CN103716012A - Filtering method based on iteration self-adaption multiscale morphological analysis - Google Patents
Filtering method based on iteration self-adaption multiscale morphological analysis Download PDFInfo
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Abstract
The invention discloses a filtering method based on iteration self-adaption multiscale morphological analysis. The content includes: after performing self-adaption multiscale morphological analysis filtering once, applying a self-adaption method to a filtering result of the last time to determine a new structural element scale range, using the newly determined scale range to perform multiscale morphological filtering on an original signal, iterating the abovementioned processes till the structural element scale range determined by the self-adaption method is stable, and finally averaging the scale results as a final result. The filtering method based on iteration self-adaption multiscale morphological analysis in the invention, a self-adaption multiscale morphological analysis method is used iteratively, and thus in the circumstance of a strong noise background and relatively high sampling frequency, interference of noise and harmonic can still be effective suppressed, fault features can be clearly extracted, and an ideal filtering result can be obtained. The filtering method overcomes the defects of self-adaption multiscale morphological analysis, and enriches a theoretical method of morphological filtering.
Description
Technical field
The present invention relates to a kind of filtering method of rotating machinery fault signal process field, specifically, is a kind of filtering method of analyzing based on iteration self-adapting Multiscale Morphological.
Background technology
Fault detection and diagnosis, Fault prediction techniques are the important topics of modern machinery and equipment operation maintenance and management.In numerous plant equipment, rotating machinery has obtained a large amount of application, and its fault-signal is very complicated, often shows significantly non-linear, non-stationary.How effectively to suppress the interference of noise, harmonic wave etc., the fault signature that extracts signal just becomes a key issue.
Traditional signal processing method is that to take the stationarity of signal be prerequisite, therefore, cannot extract the signal of faults characteristic information; In modern signal processing technology, although wavelet transformation, Hilbert-Huang conversion, rarefaction representation, independent component analysis, cyclo-stationary signal analysis, manifold learning etc. all have nonlinear and nonstationary signal analysis ability, they all have limitation separately.
Mathematical morphology as a kind of nonlinear properties processing method, occurs and develops in image processing field recently have scholar to be introduced into one-dimensional signal process field the earliest, and starts to apply in mechanical fault diagnosis.In the 441-445 page of the journal > > of < < University of Science & Technology, Beijing the 30th the 4th phase of volume in 2008, Zhang Lijun etc. propose in < < self-adapting multi-dimension morphological analysis and application > > mono-literary composition in bearing failure diagnosis thereof: self-adapting multi-dimension morphological analysis method is more conducive to extract the morphological feature of signal compared with single scale morphological analysis method, avoided blindness and the dependence to relevant priori of single scale morphological analysis when structural element is selected, but in strong noise background and sample frequency when higher, noise and harmonic wave can not be effectively suppressed, fault characteristic information can not effectively be extracted.
Summary of the invention
For in strong noise background and sample frequency when higher, after traditional self-adapting multi-dimension morphological analysis filtering, not obvious this problem of extraction of fault characteristic information.The object of this invention is to provide a kind of service condition more wide in range, the signal filtering method that filter effect is more stable.
