CN114757226A

CN114757226A - Bearing fault characteristic enhancement method of parameter self-adaptive decomposition structure

Info

Publication number: CN114757226A
Application number: CN202210353847.2A
Authority: CN
Inventors: 陈鑫; 郭瑜
Original assignee: Kunming University of Science and Technology
Current assignee: Kunming University of Science and Technology
Priority date: 2022-04-03
Filing date: 2022-04-03
Publication date: 2022-07-15

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

Bearing fault characteristic enhancement method of parameter self-adaptive decomposition structure

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 IAS_iSignal, its calculation formula is as follows

IAS in the formula_iDenotes the instantaneous angular velocity at the i-th time instant,

i

1,2,3, …, Δ Φ 2 pi/N, Δ t_i＝t_i+1-t_i(ii) a N denotes the number of grating lattices of the encoder.

And 2, step: obtaining optimized filter length M by PDS_op

2-1, assuming the value range of the parameter as [ A ]_l→p,B_l→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 M_l(d_l)＝A_l→p,A_l→p+S_l,...,floor((B_l→p-A_l→p)/S_l) Number of discrete sequences d in the range of class l_l＝1,2,...,∑_p floor((B_l→p-A_l→p)/S_l) Floor (. cndot.) denotes a round-down operation, M_l(d_l) Is increased by S_l，S_lAnd S_l+1The ratio being epsilon, epsilon>1；

2-2, based on fixed filtering orders P and M_l(d_l)＝A_l→p,A_l→p+S_l,...,floor((B_l→p-A_l→p)/S_l) Discrete sequence is d_lUsing Savitzky-Golay filter to process original signal IAS_iFiltering is carried out according to the formula

In the formula

Representing the filtered signal, n ═ M_l(d_l)-1)/2+1,(M_l(d_l)-1)/2+2,(M_l(d_l)-1)/2+3,...,length(IAS_i) Length (·) represents the fetch data length operation, the filter coefficient

Can be expressed as

In the formula D^T＝[Q,G¹,...,G^P]^T，G^P＝-((M_l(d_l)-1)/2)^P,...,(-1)^P,0,1^P,...,((M_l(d_l)-1)/2)^P，Q＝-(M_l(d_l)-1)/2,...,-1,1,1,...,(M_l(d_l)-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

Data length in equation

Further, the envelope resolved signalCan be expressed as

2-4, characterizing the envelope analytic signal by adopting IIDF index

The richness of bearing fault information is contained in the formula

Wherein gamma is₁＝f_reb-f_T-2f_v，γ₂＝f_v-2f_T，γ₃＝f_reb-2f_T，γ₄＝(h₁+1)f_reb-f_T-2f_v，γ₅＝h₁f_reb-f_T-(h₂+1)f_v，γ₆＝h₁f_reb+f_T-h₂f_v，γ₇＝h₁f_reb-f_T，

f_T＝0.02f_reb。f_rebCharacteristic order of bearing failure, f_vModulation 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 output_opCan be represented as

Wherein argmax {. denotes Return

Maximum time corresponding optimization parameter M_op；

If floor (S)_l+1/2)<If 1 is not satisfied, the filter length M is further reduced_l+1(d_l+1) Value range and refinement step length S of_l+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,B_l+1→p]Which can be represented as

Wherein gamma [. C]Indicating acquisition greater than threshold Th_lTime corresponding parameter range [ A_l+1→p,B_l+1→p]. Further, a parameter increment S is adopted_l+1Dividing the determined parameter range [ A ] _l+1→p,B_l+1→p]In which S is_l+1＝S_l/ε，M_l+1(d_l+1)＝A_l+1→p,A_l+1→p+S_l+1,...,floor((B_l+1→p-A_l+1)/S_l+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 M_op。

And step 3: optimization parameter M based on determination_opAnd a fixed filtering order P, using a Savitzky-Golay filter to the original signal IAS_iFiltering to obtain optimal residual signal R by formula (4)_op。

And 4, step 4: by the formula (7) to R_opEnvelope 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 S_lThe 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, w_o(theta) represents an encoder mounting error, ξ represents a damping coefficient, f_nDenotes a natural frequency, # θ -j Θ - τ_jThe 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 theta_e∈[φ_e,2kπ+φ_e]，θ_t∈[φ_t,2kπ+φ_t]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×，<τ_j>＝0，max{τ_j}＝0.0071rad，f_n50 x, x represents the order of features, ξ 0.03, w (θ) 5rad/s, ρ 0.01, β 0.03, SNR-1, f_v＝0，H₁＝3，H₂＝0，A_1→1＝5，B_1→1＝N，S₁＝10，P＝3，ε＝2，μ＝0.5，φ_e＝0，φ_t＝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 M_op；

