CN114254679A - Filter-based feature enhancement method - Google Patents

Abstract

The invention belongs to the technical field of fault diagnosis, and particularly relates to a filter-based feature enhancement method. The characteristic enhancement method based on the filter provides an effective means for fault diagnosis and signal processing of the bearing, avoids safety accidents caused by bearing faults, and has certain practicability and engineering value.

Description

Filter-based feature enhancement method

Technical Field

The invention belongs to the technical field of signal processing, and particularly relates to a characteristic enhancement method based on a filter.

Background

Rolling bearings are one of the most widely used components in mechanical equipment. Detection of its state and fault diagnosis have long been the technical focus of scientific research and industry. Generally, vibration signals of bearings often contain a large amount of noise, and the existence of the noise can interfere with effective identification of fault impact, which brings great difficulty to fault diagnosis of the bearings. Signals in engineering practice are often signals containing a large amount of noise, so that the research on algorithms for signal noise reduction and bearing characteristic enhancement has important research significance for engineering application.

Disclosure of Invention

The technical problem to be solved by the invention is as follows: in the prior art, vibration signals of the bearing often contain a large amount of noise, and the existence of the noise can interfere effective identification of fault impact, so that great difficulty is brought to fault diagnosis of the bearing. The invention aims to provide a filter-based characteristic enhancement method, which aims to realize the purposes of enhancing the fault impact characteristic and weakening the noise in a vibration signal of a rolling bearing and provides basic support for the fault prediction and the health management of the bearing.

The technical scheme adopted by the invention for solving the technical problems is as follows: a method for filter-based feature enhancement, comprising the steps of:

s1, preprocessing the acceleration signal containing noise by adopting lifting wavelet transform:

acquiring a signal s (t) with a data point n by using an exponential decay function containing impact characteristics:

wherein A is a displacement constant, xi is a damping ratio, w_nT is n points at a natural frequency of vibration and f is a sampling frequency_sThe relationship between t and f is (1: n)/f_s(ii) a Then adding noise into the s (n) signal, wherein the overall amplitude of the noise is larger than that of the impact characteristics, so that the impact characteristics s (n) in the signal can be submerged, and the characteristics are not obvious; primarily processing a signal to be processed containing noise by adopting lifting wavelet transform to obtain a new signal f (n); lifting wavelet transform is a faster and more efficient wavelet transform method, also known as second generation wavelet transform. The method does not depend on Fourier transform, and inherits the multi-resolution characteristics of the first generation wavelet; the whole lifting wavelet transformation process comprises decomposition and reconstruction;

s2, searching all peak points and all valley points in the new signal f (n):

finding all peak points in the new signal f (n), and recording the found peak points as A ═ A { (A)_pFind all valley points in the new signal f (n), and mark it as B ═ B { (B) }_hH is 1,2, …, and then the interval I between each adjacent peak points is calculated_A＝{i_A│i_A＝A_p+1，x－A_p，xCalculating the interval G between each adjacent valley point_B＝{g_B│g_B＝B_h+1，x－B_h，xIn which A is_p，xFor the peak value A in the signal_pCorresponding horizontal coordinate value, B_h，xAs the valley B in the signal_hCorresponding horizontal coordinate values;

s3, constructing a signal library:

calculating the peak point interval I_A＝{i_A│i_A＝A_p+1，x－A_p，xAnd valley point spacing G_B＝{g_B│g_B＝B_h+1，x－B_h，xMaximum value of max (I)_A,G_B) Selecting the size of C₁(C₁≥max(I_A,G_B) A linear type structural element difference filtering operator carries out filtering processing on the vibration signals f (n), and the processed result is recorded as f_C1(n); then using the scale C₂(C₂≥max(I_A,G_B) A linear type structural element difference filtering operator carries out filtering processing on the vibration signals f (n), and the processed result is recorded as f_C2(n); and analogizing in sequence, after m times of processing, establishing all processing results into a signal library { f_C1(n),f_C2(n),f_C3(n),...,f_Cm(n)}；

S4, extracting coincident impact characteristics from the constructed signal library:

in signal library { f_C1(n),f_C2(n),f_C3(n),...,f_Cm(n) } extraction of the coincident impact portion z (w):

z(w)＝f_C1{find(f_C1(n)＝f_C2(n)＝...＝f_Cm(n))} (2)。

in step S3, the linear-type difference filter operator is an operation form of morphological filtering.

