CN114757226A - Bearing fault characteristic enhancement method of parameter self-adaptive decomposition structure - Google Patents
Bearing fault characteristic enhancement method of parameter self-adaptive decomposition structure Download PDFInfo
- Publication number
- CN114757226A CN114757226A CN202210353847.2A CN202210353847A CN114757226A CN 114757226 A CN114757226 A CN 114757226A CN 202210353847 A CN202210353847 A CN 202210353847A CN 114757226 A CN114757226 A CN 114757226A
- Authority
- CN
- China
- Prior art keywords
- bearing
- parameter
- length
- bearing fault
- reb
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000000354 decomposition reaction Methods 0.000 title claims abstract description 19
- 238000001914 filtration Methods 0.000 claims abstract description 20
- 238000004364 calculation method Methods 0.000 claims abstract description 11
- 238000004458 analytical method Methods 0.000 claims abstract description 4
- 238000001228 spectrum Methods 0.000 claims description 12
- 230000003044 adaptive effect Effects 0.000 claims description 9
- 238000005457 optimization Methods 0.000 claims description 8
- 230000002708 enhancing effect Effects 0.000 claims description 3
- 230000003287 optical effect Effects 0.000 claims description 3
- 238000006073 displacement reaction Methods 0.000 claims description 2
- 238000012512 characterization method Methods 0.000 claims 3
- 230000009466 transformation Effects 0.000 claims 2
- 238000009434 installation Methods 0.000 abstract description 20
- 238000010183 spectrum analysis Methods 0.000 abstract description 9
- 238000012545 processing Methods 0.000 abstract description 6
- 238000003745 diagnosis Methods 0.000 abstract description 5
- 230000003595 spectral effect Effects 0.000 description 10
- 238000004422 calculation algorithm Methods 0.000 description 7
- 238000005096 rolling process Methods 0.000 description 4
- 238000000605 extraction Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 125000004122 cyclic group Chemical group 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000001629 suppression Effects 0.000 description 2
- 230000017105 transposition Effects 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 101100366000 Caenorhabditis elegans snr-1 gene Proteins 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000013016 damping Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000012821 model calculation Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/04—Bearings
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- General Engineering & Computer Science (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention discloses a bearing fault feature enhancement method of a parameter self-adaptive decomposition structure, belonging to the technical field of fault diagnosis and signal processing analysis; the method of the invention provides a bearing fault feature enhancement method of a parameter self-adaptive decomposition structure aiming at the problem that the identification interference of encoder installation errors on bearing fault features is carried out, and the method firstly carries out self-adaptive division on the filtering length of a Savitzky-Golay filter to obtain residual signals under different parameters; secondly, representing the richness of bearing fault information contained in each residual signal by combining a diagnosis index (IIDF), and obtaining an optimized filtering length parameter corresponding to the maximum IIDF value; the Savitzky-Golay filter based on the optimal parameters eliminates the installation error of the encoder, and the fault characteristics of the bearing are revealed through corresponding envelope spectrum analysis; the PDS structure provided by the method has the advantage of obtaining high-precision parameters with low calculation cost, and can effectively eliminate the interference of the encoder installation error component on the bearing fault characteristic identification by combining the Savitzky-Golay filter.
Description
Technical Field
The invention relates to a bearing fault feature enhancement method of a parameter self-adaptive decomposition structure, and belongs to the technical field of fault diagnosis and signal processing analysis.
Background
The bearing is used as a supporting part of the rotary machine, and the health degree of the bearing directly influences the running precision and the service life of the rotary machine. When a bearing fails, the contact rigidity of the rolling body and the raceway at the fault position changes, the corresponding instantaneous angular velocity (IAS) changes regularly, and then the IAS signal contains abundant bearing fault information. Therefore, the monitoring of the condition of the bearing and the fault diagnosis based on the IAS signal are one of the hot spots in the field of fault diagnosis. However, the suppression of the encoder installation error is not negligible for the rolling bearing fault feature extraction, and the suppression of the disturbance component thereof is particularly important for the bearing fault feature extraction.