To achieve these goals, the present invention is achieved by the following technical solutions:
A filtering method of analyzing (Iterative adaptive multiscale morphology analysis, IAMMA) based on iteration self-adapting Multiscale Morphological, its content comprises the steps:
Object according to difformity structural element on the impact of signal filtering result and signal filtering, selects suitable structural element shape; Conventionally structural element length value is taken as odd number, to its mid point is made as to initial point, thus the symmetry of assurance structural element;
Conventional structural element shape has triangular form, semi-circular, platypelloid type, wherein triangular form structural element is applicable to the interference of filtering pulse signal, semi-circular structural element is applicable to the interference of filtering random noise, and platypelloid type structural element conventionally to get its height value be zero, only need select length value, more easy, be easy to realize, thereby be widely used in one-dimensional signal is processed;
Primary signal is carried out to self-adapting multi-dimension morphological analysis filtering, first the discrete state signal gathering is carried out to zero-mean processing, look for zero-meanization and process all positive peak points in rear discrete signal, then according to each trunnion axis position, positive peak point place (abscissa), can obtain the interval of each adjacent positive peak point, according to the minimum value at adjacent positive peak interval and maximum, determine structural element length dimension range lambda
l; Afterwards according to each vertical axes position, positive peak point place (ordinate), according to the computing of the policy setting of length small scale respective heights small scale and length large scale respective heights large scale, can calculate elevational dimension corresponding to each length dimension of structural element, thereby determine each yardstick structural element shape; Finally calculate respectively the filtering result under each yardstick structural element, and the Output rusults using the mean value of each yardstick morphology operations result as AMMA method;
Step 3 iteration is used self-adapting multi-dimension morphological analysis
To last filtering result, adopt adaptive approach to determine new structural element range scale, and primary signal is carried out to Multi-Scale Morphological Filtering, iteration said process with new definite range scale; By the selected structural element range scale of adaptive approach, signal is carried out to multiple dimensioned differential filtering, each filtering all can be carried out filtering to the noise contribution in signal, extracts simultaneously and impacts composition; In so each iterative process, noise peak reduces gradually, and the structural element yardstick of choosing increases gradually, and the ability of poor morphology value filtering removal noise is strengthened gradually, and final poor morphology value filtering is by the most of effectively filtering of the noise in signal; The structural element out to out of now choosing tends towards stability, and adaptive process finishes; Interval between the adjacent positive peak that self adaptation is chosen is thus more the interval between the adjacent positive peak of failure impact signal, is more rational gap length; The structural element range scale obtaining according to this gap length is more suitable, thereby can obtain more preferably signal filtering result;
Step 4 stops judgement
The condition that judgement iterative process stops is: the determined structural element yardstick of adaptive approach maximum continuous three times identical; In determining step three, whether each new structural element yardstick maximum that adopts adaptive approach to determine changes, if the new structural element yardstick maximum that adaptive approach is determined continuous three times identical, iterative process finishes;
Step 5 is calculated iteration self-adapting Multiscale Morphological analysis result
Calculate the mean value of differential filtering result under each yardstick, using this value as the final result based on iteration self-adapting Multiscale Morphological analysis filtered method processing signals; Under each yardstick, differential filtering result reflects respectively the characteristic information of signal under different scale, and the impact composition that carries mechanical device fault information is present in each yardstick, therefore the result sequence under each yardstick of gained is averaged as the final result of IAMMA method, the characteristic information of reflected signal more fully, and outstanding its impacts composition, effectively filtering noise impact.
The bearing vibration data analysis that adopts respectively IAMMA, AMMA method to gather U.S. CWRU (Case Western Reserve University) bearing test-bed.Adopt respectively the frequency spectrum of the bearing inner race fault that two kinds of methods obtain as shown in Figure 1.As seen from the figure, Fig. 1 (a) is original signal spectrum figure, IAMMA method filtering result Fig. 1 (c) has clearly presented rolling bearing inner ring fault characteristic frequency 162.2Hz and frequency multiplication thereof, and fault characteristic frequency in AMMA method filtering result Fig. 1 (b) is submerged in noise.
Owing to adopting technique scheme, a kind of filtering method of analyzing based on iteration self-adapting Multiscale Morphological provided by the invention, compared with prior art has such beneficial effect:
The present invention is based on the filtering method that iteration self-adapting Multiscale Morphological is analyzed, self-adapting multi-dimension morphological analysis method has been carried out to iteration use, can be in strong noise background and sample frequency when higher, still can effectively suppress the interference of noise, harmonic wave etc., clear extraction fault signature, obtains ideal filtering result.It has made up the deficiency of self-adapting multi-dimension morphological analysis, has enriched the theoretical method of shape filtering.
Accompanying drawing explanation
Fig. 1 adopts respectively AMMA and the spectral contrast figure of IAMMA method to rolling bearing inner ring failure recovery result, and wherein (a) is original signal spectrum figure, is (b) AMMA method spectrogram, is (c) IAMMA method spectrogram;
Fig. 2 is the specific implementation flow chart of steps of analyzing (IAMMA) filtering method based on iteration self-adapting Multiscale Morphological of the present invention.
Embodiment
Below in conjunction with accompanying drawing, the filtering method based on the analysis of iteration self-adapting Multiscale Morphological of the present invention is further described.