2-1, based on M₁(d₁)＝[A_1→1,B_1→1]Initial parameter increment S ₁10, filter order P3, filter coefficient

As shown by level 1 in fig. 2, the calculation formula is

In the formula D^T＝[Q,G¹,...,G^P]^T，G^P＝-((M_l(d_l)-1)/2)^P,...,(-1)^P,0,1^P,...,((M_l(d_l)-1)/2)^P，Q＝-(M_l(d_l)-1)/2,...,-1,1,1,...,(M_l(d_l)-1)/2，[·]^TRepresenting a transposition operation, d₁＝1,2,...,length((A_1→1-B_1→1)/S₁). Further, based on the filter coefficient

IAS of original signal by adopting Savitzky-Golay filter_iThe signal is filtered by the formula

Further, the corresponding residual signal

Can be expressed as

Further, for the residual signal

Performing envelope spectrum analysis by calculating

2-2 evaluation by IIDF indexEstimated envelope resolved signal

Richness of information about bearing faults contained therein, which can be expressed as

In the formula f_v＝0，H₁＝3，H₂＝0，f_reb＝3.56×，f_T＝0.02·f_reb＝0.0712，γ₁＝f_reb-f_T-2f_v＝3.4888，γ₃＝f_reb-2f_T＝3.546，γ₇＝h₁f_reb-f_T＝h₁f_reb-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 2^s _2→p,A^e _2→p]Which can be represented as

2-5, adopting parameter increment S₂＝S₁/epsilon divides the determined parameter range [ A ]_2→p,B_2→p]Wherein ε is 2, S₂＝S₁/2＝5，M₂(d₂)＝A_2→p,A_2→p+S₂,...,floor((B_2→p-A_2→p)/S₂)。

2-6, based on the PDS structure shown in FIG. 2, if floor (S)_l+1/2)<1 is satisfied, obtain

Maximum time corresponding optimization parameter M_opWhich is calculated as

As shown in FIG. 4(d), the obtained optimization parameter M_op＝2471。

And step 3: optimization parameter M based on acquisition_op2471 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 N_wAs 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,E_b＝9.525,E_p46, 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 formula_reb5.15X.

Step 1: obtained IAS_iThe bearing outer ring fault waveform is shown in fig. 6 (a);

step 2: directly to the original IAS_iThe 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 structure_op

3-1 based on M₁(d₁)＝[A_1→1,B_1→1]＝[5,5000]Filter order P ═ 3, M₁(d₁) Increment of S₁Obtaining filter coefficients of 20

The calculation formula is

In the formula D^T＝[Q,G¹,...,G^P]^T，G^P＝-((M_l(d_l)-1)/2)^P,...,(-1)^P,0,1^P,...,((M_l(d_l)-1)/2)^P，Q＝-(M_l(d_l)-1)/2,...,-1,1,1,...,(M_l(d_l)-1)/2，[·]^TRepresenting a transposition operation, d₁＝1,2,...,length((A_1→1-B_1→1)/S₁)。

3-2, based on filter coefficients

Original IAS using Savitzky-Golay filter_iThe signal is filtered by the formula

Further, the corresponding residual signal

Can be expressed as

Further, for the residual signal

Performing envelope spectrum analysis by calculating

Further, for the residual signal

And carrying out envelope spectrum analysis.

3-3, evaluating the envelope analytic signal by IIDF index

In the formula f_v＝0，H₁＝3，H₂＝0，f_reb＝5.15×，f_T＝0.02·f_reb＝0.103，γ₁＝f_reb-f_T-2f_v＝5.047，γ₃＝f_reb-2f_T＝4.944，γ₇＝h₁f_reb-f_T＝h₁f_reb-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 [ A_2→p,B_2→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,B_2→p]In FIG. 2, level is 2, where ε is 2, S₂＝S₁/2＝10，M₂＝A_2→p,A_2→p+S₂,...,floor((B_2→p-A_2→p)/S₂)。