On one hand, due to the assembly error of the inner diameter and the shaft of the encoder, the assembly error of the encoder cannot be avoided in engineering application; the energy amplitude of the installation error of the encoder is in positive correlation with the rotating speed, namely the modulation effect of the installation error of the encoder on the bearing signal is obviously improved along with the increase of the rotating speed; the overall error produced by different degrees of eccentricity error and tilt error is different, i.e. the degree of disturbance to the bearing fault component is different. On the other hand, the IAS variation caused by early bearings is relatively weak, and is often submerged in measurement noise and encoder installation errors, making fault identification thereof difficult.
In conclusion, elimination of encoder installation errors is crucial to effectively reveal bearing failure characteristics.
Disclosure of Invention
Because the installation error of the encoder cannot be avoided in engineering application, and the modulation effect of the installation error of the encoder on the component of the rolling bearing is increased along with the rising of the rotating speed, the bearing characteristics can not be effectively identified. In order to solve the problem, the invention provides a bearing fault feature enhancement method of a parametric self-adaptive decomposition structure (PDS). in the method, on the basis of a Savitzky-Golay filter, the optimized parameters of the Savitzky-Golay filter are obtained in a self-adaptive manner on the basis of the parametric self-adaptive decomposition structure and in combination with an IIDF index, so that the installation error of an encoder is effectively eliminated, and the bearing fault feature is enhanced.
As shown in FIG. 1, the method for enhancing the bearing fault characteristics based on the parameter adaptive decomposition structure comprises the following steps:
step 1: obtaining an instantaneous angular velocity signal containing bearing fault information;
acquisition of the containing shaft of an optical encoder by a PicoScope Signal acquisition SystemThe instantaneous angular displacement and the corresponding time of the fault information are carried, and the forward difference method is adopted to calculate and obtain the instantaneous angular velocity IASiSignal, its calculation formula is as follows
IAS in the formulaiDenotes the instantaneous angular velocity at the i-th time instant, i 1,2,3, …, Δ Φ 2 pi/N, Δ ti=ti+1-ti(ii) a N denotes the number of grating lattices of the encoder.
And 2, step: obtaining optimized filter length M by PDSop
2-1, assuming the value range of the parameter as [ A ]l→p,Bl→p]The decomposition level l is 1,2, 3.. the number of parameter ranges in the level is represented by p, and p is 1,2, 3.. the. Specifically, if l is 1, p is 1. Filter length Ml(dl)=Al→p,Al→p+Sl,...,floor((Bl→p-Al→p)/Sl) Number of discrete sequences d in the range of class ll=1,2,...,∑p floor((Bl→p-Al→p)/Sl) Floor (. cndot.) denotes a round-down operation, Ml(dl) Is increased by Sl,SlAnd Sl+1The ratio being epsilon, epsilon>1;
2-2, based on fixed filtering orders P and Ml(dl)=Al→p,Al→p+Sl,...,floor((Bl→p-Al→p)/Sl) Discrete sequence is dlUsing Savitzky-Golay filter to process original signal IASiFiltering is carried out according to the formula
In the formulaRepresenting the filtered signal, n ═ Ml(dl)-1)/2+1,(Ml(dl)-1)/2+2,(Ml(dl)-1)/2+3,...,length(IASi) Length (·) represents the fetch data length operation, the filter coefficientCan be expressed as
In the formula DT=[Q,G1,...,GP]T,GP=-((Ml(dl)-1)/2)P,...,(-1)P,0,1P,...,((Ml(dl)-1)/2)P,Q=-(Ml(dl)-1)/2,...,-1,1,1,...,(Ml(dl)-1)/2,[·]TRepresenting a transpose operation.
2-3, the difference between the original signal and the filtered signal as the residual signal can be expressed as
Further, the original signal is subjected to envelope spectrum analysis, which is calculated as
Wherein F {. DEG } and F { [-1{. denotes Fourier transform and inverse Fourier transform, respectively, and h (n) can be expressed as
2-4, characterizing the envelope analytic signal by adopting IIDF indexThe richness of bearing fault information is contained in the formula
Wherein gamma is1=freb-fT-2fv,γ2=fv-2fT,γ3=freb-2fT,γ4=(h1+1)freb-fT-2fv,γ5=h1freb-fT-(h2+1)fv,γ6=h1freb+fT-h2fv,γ7=h1freb-fT,fT=0.02freb。frebCharacteristic order of bearing failure, fvModulation frequency, H, related to the characteristic of bearing failure1And H2Respectively representing the bearing fault signature order and the modulation component signature order. Chi-type food processing machine1Can be expressed as
Wherein MAD is 1.4826 & kappa (| χ)2-κ(χ2) I), k (·) represents taking the median value, | · represents taking the absolute value, and the envelope analytic signal can be obtained by equation (8)Index containing richness of bearing fault information
2-5, if floor (S)l+1/2)<If 1 is satisfied, the optimized filter length parameter M is outputopCan be represented as
If floor (S)l+1/2)<If 1 is not satisfied, the filter length M is further reducedl+1(dl+1) Value range and refinement step length S ofl+1An adaptive threshold is proposed, which can be expressed as
In the formula, 0.5< mu <1 represents an amplitude coefficient, max {. cndot.) is a maximum value operation, and min {. cndot.) is a minimum value operation.
