CN106487359A - The building method of the Morphologic filters converted based on self-adapting multi-dimension AVG Hat - Google Patents
The building method of the Morphologic filters converted based on self-adapting multi-dimension AVG Hat Download PDFInfo
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- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
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- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
- H03H17/00—Networks using digital techniques
- H03H17/02—Frequency selective networks
- H03H17/0211—Frequency selective networks using specific transformation algorithms, e.g. WALSH functions, Fermat transforms, Mersenne transforms, polynomial transforms, Hilbert transforms
Abstract
The invention discloses a kind of building method of the Morphologic filters converted based on self-adapting multi-dimension AVG Hat, realizes step as follows：1st, the parameter index according to set by bearing fault signal, determines the number of the initial Multi-scale model element of signal and initial structural element value；2nd, pass through morphological dilations computing, build the set of initial Multi-scale model element composition；3rd, the result of calculation bearing fault vibration signal corresponding morphology AVG Hat conversion under initial Multi-scale model element, builds the set of the result；4th, pass through particle group optimizing method, from filtered bearing fault vibration signal arrangement entropy and envelope spectrum degree of rarefication ratio as evaluation index, adaptive should determine that the initial corresponding optimal weights coefficient of Multi-scale model element after filtering；5th, according to the weight coefficient, optimum Multiscale Morphological AVG Hat wave filter is built.It is an advantage of the invention that signal de-noising, fault signature can be taken into account extract and keep filter result accuracy.
Description
Technical field
The present invention relates to a kind of building method of the Morphologic filters converted based on self-adapting multi-dimension AVG-Hat, its
Belong to mechanical fault diagnosis and signal processing technology field.
Background technology
In Practical Project, rolling bearing fault vibration signal is typical non-linear, non-stationary signal, and in signal, fault is special
Levying and easily being covered by various ambient noises, the difficulty that bearing fault is therefore diagnosed under strong background noise is very big.Mathematical Morphology
Be a kind of typical Nonlinear harmonic oscillator method, and it is by the structural element of particular dimensions and shape to time domain plethysmographic signal
Local detail be fitted finishing, in signal is extracted while main wave character, can effectively eliminate the dry of ambient noise
Disturb.Using morphological method handling failure signal, it is important to the Morphologic filters of ad hoc structure to be built, traditional morphology
Wave filter is mainly expanded by burn into, opening operation and closed operation are combined, and is mainly used in eliminating ambient noise in fault-signal,
The ability for extracting fault signature is weaker.Some new Morphologic filters are such as：Differential filtering device, gradient filter, although lay particular stress on
The extraction of fault signature in the signal, but the negative value information in signal is changed into, on the occasion of information, changing letter by these wave filters
Number constituent, the robustness of result is poor.
In signal waveform the structural object of particular dimensions can only by the structural elements of particular dimensions usually alignment processing, therefore,
During morphologic filtering, the effect of fault signature extraction and noise reduction filtering depends primarily on the selection of structural element yardstick.Mesh
The front main Morphologic filters built using single mesostructure element are processed to fault-signal, but, fault vibration
Signal is extremely complex, the structural object comprising multiple different scales, and single scale topographical filtering tends not to fully analyze
Signal.Compare using single yardstick, multi-scale morphology filtering can process different scale size with the structural element of different scale
Structural object.Therefore, morphologic filtering being carried out to signal using multiple different scale structural elements, there is more outstanding suppression
Noise processed and ability in feature extraction.
The diagnosis of rolling bearing fault vibration signal is realized by multi-scale morphology filtering device, needs to solve two to ask
Topic：One is to build one to take into account the morphologic filtering that signal de-noising, fault signature are extracted and keep filter result accuracy
Device；Two be adaptive should determine that multi-scale morphology filtering device composition in each weight coefficient shared by mesostructure element.And
In prior art, the correlation technique that can not solve the two key issues very well is recorded.This also becomes people in the art
The problem of member's urgent need to resolve.
Content of the invention
The technical problem to be solved there is provided one kind and can take into account signal de-noising, fault signature extraction and protect
Hold the building method of the Morphologic filters converted based on self-adapting multi-dimension AVG-Hat of filter result accuracy.
