical signal features of blade unbalance faults based on Hilbert transform. Background technique: [0002] In recent years, as wind energy and ocean current energy have received more and more attention, new energy generating equipment such as wind turbines and ocean current generators have been widely used. Among them, the most common faults in wind power generation and ocean current power generation are blade imbalance problems caused by blade attachment, blade corrosion, blade wear, etc. Such faults will affect the operation of the system by introducing unbalanced mass, so the detection machine It is important to condition and repair or replace faulty blades to avoid system damage. Condition monitoring using electrical signals has advantages over traditional detection methods based on vibration sig...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M1/16G06K9/00
Inventor 李志超王天真张米露谢涛
Owner SHANGHAI MARITIME UNIVERSITY