2-6, decomposition level l +1 parameter Range [ A ]l+1→p,Bl+1→p]Which can be represented as
Wherein gamma [. C]Indicating acquisition greater than threshold ThlTime corresponding parameter range [ Al+1→p,Bl+1→p]. Further, a parameter increment S is adoptedl+1Dividing the determined parameter range [ A ] l+1→p,Bl+1→p]In which S isl+1=Sl/ε,Ml+1(dl+1)=Al+1→p,Al+1→p+Sl+1,...,floor((Bl+1→p-Al+1)/Sl+1) (ii) a Returning to the step 2-2, performing iterative operation until the formula (10) is met, and outputting an optimized filtering length parameter Mop。
And step 3: optimization parameter M based on determinationopAnd a fixed filtering order P, using a Savitzky-Golay filter to the original signal IASiFiltering to obtain optimal residual signal R by formula (4)op。
And 4, step 4: by the formula (7) to RopEnvelope spectrum analysis is performed to reveal bearing fault characteristics.
The invention has the beneficial effects that:
(1) the invention provides a self-adaptive parameter partitioning structure (PDS), which solves the defect that a Savitzky-Golay filter determines an optimized parameter depending on experience;
(2) the invention provides a self-adaptive parameter division structure, which has the advantages of low calculation cost and high precision, and the advantages of the self-adaptive parameter division structure are more obvious under the working condition of industrial big data;
(3) the invention reasonably sets epsilon, mu and SlThe algorithm calculation efficiency can be improved, and the adaptability of the PDS can be enhanced;
(4) the Savitzky-Golay filter based on the self-adaptive parameters eliminates the installation error of the encoder and enhances the fault characteristics of the bearing.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of a parameter adaptive structure (PDS);
FIG. 3 is a time domain waveform of a simulation signal in example 1, in which (a) is an encoder installation error waveform, (b) is a bearing fault waveform with noise, (c) is a superimposed signal, and (d) is an envelope spectrum of the graph (c);
fig. 4 shows the results obtained by different theories in example 1, wherein (a) shows the waveform after CPW processing, (b) shows the envelope spectrum of (a), (c) shows the result of the fast spectral coherence algorithm, and (d) shows the optimal filter parameters determined by the method of the present invention, (e) shows the residual signal of the method, and (f) shows the envelope spectrum of (e);
FIG. 5 is a bench top in example 2;
fig. 6 is an IAS signal plot (a) and a corresponding envelope spectrum (b) acquired in example 2;
fig. 7 shows the results obtained by different theories in example 2, wherein (a) shows the waveform after CPW processing, (b) shows the envelope spectrum of (a), (c) shows the processing result of the fast spectral coherence algorithm, (d) shows the determination of the optimal filter parameters by the method of the present invention, (e) shows the residual signal of the method, and (f) shows the envelope spectrum of (e).