A filtering method of analyzing based on iteration self-adapting Multiscale Morphological, as shown in Figure 2, it is as follows that the method comprises concrete steps:
Impact according to difformity structural element on signal filtering result, and the object of signal filtering, select suitable structural element shape.
The morphological analysis of step 2 self-adapting multi-dimension
First the discrete state signal f (n) gathering is carried out to zero-mean processing, obtain on this basis its discrete series f
1(n)={ x
n| n=1,2 ..., N}, looks for f
1(n) all positive peak point P={p in
m| m=1,2 ..., M}, according to each positive peak point p
mtrunnion axis position, place (abscissa) p
m,x, can obtain being spaced apart between each adjacent positive peak point: I
p={ i
p| i
p=p
m+1, x-p
m, x,m=1,2 ..., M-1}.
According to the interval I of adjacent peak point
pand following two formulas:
Can obtain Multiscale Morphological length dimension range lambda
l: λ
l={ λ
lmin, λ
lmin+ 1 ..., λ
lmax(j)-1, λ
lmax(j) }, j=1 wherein.In formula,
for the operator that rounds up,
for downward rounding operation symbol.
By each positive peak point p
mvertical axes position, place (ordinate) p
m,ysubstitution following formula:
λ
h=β·{min(p
m,y)+α·[max(p
m,y)-min(p
m,y)]/(λ
lmax-λ
lmin)} (3)
Can calculate structural element elevational dimension range lambda
hthereby, finally determine each yardstick structural element shape.Wherein, α=1,2 ..., λ
lmax-λ
lmin+ 1, β is height ratio coefficient (0< β <1).
Under selected structural element yardstick, discrete signal is carried out to the computing of poor morphology value filtering respectively, obtain the filtering result result_data (k, 1:1:N) under each yardstick, wherein k=λ
lmin(1): 1: λ
lmax(j), j=1.Calculate the mean value multi_result_data of filtering result result_data (k, 1:1:N) under each yardstick as the result of this filtering.
Step 3 iteration is used self-adapting multi-dimension morphological analysis
Find out each positive peak point P ' in the time domain form of consequential signal multi_result_data=p '
m| m=1,2 ..., M ' }, according to each positive peak point p '
mtrunnion axis position, place p '
m,x, can obtain being spaced apart between each adjacent positive peak point: I '
p={ i
p| i
p=p '
m+1, x-p '
m,x, m=1,2 ..., M '-1}.According to gap size between each peak point, select the maximum λ of structural element length dimension
lmax(j+1).Judgement λ
lmaxand λ (j+1)
lmax(j) size, if λ
lmax(j+1) be greater than λ
lmax(j), continue to carry out, otherwise leap to step 4.
The structural element yardstick maximum λ choosing with the last time
lmax(j) as the minimum value of this filtering, the structural element yardstick maximum λ that this is chosen
lmax(j+1), still as maximum, determine the structural element length dimension range lambda that this filtering adopts
l' be: λ
l'={ λ
lmax(j), λ
lmax(j)+1 ..., λ
lmax(j+1)-1, λ
lmax(j+1) }.According to formula (3), can calculate structural element elevational dimension range lambda
h', thereby finally determine each yardstick structural element shape.
Under selected structural element yardstick, original discrete signal is carried out to the computing of poor morphology value filtering respectively, obtain the filtering result result_data (k, 1:1:N) under each yardstick, wherein k=λ
lmax(j): 1: λ
lmax(j+1).Calculate the mean value multi_result_data of differential filtering result result_data (k, 1:1:N) under each yardstick as the result of this filtering.
J is after adding 1, and the multi_result_data of usining processes from step 3 is initial again as object.
Step 4 stops judgement
Judgement λ
lmax(j+1) whether with λ
lmax(j) equate if unequal j jumps to step 3 after adding 1, otherwise judge whether j equals 1, when j=1, j is usingd multi_result_data and again from step 3 is initial, is processed as object after adding 1, otherwise judgement λ
lmax(j+1) whether with λ
lmax(j-1) equate, if equate, jump to step 5, otherwise j is usingd multi_result_data and again from step 3 is initial, processed as object after adding 1.