The present invention is adopted the following technical scheme that：
A kind of building method of the Morphologic filters converted based on self-adapting multi-dimension AVG-Hat, which includes following step
Suddenly：
Step 1, the parameters index according to set by sensor gathers bearing fault signal, determine that signal Analysis are wanted
The number and initial structural element value of the initial Multi-scale model element for using；
Step 2, the set constituted by morphological dilations computing, the initial Multi-scale model element of structure；
Step 3, calculation bearing fault vibration signal corresponding morphology AVG-Hat under initial Multi-scale model element becomes
The result that changes, builds the set of the result of signal aspect AVG-Hat conversion under initial Multi-scale model element；
Step 4, pass through particle group optimizing method, from after morphology AVG-Hat filter filtering bearing fault vibrate
The ratio of arrangement entropy and the envelope spectrum degree of rarefication of signal as evaluation index, as the adaptive optimal control of particle group optimizing method
Degree functional value, completes population optimizing iterative process, adaptive should determine that optimum Multiscale Morphological AVG-Hat filter filtering after
The initial corresponding optimal weights coefficient of Multi-scale model element；
Step 5, according to the corresponding weight coefficient of different scale structural element in the step 4, build optimum multiple dimensioned
Morphology AVG-Hat wave filter.
Further, signal aspect AVG-Hat conversion under initial Multi-scale model element is built in the step 3
As a result set, specifically adopts with the following method：
Smallest dimension structural element g=[00] of 3-1, acquisition bearing fault vibration signal x (n) and initial setting up, output
The corresponding morphological dilations operation result of signalErosion operation result (x Θ g) (n), opening operation resultAnd closed operation result (x g) (n)；
3-2, acquisition bearing fault vibration signal x (n) and bearing fault vibration signal x (n) are through initial configuration unit
Result after plain morphology opening operation, closed operation, original bearing fault vibration signal x (n) of output deduct bearing fault after taking advantage of 2 and shake
Dynamic signal x (n) through morphology opening operation, the difference value of closed operation result sum, its mathematic(al) representation is：Bearing fault signal x (n) is calculated through just
Result AVGH (f (n)) of beginning single structure element morphology AVG-Hat filtering；
The initial structural element yardstick number of the initial smallest dimension structural element of 3-3, acquisition and setting, export structure unit
Result after plain λ Multi-scale model element dilation operation, its mathematic(al) representation is：Expand
Computing λ time, the set of the result that sets up after multiple mesostructure element dilation operations：That is { g1,g2,…gλ}；
While calculating multiple dimensioned erosion operation result (the x Θ g of corresponding bearing fault vibration signal x (n)λ) (n), expansion
Operation resultOpening operation resultAnd closed operation result (x gλ)(n)；
3-4, acquisition bearing fault vibration signal x (n) and λ Multi-scale model element lower bearing fault vibration signal x (n)
Opening operation, the result of closed operation, the result of morphology AVG-Hat conversion under the corresponding λ Multi-scale model element of output, which counts
Learning expression formula is：Set up λ Multi-scale model unit
The set of signal after plain morphology AVG-Hat filter filtering：That is { f1(n),f2(n),…,fλ(n)}.
Further, pass through particle group optimizing method in the step 4, adaptive should determine that optimum Multiscale Morphological AVG-
After Hat filter filtering, the concrete grammar of the corresponding optimal weights coefficient of initial Multi-scale model element is as follows：
Set { the g of 4-1, acquisition bearing fault vibration signal x (n) and λ Multi-scale model element1,g2,…gλ, output
The set of signal after λ Multi-scale model element morphology AVG-Hat filter filtering：That is { f1(n),f2(n),…,fλ(n)}；
Set { the f of signal after 4-2, λ Multi-scale model element morphology AVG-Hat filter filtering of acquisition1(n),f2
(n),…,fλ(n) } and the originally determined weight coefficient ω=(ω of filtered signal described in each yardstick1,ω2,…,ωλ),
ωi∈ [0,1] (0≤i≤λ), output initial weight coefficient are filtered with corresponding λ Multi-scale model element morphology AVG-Hat
The sum of products of device filtered signal：I.e.It is calculated the arrangement entropy H of the sum of products signalPEAnd bag
Sparse angle value S composed by network, by the arrangement entropy HPERemove with the sparse angle value S-phase of envelope spectrum the filtered signal is obtained in population
Fitness function value P in optimization method, its mathematic(al) representation is：
4-3, after the I time interative computation of population signal fitness function value PISecondary less than or equal to (I-1) repeatedly
Fitness function value P for signal after computingI-1When, i.e. PI≤PI-1, remember PIFor optimum fitness function value；The like, obtain
The whole process of particle group optimizing method iteration is taken, exports the minimum fitness letter of all sum of products signals after G interative computation
Numerical value PbestAs fitness function value, i.e. P optimum during whole particle group optimizingbest=min (P1,P2,…,PG)；Its
In, G is maximum iteration time；
4-4, extraction minimum fitness function value PbestCorresponding Multi-scale model element weights coefficient ωbest=
(ω1,ω2,…,ωλ) as the corresponding optimal weights coefficient of Multi-scale model element.