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art without any creative effort based on the embodiments of the present invention belong to the protection scope of the present invention, and the methods in the embodiments are all conventional methods if not specifically described.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described herein, and it will be apparent to those of ordinary skill in the art that the present invention may be practiced without departing from the spirit and scope of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Example 1: the example describes that the method is used for simulating the extraction of the fault characteristics of the outer ring of the instantaneous angular velocity bearing, and as shown in fig. 3, the specific process comprises the following steps:
the bearing outer ring fault signal model calculation formula adopted by simulation analysis is as follows:
wherein w (θ) represents an average angular velocity, wo(theta) represents an encoder mounting error, ξ represents a damping coefficient, fnDenotes a natural frequency, # θ -j Θ - τjThe angle sequence theta is 2 pi/N, 4 pi/N, 6 pi/N, N (theta) represents the measurement noise of the encoder, rho is the ratio of the eccentric distance of a geometric center and a rotation center, r is the diameter of an encoder hole, delta r is the eccentric distance of the geometric center and the rotation center, beta represents the inclination angle between the rotary shaft and the rotary shaft of the encoder, and the initial angle theta is an initial angle thetae∈[φe,2kπ+φe],θt∈[φt,2kπ+φt]And C represents a bearing fault impact amplitude. In the simulation signal, N is 5000, C is 0.0002, Θ is N/3.56, f reb=3.56×,<τj>=0,max{τj}=0.0071rad,fn50 x, x represents the order of features, ξ 0.03, w (θ) 5rad/s, ρ 0.01, β 0.03, SNR-1, fv=0,H1=3,H2=0,A1→1=5,B1→1=N,S1=10,P=3,ε=2,μ=0.5,φe=0,φt=0。
Step 1: the waveform of the installation error of the simulated encoder is shown in fig. 3(a), the waveform of the superposition of the fault and the measurement noise of the outer ring of the bearing is shown in fig. 3(b), the superposition of fig. 3(a) and 3(b) is shown in fig. 3(c), and the corresponding envelope spectrum is shown in fig. 3 (d). It can be found that the spectral line of the installation error of the encoder is dominant, and the bearing fault characteristic spectral line cannot be effectively identified. Therefore, suppressing encoder installation errors is critical to revealing bearing failure characteristics.
Step 2: obtaining the optimal parameter Mop;
2-1, based on M1(d1)=[A1→1,B1→1]Initial parameter increment S 110, filter order P3, filter coefficientAs shown by level 1 in fig. 2, the calculation formula is
In the formula DT=[Q,G1,...,GP]T,GP=-((Ml(dl)-1)/2)P,...,(-1)P,0,1P,...,((Ml(dl)-1)/2)P,Q=-(Ml(dl)-1)/2,...,-1,1,1,...,(Ml(dl)-1)/2,[·]TRepresenting a transposition operation, d1=1,2,...,length((A1→1-B1→1)/S1). Further, based on the filter coefficientIAS of original signal by adopting Savitzky-Golay filteriThe signal is filtered by the formula
2-2 evaluation by IIDF indexEstimated envelope resolved signalRichness of information about bearing faults contained therein, which can be expressed as
In the formula fv=0,H1=3,H2=0,freb=3.56×,fT=0.02·freb=0.0712,γ1=freb-fT-2fv=3.4888,γ3=freb-2fT=3.546,γ7=h1freb-fT=h1freb-0.0712,χ1Is calculated as
Wherein MAD is 1.4826 & kappa (| χ)2-κ(χ2) κ (-) denotes a median operation, | | denotes an absolute value operation;
2-3 based on S2=S1If/ε is 5, then floor (S)2/2)<If 1 is not satisfied, the filter length M is further reduced based on the PDS structure shown in fig. 21(d1) Value range of and refinement M1(d1) μ ═ 0.5 and IIDF1 d1The method for obtaining the adaptive threshold may be expressed as
2-4, obtaining parameter range [ A ] when decomposition level is 2s 2→p,Ae 2→p]Which can be represented as
2-5, adopting parameter increment S2=S1/epsilon divides the determined parameter range [ A ]2→p,B2→p]Wherein ε is 2, S2=S1/2=5,M2(d2)=A2→p,A2→p+S2,...,floor((B2→p-A2→p)/S2)。
2-6, based on the PDS structure shown in FIG. 2, if floor (S)l+1/2)<1 is satisfied, obtainMaximum time corresponding optimization parameter MopWhich is calculated as
As shown in FIG. 4(d), the obtained optimization parameter Mop=2471。
And step 3: optimization parameter M based on acquisitionop2471 and filtering order P3, the original signal is filtered using a Savitzky-Golay filter, and the residual signal is shown in fig. 4(e), which shows that the encoder installation error is effectively suppressed.
And 4, step 4: by performing envelope spectrum analysis on the residual signal obtained in step 3, as shown in fig. 4(f), it can be found that the impact component of the bearing fault is effectively enhanced.