Step 5 is calculated iteration self-adapting Multiscale Morphological analysis result
Calculate the mean value multi_result_data of differential filtering result result_data (k, 1:1:N) under each yardstick as the final result of IAMMA method processing signals, wherein k=λ
lmin(1): 1: λ
lmax(j+1).Differential filtering result result_data (k under each yardstick, 1:1:N) reflecting respectively the characteristic information of signal under different scale, and the impact composition that carries mechanical device fault information is present in each yardstick, therefore the result sequence under each yardstick of gained is averaged as the final result of IAMMA method, the characteristic information of reflected signal more fully, and outstanding its impacts composition, effectively filtering noise impact.
Claims (1)
1. a filtering method of analyzing based on iteration self-adapting Multiscale Morphological, is characterized in that: the method content comprises the steps:
Step 1 choice structure element shape
Object according to difformity structural element on the impact of signal filtering result and signal filtering, selects suitable structural element shape; Conventionally structural element length value is taken as odd number, to its mid point is made as to initial point, thus the symmetry of assurance structural element;
Conventional structural element shape has triangular form, semi-circular, platypelloid type, wherein triangular form structural element is applicable to the interference of filtering pulse signal, semi-circular structural element is applicable to the interference of filtering random noise, it is zero that platypelloid type structural element is got its height value conventionally, only need select length value, more easy, be easy to realize;
The morphological analysis of step 2 self-adapting multi-dimension
Primary signal is carried out to self-adapting multi-dimension morphological analysis filtering, first the discrete state signal gathering is carried out to zero-mean processing, find out all positive peak points in the discrete signal after zero-meanization is processed, then according to each trunnion axis position, positive peak point place, can obtain the interval of each adjacent positive peak point, according to the minimum value at adjacent positive peak interval and maximum, determine structural element length dimension scope; Afterwards according to each vertical axes position, positive peak point place, according to the computing of the policy setting of length small scale respective heights small scale and length large scale respective heights large scale, can calculate elevational dimension corresponding to each length dimension of structural element, thereby determine each yardstick structural element shape; Finally calculate respectively the morphologic filtering result of each yardstick structural element, and the Output rusults using the mean value of each yardstick morphologic filtering result as AMMA method;
Step 3 iteration is used self-adapting multi-dimension morphological analysis
To last filtering result, adopt adaptive approach to determine new structural element range scale, and primary signal is carried out to Multi-Scale Morphological Filtering, iteration said process with new definite range scale; By the selected structural element range scale of adaptive approach, signal is carried out to multiple dimensioned differential filtering, each filtering all can be carried out filtering to the noise contribution in signal, extracts simultaneously and impacts composition; In so each iterative process, noise peak reduces gradually, and the structural element yardstick of choosing increases gradually, and the ability of poor morphology value filtering removal noise is strengthened gradually, and final poor morphology value filtering is by the most of effectively filtering of the noise in signal; The structural element out to out of now choosing tends towards stability, and adaptive process finishes; Interval between the adjacent positive peak that self adaptation is chosen is thus more the interval between the adjacent positive peak of failure impact signal, is more rational gap length; The structural element range scale obtaining according to this gap length is more suitable, thereby can obtain more preferably signal filtering result;
Step 4 stops judgement
The condition that judgement iterative process stops is: the determined structural element yardstick of adaptive approach maximum continuous three times identical; In determining step three, whether each new structural element yardstick maximum that adopts adaptive approach to determine changes, if the new structural element yardstick maximum that adaptive approach is determined continuous three times identical, iterative process finishes;
Step 5 is calculated iteration self-adapting Multiscale Morphological analysis result
Calculate the mean value of differential filtering result under each yardstick, using this value as the final result based on iteration self-adapting Multiscale Morphological analysis filtered method processing signals; Under each yardstick, differential filtering result reflects respectively the characteristic information of signal under different scale, and the impact composition that carries mechanical device fault information is present in each yardstick, therefore the result sequence under each yardstick of gained is averaged as the final result of IAMMA method, the characteristic information of reflected signal more fully, and outstanding its impacts composition, effectively filtering noise impact.
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| CN106500991A (en) * | 2016-10-14 | 2017-03-15 | 石家庄铁道大学 | Based on the bearing fault signal characteristic extracting methods that self-adapting multi-dimension AVGH is converted |
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