The characteristic enhancement method based on the filter has the advantages that the characteristic enhancement method based on the filter is provided based on the mathematical principle of the morphological algorithm, the method can not only realize noise reduction, but also well enhance the impact characteristic of the fault, provides good basic support for fault diagnosis of the bearing, including qualitative diagnosis, quantitative diagnosis, outer ring positioning diagnosis and the like, prevents major accidents caused by the fault of the bearing, and has important practicability and engineering application value.

Drawings

The invention is further illustrated with reference to the following figures and examples.

FIG. 1 is a flow chart of a filter-based feature enhancement method of the present invention;

FIG. 2 is a diagram of an original signal to be processed containing noise interference according to the present invention;

FIG. 3 is a signal after processing with lifting wavelets according to the present invention;

fig. 4 shows the final processed signal of the filter-based feature enhancement method of the present invention.

Detailed Description

The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention.

As shown in fig. 1, the method for enhancing features based on a filter provided by the present invention specifically includes the following steps:

s1, preprocessing the acceleration signal containing noise by adopting lifting wavelet transform:

obtaining a signal s (t) with n data points by using an exponential decay function containing impact characteristics:

in the present embodiment, the displacement constant a is 208.8, the damping ratio ξ is 0.055, and the natural frequency w is set to 208.8_n4712, total points n 78644 at sampling frequency f_sTotal time t of 65536 ═ 1.2 s; then adding a large amount of noise into the s (n) signal, wherein the noise submerges the impact characteristics s (n) in the signal, so that the impact characteristics are not clearly visible any more, and the signal after noise addition is shown in FIG. 2; processing a signal to be processed containing noise by lifting wavelet transform, wherein a threshold value is set as a soft threshold value, a threshold value coefficient is set as 2.5, and an update length of a predictor is set as 20, so as to obtain a new signal f (n), as shown in fig. 3; comparing fig. 2 and 3, it can be seen that fig. 3 has revealed four impulses, but the impulses are not obvious because there is still much noise interference in the signal; the purpose of this step is to remove noise from the impulse portion of the signal.

S2, searching all peak points and all valley points in the new signal f (n):

finding all peak points in the new signal f (n), and recording the found peak points as A ═ A { (A)_pFind all valley points in the new signal f (n), and mark it as B ═ B { (B) }_hH is 1,2, …, and then the interval I between each adjacent peak points is calculated_A＝{i_A│i_A＝A_p+1，x－A_p，xCalculating the interval G between each adjacent valley point_B＝{g_B│g_B＝B_h+1，x－B_h，xIn which A is_p，xFor the peak value A in the signal_pPoint correspondenceHorizontal coordinate value of (B)_h，xAs the valley B in the signal_hThe corresponding horizontal coordinate value.

S3, constructing a signal library:

calculating the peak point interval I_A＝{i_A│i_A＝A_p+1，x－A_p，xAnd valley point spacing G_B＝{g_B│g_B＝B_h+1，x－B_h，xMaximum value of max (I)_A,G_B) Selecting the size of C₁(C₁≥max(I_A,G_B) A linear type structural element difference filtering operator carries out filtering processing on the vibration signals f (n), and the processed result is recorded as f_C1(n); then using the scale C₂(C₂≥max(I_A,G_B) A linear type structural element difference filtering operator carries out filtering processing on the vibration signals f (n), and the processed result is recorded as f_C2(n); and analogizing in sequence, after m times of processing, establishing all processing results into a signal library { f_C1(n),f_C2(n),f_C3(n),...,f_Cm(n)}。