And 5: to further verify the effectiveness of the method proposed herein, the original signal is analyzed using a conventional CPW algorithm, the processed signal and the corresponding spectrum are shown in fig. 4(a) and 4(b), and furthermore, the original signal is analyzed using a fast spectral coherence algorithm, the cyclic frequency α max30Hz, window width NwAs a result, as shown in fig. 4(c), it can be seen that the characteristic spectral lines related to the bearing fault cannot be identified effectively.
Example 2: the embodiment describes the method for extracting the fault characteristics of the outer ring of the actual rolling bearing
In this embodiment, a bearing test bench is adopted, and as shown in fig. 5, a ReSatron optical encoder is installed on the test bench, where N is 5000, and 10 is adopted6And the PicoScope high-speed acquisition device of the sampling rate acquires corresponding angle information and time information. The bearing type of the test stand is NU206E (N)b=13,Eb=9.525,Ep46, alpha is 0), in order to simulate the fault of the bearing outer ring, a groove with the width of about 0.5mm and the depth of about 0.5mm is machined on the outer ring by adopting a linear cutting mode; obtaining the characteristic frequency f of the bearing outer ring fault by the following calculation formulareb5.15X.
Step 1: obtained IASiThe bearing outer ring fault waveform is shown in fig. 6 (a);
step 2: directly to the original IASiThe signal is subjected to envelope order spectral analysis as shown in fig. 6 (b). It can be found that spectral lines of encoder installation errors are dominant, but bearing fault characteristic spectral lines cannot be effectively identified. Therefore, the disturbance of the encoder installation error needs to be suppressed to enhance the bearing failure characteristics.
And 3, step 3: determination of optimized filter parameters M by PDS structureop
3-1 based on M1(d1)=[A1→1,B1→1]=[5,5000]Filter order P ═ 3, M1(d1) Increment of S1Obtaining filter coefficients of 20The calculation formula is
In the formula DT=[Q,G1,...,GP]T,GP=-((Ml(dl)-1)/2)P,...,(-1)P,0,1P,...,((Ml(dl)-1)/2)P,Q=-(Ml(dl)-1)/2,...,-1,1,1,...,(Ml(dl)-1)/2,[·]TRepresenting a transposition operation, d1=1,2,...,length((A1→1-B1→1)/S1)。
3-2, based on filter coefficientsOriginal IAS using Savitzky-Golay filteriThe signal is filtered by the formula
3-3, evaluating the envelope analytic signal by IIDF indexRichness of information about bearing faults contained therein, which can be expressed as
In the formula fv=0,H1=3,H2=0,freb=5.15×,fT=0.02·freb=0.103,γ1=freb-fT-2fv=5.047,γ3=freb-2fT=4.944,γ7=h1freb-fT=h1freb-0.103,χ1The calculation formula (2) is as follows.
Wherein MAD is 1.4826 & kappa (| χ)2-κ(χ2) κ (-) denotes a median operation and | (-) denotes an absolute operation.
3-5 based on ∈ ═ 2, S2=S1If/ε is 10, then floor (S)2/2)<If 1 is not satisfied, the filter length M is further reduced based on the PDS structure shown in fig. 21Value range of and refinement M1μ ═ 0.5 and IIDF1 d1For obtaining adaptive thresholds
3-6, obtaining parameter range when decomposition level l is 2 [ A2→p,B2→p]As shown by level-2 in fig. 2, it can be expressed as
3-7, adopting parameter increment S2=S1/epsilon divides the determined parameter range [ A ] 2→p,B2→p]In FIG. 2, level is 2, where ε is 2, S2=S1/2=10,M2=A2→p,A2→p+S2,...,floor((B2→p-A2→p)/S2)。
2-6, PDS Structure based on FIG. 2, if floor (S)l+1/2)<1 satisfies, obtainMaximum time corresponding optimization parameter MopCalculated as
The determined optimization parameter M is shown in FIG. 7(d)op=147;
And 3, step 3: based on P ═ 3 and MopThe original signal is filtered using a Savitzky-Golay filter 147, the remaining signal of which is shown in fig. 7 (e). It can be found that the encoder installation error is effectively suppressed;
and 4, step 4: performing envelope spectrum analysis on the residual signal obtained in the step 3, as shown in fig. 7(f), it can be found that the impact component of the bearing fault is effectively enhanced;
and 5: to further verify the effectiveness of the method proposed herein, the original signal is analyzed using a conventional CPW algorithm, the processed signal and the corresponding spectrum are shown in fig. 7(a) and 7(b), and furthermore, the original signal is analyzed using a fast spectral coherence algorithm, the cyclic frequency αmax30Hz, window width NwAs a result, as shown in fig. 7(c), it can be seen that no characteristic spectral line related to the bearing fault can be identified effectively.