Further, the method for building the Multiscale Morphological AVG-Hat wave filter of optimum in the step 5 is as follows：Meter
Calculate optimal weights coefficient ωbest=(ω1,ω2,…,ωλ) filter with λ Multi-scale model element morphology AVG-Hat wave filter
Signal f after rippleiN the sum of products of (), using the sum of products signal as many of the adaptive optimum that should determine that of particle group optimizing method
Mesostructure element morphology AVG-Hat wave filter composition form, wherein ωi∈ωbest,fi(n)={ f1(n),f2(n),…,
fλ(n)}.
Beneficial effects of the present invention are as follows：
(1) present invention is overcome traditional form wave filter and exists by building a new morphology AVG-Hat wave filter
Existed during handling failure signal can not take into account the defect that signal de-noising and fault signature are extracted, permissible in handling failure signal
The fault shock characteristic that in signal contain effectively is extracted.
(2) present invention calculates the arrangement entropy of filtered signal and the ratio of envelope spectrum degree of rarefication as evaluation criterion, utilizes
Particle group optimizing method, determines each mesostructure unit in optimum multi-scale morphology filtering device by interative computation self adaptation
The corresponding weight coefficient of element, so as to construct Multiscale Morphological AVG-Hat wave filter.The morphology AVG- of present invention construction
Hat wave filter, can effectively extract the shock characteristic in fault-signal on the basis of fault-signal ambient noise is eliminated, and
And will not change signal composition increase point, do not produce extra interference composition, filter result is accurately and reliably.
(3) present invention builds multi-scale morphology filtering device, can select the structural element pair of multiple different scale sizes
Fault-signal carries out morphologic filtering, compares the filtering of single scale topographical, and the fault-signal of analysis composition complicated component is more
Scientific and reasonable, the defect that single scaling filter causes to take into account signal de-noising and details retentivity is effectively overcome, is carried
In the number of winning the confidence, the ability of fault shock characteristic is higher；
(4) present invention obtains the index of Optimal Signals after evaluation multi-scale filtering, is the row by calculating filtered signal
The ratio of row entropy and envelope spectrum degree of rarefication is worth to.The arrangement entropy of signal represents the regular degree of burst, is to weigh letter
Number contain the index of ambient noise size；Signal envelope spectrum degree of rarefication then reflects the size that fault in signal impacts composition, because
Ratio both this has considered the size of ambient noise in signal and fault signature, with clear and definite physical significance, with
This can effectively choose the Optimal Signals after multi-scale morphology filtering for evaluation criterion.
(5) present invention using adaptive should determine that multi-scale morphology filtering composition particle group optimizing method, by particle
Group's optimizing iterative process, finds the minimum corresponding Multi-scale model element weights coefficient of interative computation post-evaluation standard as optimum
The corresponding weight coefficient of multi-scale filtering device, self adaptation determine the building form of multi-scale morphology filtering device, it is to avoid with
Toward the defect for determining that by artificial experience multi-scale filtering device is present when constituting, filter result is used directly for extracting fault
Feature, and adaptive adjustment can be carried out to the signal that is analyzed, analysis efficiency is higher.
Description of the drawings
Fig. 1 is Multiscale Morphological AVG-Hat filtering structural representation in the present invention.
Fig. 2 is fitness function value solution procedure schematic diagram in particle group optimizing method in the present invention.
Fig. 3 is the time domain waveform of rolling bearing fault vibration signal in embodiments of the invention.