S4, extracting coincident impact characteristics from the constructed signal library:

in signal library { f_C1(n),f_C2(n),f_C3(n),...,f_Cm(n) } extraction of the coincident impact portion z (w):

z(w)＝f_C1{find(f_C1(n)＝f_C2(n)＝...＝f_Cm(n))} (2)

the impact after extraction is shown in fig. 4. As can be seen by comparing fig. 2, 3 and 4, 4 very distinct impact signals are directly extracted from fig. 4, while no impact signal is visible in fig. 2 and no impact signal is evident in fig. 3; in addition, the amplitude in fig. 4 reaches about 3, and the amplitude in fig. 3 is only about 1, so that the impact amplitude is obviously increased.

The characteristic enhancement method based on the filter is based on the mathematical principle of the morphological algorithm, compared with the original signal and the noise-containing ratio processed by adopting the lifting wavelet transform, the method can not only realize noise reduction, but also well enhance the impact characteristic of the fault, provide good basic support for fault diagnosis of the bearing, including qualitative diagnosis, quantitative diagnosis, outer ring positioning diagnosis and the like, prevent major accidents caused by the fault of the bearing, and has important practicability and engineering application value.

In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.

Claims (2)

1. A method for filter-based feature enhancement, comprising the steps of:

s1, preprocessing the acceleration signal containing noise by adopting lifting wavelet transform:

acquiring a signal s (t) with a data point n by using an exponential decay function containing impact characteristics:

wherein A is a displacement constant, xi is a damping ratio, w_nT is n points at a natural frequency of vibration and f is a sampling frequency_sThe relationship between t and f is (1: n)/f_s(ii) a Adding noise into the s (n) signal, wherein the total amplitude of the noise is larger than that of the impact characteristic, and then preprocessing the signal to be processed containing the noise by adopting a lifting wavelet transform technology to obtain a new signal f (n);

s2, searching all peak points and all valley points in the new signal f (n):

finding all peak points in the new signal f (n), and recording the found peak points as A ═ A { (A)_pFind all valley points in the new signal f (n), and mark it as B ═ B { (B) }_hH is 1,2, …, and then the interval I between each adjacent peak points is calculated_A＝{i_A│i_A＝A_p+1，x－A_p，xCalculating the interval G between each adjacent valley point_B＝{g_B│g_B＝B_h+1，x－B_h，xIn which A is_p，xFor the peak value A in the signal_pCorresponding horizontal coordinate value, B_h，xAs the valley B in the signal_hCorresponding horizontal coordinate values;

s3, constructing a signal library:

calculating the peak point interval I_A＝{i_A│i_A＝A_p+1，x－A_p，xAnd valley point spacing G_B＝{g_B│g_B＝B_h+1，x－B_h，xMaximum value of max (I)_A,G_B) Selecting the size of C₁(C₁≥max(I_A,G_B) A linear type structural element difference filtering operator carries out filtering processing on the vibration signals f (n), and the processed result is recorded as f_C1(n); then using the scale C₂(C₂≥max(I_A,G_B) A linear type structural element difference filtering operator carries out filtering processing on the vibration signals f (n), and the processed result is recorded as f_C2(n); building all processing results into a signal library { f) until m times of processing_C1(n),f_C2(n),f_C3(n),...,f_Cm(n)}；

S4, extracting coincident impact characteristics from the constructed signal library:

in signal library { f_C1(n),f_C2(n),f_C3(n),...,f_Cm(n) } extraction of the coincident impact portion z (w):

z(w)＝f_C1{find(f_C1(n)＝f_C2(n)＝...＝f_Cm(n))} (2)。

2. the method of claim 1, wherein in step S3, the linear-type structure element difference filter operator is an operation form of morphological filtering.

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