The principles and embodiments of the present invention have been parameterized by specific examples herein, which are presented solely to aid in the understanding of the invention and the core concepts; meanwhile, for a person skilled in the art, based on the idea of the present invention, changes may be made in the specific embodiments and the application scope, and in summary, the content of the present description should not be construed as a limitation of the present invention.
Claims (5)
1. A bearing fault characteristic enhancement method of a parameter self-adaptive decomposition structure is characterized by comprising the following steps:
(1) collecting instantaneous angular displacement and corresponding time of the optical encoder, and calculating instantaneous angular velocity IAS by adopting a forward difference methodi;
(2) Obtaining the filtering length M of the Savitzky-Golay filter based on a parameter self-adaptive decomposition structurel(dl) The sequence is then applied to the original signal IAS using a Savitzky-Golay filter in combination with a fixed filtering order PiFiltering to obtain filtered signalAnd a residual signalAnd carrying out envelope spectrum transformation on the residual signal; characterization of envelope analytic signals using IIDF indexRichness of bearing fault information is contained; selecting the optimized filtering length M corresponding to the maximum IIDF indexop;
(3) Based on optimizing filtering length MopAnd a fixed filtering order P, using a Savitzky-Golay filter to the original signal IASiAnd filtering to obtain an optimal residual signal, and analyzing the residual signal through an envelope spectrum to reveal the fault characteristics of the bearing.
2. The method for enhancing the bearing fault characteristics of the parametric adaptive decomposition structure according to claim 1, wherein the step (2) is specifically operated as follows:
(1) setting an initial filter length Ml(dl) The value range is [ A ] l→p,Bl→p]With fixed filtering order P, Ml(dl) Is increased by Sl,Ml(dl)=Al→p,Al→p+Sl,...,floor((Bl→p-Al→p)/Sl) (ii) a Number of discrete sequences d at level ll=1,2,...,∑pfloor((Bl→p-Al→p)/Sl) (ii) a 1,2, 3.; 1,2, 3.; floor (. cndot.) indicates rounding down;
(2) based on fixed filtering order P and filtering length Ml(dl) Sequence to obtain different filter lengths Ml(dl) Corresponding filter coefficientIAS of original signal by adopting Savitzky-Golay filteriFiltering to obtain filtered signalThe calculation formula is as follows:
wherein n ═ Ml(dl)-1)/2+1,(Ml(dl)-1)/2+2,(Ml(dl)-1)/2+3,length(IASi) Length (-) indicates the length of the data fetch, and the difference between the original signal and the filtered signal is the residual signal, indicated as
(3) And carrying out envelope spectrum transformation on the residual signal, wherein the calculation formula is as follows:
wherein F {. cndot. } andF-1{. represents a fourier transform and an inverse fourier transform, respectively, | - | represents an absolute value,h (n) represents envelope coefficients.
(4) Characterization of envelope analytic signals using IIDF indicatorsRichness of bearing fault information is included, and envelope analysis signals are obtainedIndex containing richness of bearing fault information
(5) If floor (S) is not satisfiedl+1/2)<1, the filter length M is reducedl+1(dl+1) Value range and fine step length S ofl+1,Sl+1=Sl/ε,Ml+1(dl+1)=Al+1→p,Al+1→p+Sl+1,...,floor((Bl+1→p-Al+1→p)/Sl+1) The value range of the parameter [ A ] is obtained when the decomposition level is l +1l+1→p,Bl+1→p]Obtained through self-adaptive threshold, and further replaces the parameter value range and parameter increment step length of the decomposition level l, namely [ A l→p,Bl→p]=[Al+1→p,Bl+1→p],Sl=Sl+1Number of discrete sequences d at level l +1l+1=1,2,...,∑pfloor((Al+1→p-Bl+1→p)/Sl) And returning to the step (2) to continue execution;
3. The method of enhancing bearing failure characteristics of a parametric adaptive decomposition structure according to claim 2, wherein: characterization of envelope analytic signals using IIDF indicatorsThe richness of bearing fault information is contained, and the calculation formula is as follows:
in the formula of gamma1=freb-fT-2fv,γ2=fv-2fT,γ3=freb-2fT,γ4=(h1+1)freb-fT-2fv,γ5=h1freb-fT-(h2+1)fv,γ6=h1freb+fT-h2fv,γ7=h1freb-fT,fT=0.02freb,χ1Through chi2Obtaining an adaptive threshold; f. ofrebIndicating the characteristic order of bearing failure, fvModulation frequency, H, associated with a characteristic indicative of bearing failure1And H2Respectively representing the bearing fault characteristic harmonic order and the modulation component characteristic harmonic order.