Fig. 4 is the envelope spectrogram of rolling bearing fault vibration signal in embodiments of the invention.
Fig. 5 is the time domain beamformer of Multiscale Morphological AVG-Hat filtered signal in embodiments of the invention.
Fig. 6 is the envelope spectrogram of Multiscale Morphological AVG-Hat filtered signal in embodiments of the invention.
Specific embodiment
1～Fig. 6 and specific embodiment are based on self-adapting multi-dimension to one kind proposed by the invention below in conjunction with the accompanying drawings
The building method of the Morphologic filters of AVG-Hat conversion is described in detail.
Spy according to fault shock characteristic in four kinds of basic operations of Morphological scale-space one-dimensional signal and bearing fault signal
Point, it is proposed that the definition of morphology AVG-Hat conversion and the building method of morphology AVG-Hat wave filter.
As shown in Fig. 1～Fig. 6, this example describes the form for building self-adapting multi-dimension morphology AVG-Hat conversion
The method for learning wave filter, and actual housing washer fault vibration signal is diagnosed by the method.
Detailed process is as follows：
Step 101：The sample frequency of acceleration transducer is set, gathers rolling bearing fault vibration signal；
Step 102：Structural parameters and the rotating shaft rotating speed of rolling bearing is obtained, according to each element fault feature of rolling bearing
Frequency computing formula, obtains the fault characteristic frequency of each element of surveyed bearing, in conjunction with the sample frequency of sensor, calculates vibration
Sampling number in signal fault impulse period, in this, as the out to out of structural element used, meanwhile, setting is initial most
Small-scale structure element, the setting of completion morphology filter parameter.
Step 103：By dilation operation, initial configuration element is carried out scale expansion successively, largest extension is to setting
Structural element out to out, so as to establish the structural element set of multiple different scales；
Step 104：Structural element to different scale builds morphology AVG-Hat wave filter respectively, so as to establish not
Set with the AVG-Hat wave filter of yardstick；
Step 105：The parameters of particle cluster algorithm are set, including：Maximum iteration time, population scale, acceleration because
Son, the excursion of random factor and each mesostructure element respective weights coefficient, the scope for arranging here is [0,1].
Step 106：According to the random determination rule in particle cluster algorithm with regard to weight coefficient, when obtaining interative computation first
The corresponding weight coefficient of each mesostructure element；
Step 107：Obtain the AVG-Hat filter filtering signal results that different scale structural element is set up set and
The corresponding weight coefficient of different scale structural element, builds the Multiscale Morphological AVG- for being set up in particle swarm optimization algorithm first
Hat wave filter, exports the filtered signal of multi-scale filtering device first；
Step 108：The evaluation criterion of filtering signal first is calculated, in this, as the fitness function value of the signal；
Step 109：Judging whether to meet the stopping criterion of particle swarm optimization algorithm, if be unsatisfactory for, carrying out second
Interative computation, repeat step 106-108, the fitness function value of second signal is obtained, records the fitness function of minimum
Its corresponding each mesostructure element weights coefficient is optimal result, carries out again judging whether that the stopping for meeting algorithm is accurate
Then, the like.If meeting the stopping criterion of particle cluster algorithm, then terminate particle group optimizing process, optimal result is exported.
Step 110：According to each corresponding weight coefficient of mesostructure element in output optimal result, in conjunction with different scale
The set of the AVG-Hat filter filtering signal results that structural element is set up, sets up the adaptive optimum that should determine that of optimized algorithm many
Scale topographical wave filter, exports filtered signal.
As shown in figure 1, Multiscale Morphological AVG-Hat filtering of the present invention details are as follows：
Step 201：Obtain smallest dimension structural element g=[0 0] and the max architecture element dimensions number of initial setting up
λmax, successively morphological dilations computing is carried out to smallest dimension structural element, until dilation operation is to max architecture element dimensions
Count, mathematic(al) representation is：
Wherein, λ is the scale parameter of Multi-scale model element.Thus establish and be made up of multiple different scale structural elements
Set { g1, g2,…,gλ}.