2-6, PDS Structure based on FIG. 2, if floor (S)_l+1/2)<1 satisfies, obtain

Maximum time corresponding optimization parameter M_opCalculated as

The determined optimization parameter M is shown in FIG. 7(d)_op＝147；

And 3, step 3: based on P ═ 3 and M_opThe 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 α_max30Hz, window width N_wAs 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

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 method_i；

(2) Obtaining the filtering length M of the Savitzky-Golay filter based on a parameter self-adaptive decomposition structure_l(d_l) The sequence is then applied to the original signal IAS using a Savitzky-Golay filter in combination with a fixed filtering order P_iFiltering to obtain filtered signal

And a residual signal

And carrying out envelope spectrum transformation on the residual signal; characterization of envelope analytic signals using IIDF index

Richness of bearing fault information is contained; selecting the optimized filtering length M corresponding to the maximum IIDF index_op；

(3) Based on optimizing filtering length M_opAnd a fixed filtering order P, using a Savitzky-Golay filter to the original signal IAS_iAnd 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 M_l(d_l) The value range is [ A ] _l→p,B_l→p]With fixed filtering order P, M_l(d_l) Is increased by S_l，M_l(d_l)＝A_l→p,A_l→p+S_l,...,floor((B_l→p-A_l→p)/S_l) (ii) a Number of discrete sequences d at level l_l＝1,2,...,∑_pfloor((B_l→p-A_l→p)/S_l) (ii) a 1,2, 3.; 1,2, 3.; floor (. cndot.) indicates rounding down;

(2) based on fixed filtering order P and filtering length M_l(d_l) Sequence to obtain different filter lengths M_l(d_l) Corresponding filter coefficient

IAS of original signal by adopting Savitzky-Golay filter_iFiltering to obtain filtered signal

The calculation formula is as follows:

wherein n ═ M_l(d_l)-1)/2+1,(M_l(d_l)-1)/2+2,(M_l(d_l)-1)/2+3,length(IAS_i) 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 indicators

Richness of bearing fault information is included, and envelope analysis signals are obtained

Index containing richness of bearing fault information

(5) If floor (S) is not satisfied_l+1/2)<1, the filter length M is reduced_l+1(d_l+1) Value range and fine step length S of_l+1，S_l+1＝S_l/ε，M_l+1(d_l+1)＝A_l+1→p,A_l+1→p+S_l+1,...,floor((B_l+1→p-A_l+1→p)/S_l+1) The value range of the parameter [ A ] is obtained when the decomposition level is l +1_l+1→p,B_l+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,B_l→p]＝[A_l+1→p,B_l+1→p]，S_l＝S_l+1Number of discrete sequences d at level l +1_l+1＝1,2,...,∑_pfloor((A_l+1→p-B_l+1→p)/S_l) And returning to the step (2) to continue execution;

if floor (S) is satisfied_l+1/2)<1, then outputting the optimized filtering length parameter M_opWhich is calculated as

In the formulaargmax {. denotes return

Maximum time corresponding optimization parameter M_op。

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 indicators

The richness of bearing fault information is contained, and the calculation formula is as follows:

in the formula of gamma₁＝f_reb-f_T-2f_v，γ₂＝f_v-2f_T，γ₃＝f_reb-2f_T，γ₄＝(h₁+1)f_reb-f_T-2f_v，γ₅＝h₁f_reb-f_T-(h₂+1)f_v，γ₆＝h₁f_reb+f_T-h₂f_v，γ₇＝h₁f_reb-f_T，

f_T＝0.02f_reb，χ₁Through chi₂Obtaining an adaptive threshold; f. of_rebIndicating the characteristic order of bearing failure, f_vModulation 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.

4. The method of claim 3, wherein the method comprises the steps of:χ₁through chi₂Is calculated as

Wherein MAD is 1.4826 & kappa (| χ)₂-κ(χ₂) κ (-) denotes the median value, | - | denotes the absolute value.

5. The method of claim 1, wherein the parameter value range [ A ] is defined as a decomposition level of l +1 _l+1→p,B_l+1→p]Obtained by adaptive thresholding, calculated as

Wherein gamma [. C]Indicating acquisition greater than threshold Th_lThe corresponding parameter range is [ A ]_l+1→p,B_l+1→p]Wherein the threshold value Th_lIs calculated as

Wherein 0.5< mu <1 represents an amplitude coefficient, max {. cndot.is a maximum value, and min {. cndot.is a minimum value.