5. The method of claim 1, wherein the parameter value range [ A ] is defined as a decomposition level of l +1 l+1→p,Bl+1→p]Obtained by adaptive thresholding, calculated as
Wherein gamma [. C]Indicating acquisition greater than threshold ThlThe corresponding parameter range is [ A ]l+1→p,Bl+1→p]Wherein the threshold value ThlIs calculated as
Wherein 0.5< mu <1 represents an amplitude coefficient, max {. cndot.is a maximum value, and min {. cndot.is a minimum value.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210353847.2A CN114757226A (en) | 2022-04-03 | 2022-04-03 | Bearing fault characteristic enhancement method of parameter self-adaptive decomposition structure |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210353847.2A CN114757226A (en) | 2022-04-03 | 2022-04-03 | Bearing fault characteristic enhancement method of parameter self-adaptive decomposition structure |
Publications (1)
Publication Number | Publication Date |
---|---|
CN114757226A true CN114757226A (en) | 2022-07-15 |
Family
ID=82328691
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202210353847.2A Pending CN114757226A (en) | 2022-04-03 | 2022-04-03 | Bearing fault characteristic enhancement method of parameter self-adaptive decomposition structure |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN114757226A (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116304581A (en) * | 2023-05-10 | 2023-06-23 | 佛山市钒音科技有限公司 | Intelligent electric control system for air conditioner |
Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20120106219A1 (en) * | 2010-10-28 | 2012-05-03 | Renesas Electronics Corporation | Semiconductor device for wireless communication |
CN102759397A (en) * | 2012-07-10 | 2012-10-31 | 东南大学 | Efficient extraction method of friction fault characteristics in vibration signal of rotating shaft |
CN103245518A (en) * | 2013-03-29 | 2013-08-14 | 燕山大学 | Rotary mechanical fault diagnosis method based on differential local mean decomposition |
CN111259765A (en) * | 2020-01-13 | 2020-06-09 | 北京工业大学 | Order analysis method based on numerical control machine tool spindle current signal |
CN111855192A (en) * | 2020-07-31 | 2020-10-30 | 北京航空航天大学 | Singular value decomposition method for denoising encoder signal |
CN112507769A (en) * | 2020-08-10 | 2021-03-16 | 北京化工大学 | Bearing fault diagnosis method based on simulated sensor resonance enhancement features |
WO2021139331A1 (en) * | 2020-01-08 | 2021-07-15 | 重庆交通大学 | Bearing fault diagnosis method based on instantaneous frequency optimization vmd |
CN113375938A (en) * | 2021-04-29 | 2021-09-10 | 国营芜湖机械厂 | Fault diagnosis method for hydraulic axial plunger pump bearing |
CN114216676A (en) * | 2021-11-30 | 2022-03-22 | 上海海事大学 | Compound fault diagnosis method for planetary gearbox without tachometer under time-varying working condition |
-
2022
- 2022-04-03 CN CN202210353847.2A patent/CN114757226A/en active Pending
Patent Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20120106219A1 (en) * | 2010-10-28 | 2012-05-03 | Renesas Electronics Corporation | Semiconductor device for wireless communication |
CN102759397A (en) * | 2012-07-10 | 2012-10-31 | 东南大学 | Efficient extraction method of friction fault characteristics in vibration signal of rotating shaft |
CN103245518A (en) * | 2013-03-29 | 2013-08-14 | 燕山大学 | Rotary mechanical fault diagnosis method based on differential local mean decomposition |
WO2021139331A1 (en) * | 2020-01-08 | 2021-07-15 | 重庆交通大学 | Bearing fault diagnosis method based on instantaneous frequency optimization vmd |
CN111259765A (en) * | 2020-01-13 | 2020-06-09 | 北京工业大学 | Order analysis method based on numerical control machine tool spindle current signal |
CN111855192A (en) * | 2020-07-31 | 2020-10-30 | 北京航空航天大学 | Singular value decomposition method for denoising encoder signal |