Step 202：The morphological dilations computing of signal is carried out to each mesostructure elementErosion operation
(x Θ g) (n), opening operationAnd closed operation (x g) (n), set up morphology AVG-Hat wave filter, mathematic(al) representation
For：
Wherein, x (n) is rolling bearing fault signal.Fault vibration signal is divided under each mesostructure element
The morphology AVG-Hat filtering process not carried out, believes after obtaining multiple mesostructure element morphology AVG-Hat filter filterings
Number set：{f1(n),f2(n),…,fλ(n)}；
Step 203：The each mesostructure element respective weights coefficient determined during obtaining particle group optimizing
ω=(ω1,ω2,…,ωλ),ωi∈ [0,1] (0≤i≤λ), in conjunction with multiple mesostructure element morphologies
The set of AVG-Hat filtered signal, obtains the filtered final signal of Multiscale Morphological AVG-Hat, is expressed as：
Wherein, y is the filtered signal of Multiscale Morphological AVG-Hat.
As shown in Fig. 2 in particle swarm optimization algorithm of the present invention fitness function value solution procedure details are as follows：
Step 301：During obtaining particle group optimizing, filtered signal y (n) of multiple dimensioned AVG-Hat；
Step 302：The phase space reconfiguration matrix of filtered signal is built, obtaining matrix is：
Wherein, m and τ is respectively dimension and the time delay of embeded matrix, k=n- (m-1) τ.
A reconstruct component is regarded in space matrix per a line as, rearrange according to ascending order, j1,j2,...,jmRepresent and divide
The sequence of each element in amount, then a group code sequence can be obtained for this space matrix：
X (j)=(j1,j2,...,jm), wherein, 1≤j≤k.
M-dimensional space matrix has m！=1 × 2 × ... the possible symbol sebolic addressing of × m kind, it is general that each symbol sebolic addressing occurs
Rate is P1,P2,…,Pk.According to the definition of Shannon entropy, after normalization, the arrangement entropy of signal is defined as：
Wherein, HPEArrangement entropy for filtered signal.
Step 303：Envelope spectrum analysis are carried out to filtered signal, obtains envelope spectrum sequence X (n) of the signal.Calculate bag
The degree of rarefication of network spectral sequence, is expressed as：
Wherein, S is the envelope spectrum degree of rarefication of signal, and N is the length of signal.
Step 304：The arrangement entropy of filtered signal is divided by with the sparse angle value of envelope spectrum, obtains particle swarm optimization algorithm
Evaluation criterion, in this, as the fitness function value of algorithm, is expressed as：
Wherein, F is the fitness function value of filtered signal.
This example describes the housing washer actual by self-adapting multi-dimension morphology AVG-Hat conversion diagnosis
Fault vibration signal.
The housing washer fault-signal of sensor collection is expressed as x (n), and the wherein length of signal is 8192 points.
The time domain waveform of fault-signal and envelope spectrum frequency spectrum are not found significantly as shown in accompanying drawing 3 and Fig. 4 from time domain plethysmographic signal
Fault impulse period, does not extract obvious outer ring fault characteristic frequency and frequency multiplication composition in envelope spectrum yet, therefore cannot examine
The disconnected bearing outer ring fault；
The structural parameters of rolling bearing are as follows：Pitch diameter is 176.29mm, a diameter of 24.74mm of rolling element, rolling element
Number is 20, and contact angle is 8.83 °.Rotating shaft rotating speed is 465r/min, and the sample frequency of signal is 25600HZ.Calculate axle
Bearing outer-ring fault characteristic frequency is 66.75Hz, and it is 25600 ÷, 66.75 ≈ to calculate sampled point of the vibration signal in inaction interval
385.3, therefore initial smallest dimension structural element is set for g1=[00], structural element out to out number are 385；
By structural element dilation operation, the set { g of multiple mesostructure elements is set up1,g2,…,g384, have 384
The structural element of individual different scale, and the yardstick of structural element increases successively；
Morphology AVG-Hat wave filter under construction different scale structural element, obtains fault-signal different scale structural elements
Set { the x of plain filtered signal1(n),x2(n),…,x384(n)}；
The parameters of particle cluster algorithm are set：Maximum iteration time G=10, population scale M=20, acceleration factor c1
=1.5, c2=1.5, random factor r1=0.9, r2=0.4, the corresponding weight coefficient scope of each mesostructure element be [0,
1].According to particle swarm optimization algorithm, the weight coefficient of each mesostructure element is determined at random, build Multiscale Morphological AVG-
Hat wave filter, calculates the evaluation criterion of filtered signal, remembers that minimum evaluation criterion is optimum fitness function value.Carry out
Population interative computation, until meeting the stopping criterion of optimized algorithm, exports minimum fitness function value and each mesostructure
The corresponding weight coefficient ω=(ω of element1,ω2,…,ω384),ωi∈[0,1](0≤i≤384).
According to the optimal result of population self adaptation output, Multiscale Morphological AVG-Hat wave filter is set up, to bearing event
Barrier signal be filtered process, the time domain waveform of filtered signal as shown in Figure 5, from the time domain waveform in figure of filtered signal
Bearing outer ring fault impact composition can clearly be found.Filtered signal carries out envelope spectrum analysis, envelope spectrum result such as Fig. 6
Shown, housing washer fault characteristic frequency 66.7Hz has clearly been extracted from envelope spectrum, two frequencys multiplication and frequency tripling become
Divide 133Hz and 198Hz.
By envelope spectrum analysis, the outer ring feature of bearing fault vibration signal has effectively been extracted, it is achieved that outside the bearing
The Accurate Diagnosis of circle fault.
This example sets up multiple scale topographical by the housing washer fault vibration signal of one section of reality of analysis
The set of AVG-Hat filtered signal, determines different scale in multi-scale filtering device by particle swarm optimization algorithm self adaptation
The corresponding weight coefficient of structural element, constructs the multi-scale morphological AVG-Hat wave filter of optimum, by filtered signal
The envelope spectrum analysis Accurate Diagnosis fault vibration signal.The present invention passes through to build a new morphology AVG-Hat wave filter,
The fault signature in fault-signal can effectively be extracted on the basis of signal de-noising performance is taken into account.Form proposed by the present invention
Multi-scale filtering device self-adaptive construction method is learned for solving how to build multi-scale morphology filtering device offer one effectively later
Solution, can be according to institute's signal Analysis fault signature, the optimum Morphologic filters of adaptively selected structure, this is many
Scale topographical method is applied to mechanical fault diagnosis and provides accurate establishing criteria.
Specific case used herein is set forth to the principle of the present invention and embodiment, the saying of above example
Bright it is only intended to help and understands the method for the present invention and its core concept；Simultaneously for one of ordinary skill in the art, foundation
The thought of the present invention, all will change in specific embodiments and applications.To sum up, this specification content should not be managed
Solve as limitation of the present invention.
Claims (4)
1. a kind of based on self-adapting multi-dimension AVG-Hat convert Morphologic filters building method, it is characterised in that：Which is adopted
Use following steps：
Step 1, the parameters index according to set by sensor gathers bearing fault signal, determine that signal Analysis will be used
The number of initial Multi-scale model element and initial structural element value；
Step 2, the set constituted by morphological dilations computing, the initial Multi-scale model element of structure；
The corresponding morphology AVG-Hat conversion under initial Multi-scale model element of step 3, calculation bearing fault vibration signal
As a result, the set of the result of signal aspect AVG-Hat conversion under initial Multi-scale model element is built；
Step 4, pass through particle group optimizing method, from the bearing fault vibration signal after morphology AVG-Hat filter filtering
Arrangement entropy and envelope spectrum degree of rarefication ratio as evaluation index, as the adaptive optimal control degree letter of particle group optimizing method
Numerical value, completes population optimizing iterative process, adaptive should determine that initial after optimum Multiscale Morphological AVG-Hat filter filtering
The corresponding optimal weights coefficient of Multi-scale model element；
Step 5, according to the corresponding weight coefficient of different scale structural element in the step 4, build optimum Multiscale Morphological
Learn AVG-Hat wave filter.
2. according to claim 1 based on self-adapting multi-dimension AVG-Hat convert Morphologic filters building method,
It is characterized in that：The result of signal aspect AVG-Hat conversion under initial Multi-scale model element is built in the step 3
Set, specifically adopts with the following method：
Smallest dimension structural element g=[0 0] of 3-1, acquisition bearing fault vibration signal x (n) and initial setting up, output signal
Corresponding morphological dilations operation resultErosion operation result (x Θ g) (n), opening operation resultAnd
Closed operation result (x g) (n)；
3-2, acquisition bearing fault vibration signal x (n) and bearing fault vibration signal x (n) are through initial configuration element shape
Result after state opening operation, closed operation, exports and deducts bearing fault vibration letter after original bearing fault vibration signal x (n) takes advantage of 2
, through morphology opening operation, the difference value of closed operation result sum, its mathematic(al) representation is for number x (n)：Bearing fault signal x (n) is calculated through just
Result AVGH (f (n)) of beginning single structure element morphology AVG-Hat filtering；
The initial structural element yardstick number of the initial smallest dimension structural element of 3-3, acquisition and setting, export structure element λ
Result after individual Multi-scale model element dilation operation, its mathematic(al) representation is：
That is dilation operation λ time, the collection of the result that sets up after multiple mesostructure element dilation operations
Close：That is { g1,g2,…gλ}；
While calculating multiple dimensioned erosion operation result (the x Θ g of corresponding bearing fault vibration signal x (n)λ) (n), dilation operation
As a resultOpening operation resultAnd closed operation result (x gλ)(n)；
3-4, obtain bearing fault vibration signal x (n) and the opening of λ Multi-scale model element lower bearing fault vibration signal x (n)
Computing, the result of closed operation, the result of morphology AVG-Hat conversion, its mathematical table under the corresponding λ Multi-scale model element of output
Reaching formula is：
Set up λ Multi-scale model element morphology
The set of signal after AVG-Hat filter filtering：That is { f1(n),f2(n),…,fλ(n)}.
3. according to claim 1 based on self-adapting multi-dimension AVG-Hat convert Morphologic filters building method,
It is characterized in that：In the step 4 pass through particle group optimizing method, adaptive should determine that optimum Multiscale Morphological AVG-Hat filter
After the filtering of ripple device, the concrete grammar of the initial corresponding optimal weights coefficient of Multi-scale model element is as follows：
Set { the g of 4-1, acquisition bearing fault vibration signal x (n) and λ Multi-scale model element1,g2,…gλ, output λ
The set of signal after Multi-scale model element morphology AVG-Hat filter filtering：That is { f1(n),f2(n),…,fλ(n)}；
Set { the f of signal after 4-2, λ Multi-scale model element morphology AVG-Hat filter filtering of acquisition1(n),f2
(n),…,fλ(n) } and the originally determined weight coefficient ω=(ω of filtered signal described in each yardstick1,ω2,…,ωλ),
ωi∈ [0,1] (0≤i≤λ), output initial weight coefficient are filtered with corresponding λ Multi-scale model element morphology AVG-Hat
The sum of products of device filtered signal：I.e.It is calculated the arrangement entropy H of the sum of products signalPEAnd bag
Sparse angle value S composed by network, by the arrangement entropy HPERemove with the sparse angle value S-phase of envelope spectrum the filtered signal is obtained in population
Fitness function value P in optimization method, its mathematic(al) representation is：
4-3, after the I time interative computation of population signal fitness function value PILess than or equal to (I-1) secondary interative computation
Fitness function value P of signal afterwardsI-1When, i.e. PI≤PI-1, remember PIFor optimum fitness function value；The like, obtain particle
The whole process of group optimizing method iteration, exports the minimum fitness function value of all sum of products signals after G interative computation
PbestAs fitness function value, i.e. P optimum during whole particle group optimizingbest=min (P1,P2,…,PG)；Wherein, G
For maximum iteration time；
4-4, extraction minimum fitness function value PbestCorresponding Multi-scale model element weights coefficient ωbest=(ω1,
ω2,…,ωλ) as the corresponding optimal weights coefficient of Multi-scale model element.
4. according to claim 1 based on self-adapting multi-dimension AVG-Hat convert Morphologic filters building method,
It is characterized in that：The method for building the Multiscale Morphological AVG-Hat wave filter of optimum in the step 5 is as follows：
Calculate optimal weights coefficient ωbest=(ω1,ω2,…,ωλ) filter with λ Multi-scale model element morphology AVG-Hat
Device filtered signal fiN the sum of products of (), using the sum of products signal as the adaptive optimum that should determine that of particle group optimizing method
Multi-scale model element morphology AVG-Hat wave filter composition form, wherein ωi∈ωbest,fi(n)={ f1(n),f2
(n),…,fλ(n)}.
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