CN112507769A (en) * | 2020-08-10 | 2021-03-16 | 北京化工大学 | Bearing fault diagnosis method based on simulated sensor resonance enhancement features |
CN113375938A (en) * | 2021-04-29 | 2021-09-10 | 国营芜湖机械厂 | Fault diagnosis method for hydraulic axial plunger pump bearing |
CN114216676A (en) * | 2021-11-30 | 2022-03-22 | 上海海事大学 | Compound fault diagnosis method for planetary gearbox without tachometer under time-varying working condition |
Non-Patent Citations (1)
Title |
---|
张俊等: "基于PSO -VMD-MCKD方法的风机轴承微弱故障诊断", 振动、测试与诊断, vol. 40, no. 02, 30 April 2020 (2020-04-30), pages 289 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116304581A (en) * | 2023-05-10 | 2023-06-23 | 佛山市钒音科技有限公司 | Intelligent electric control system for air conditioner |
CN116304581B (en) * | 2023-05-10 | 2023-07-21 | 佛山市钒音科技有限公司 | Intelligent electric control system for air conditioner |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN111089726B (en) | Rolling bearing fault diagnosis method based on optimal dimension singular spectrum decomposition | |
Cheng et al. | Adaptive multipoint optimal minimum entropy deconvolution adjusted and application to fault diagnosis of rolling element bearings | |
Yan et al. | Improved Hilbert–Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis | |
Bozchalooi et al. | A joint resonance frequency estimation and in-band noise reduction method for enhancing the detectability of bearing fault signals | |
Jiang et al. | Sparse dictionary design based on edited cepstrum and its application in rolling bearing fault diagnosis | |
CN108388908B (en) | Rolling bearing impact fault diagnosis method based on K-SVD and sliding window noise reduction | |
Cong et al. | Research of singular value decomposition based on slip matrix for rolling bearing fault diagnosis | |
CN112101174A (en) | LOF-Kurtogram-based mechanical fault diagnosis method | |
CN112067297B (en) | Bearing fault feature extraction method | |
CN109029999B (en) | Rolling bearing fault diagnosis method based on enhanced modulation bispectrum analysis | |
CN112101245A (en) | Short-time Fourier transform mechanical impact feature extraction method based on frequency domain window function | |
Zhu et al. | An accurate integral method for vibration signal based on feature information extraction | |
Zhao et al. | Rolling bearing fault diagnosis based on CEEMD and time series modeling | |
CN113607415A (en) | Bearing fault diagnosis method based on short-time stochastic resonance under variable rotating speed | |
Soave et al. | Blind deconvolution criterion based on Fourier–Bessel series expansion for rolling element bearing diagnostics | |
CN114757226A (en) | Bearing fault characteristic enhancement method of parameter self-adaptive decomposition structure | |
Wang et al. | Application of RSSD‐OCYCBD Strategy in Enhanced Fault Detection of Rolling Bearing | |
CN113565584A (en) | Time-frequency filtering method for leaf-end timing signals | |
Yan et al. | Tacholess skidding evaluation and fault feature enhancement base on a two-step speed estimation method for rolling bearings | |
Zhao et al. | Feature extraction of faulty rolling element bearing under variable rotational speed and gear interferences conditions | |
CN117349615B (en) | Self-adaptive enhancement envelope spectrum method for fault diagnosis of rolling bearing under strong noise condition | |
CN110147637B (en) | Rub-impact fault diagnosis method based on wavelet and harmonic component greedy sparse identification | |
CN112113767B (en) | Bearing fault diagnosis method based on proportional band selection criterion | |
CN116304648B (en) | Gear fault identification method based on optimized pulse enhancement and envelope synchronous averaging | |
CN116933059A (en) | Fault identification method fusing variance and 1D-LBP |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination |