CN106127136A - Fault Diagnosis of Roller Bearings - Google Patents

Abstract

The present invention proposes a kind of Fault Diagnosis of Roller Bearings based on the fractal box algorithm improved with self adaptation grey correlation theory algorithm, the fractal box first passing through improvement extracts fault signature (comprising the more useful information being more easy to distinguish that can reflect bearing different operating state) from bearing vibration signal, then, the fault type of bearing and the different orders of severity are automatically identified by self adaptation Grey Relation Algorithm.Present invention seek to address that employing conventional Time-domain and frequency domain method are difficult to rolling bearing work health situation is made the problem assessed accurately, it is possible to identify different rolling bearing fault types and fault severity level accurately and effectively.

Description

Fault Diagnosis of Roller Bearings

Technical field

The present invention relates to a kind of rolling based on the fractal box algorithm improved with self adaptation grey correlation theory algorithm Method for Bearing Fault Diagnosis.

Background technology

Rolling bearing, as vitals, is widely used in almost all kinds of rotating machinery.Rolling bearing event Barrier is one of main reason of rotating machinery inefficacy and damage, and brings huge economic loss.For guaranteeing that unit operation can Leaning on and reduce economic loss, it is extremely necessary for researching and developing a kind of reliable and effective Fault Diagnosis of Roller Bearings.At numerous axles Holding in method for diagnosing faults, diagnostic method based on vibration signal has received extensive concern between the past few decades.

The vibration signal of bearing contains mechanical health condition information galore, and this is also from shaking by signal processing technology The dominant characteristics extracting sign mechanical health situation in dynamic signal is possibly realized.Currently, many signal processing technologies have been applied In bearing fault monitoring and diagnosis.But, owing to there is many non-linear factors (e.g., rigidity, friction, gap etc.), bearing is examined Break signal (particularly during malfunction) will appear as non-linear and astable feature.It addition, the vibration signal of actual measurement is not only Comprise the health information relevant to bearing itself, also comprise other rotary parts and the letter of structure in substantial amounts of unit equipment Breath (these belong to background noise compared to the former).Owing to background noise is the biggest, slight bearing fault information is easily flooded Not in background noise, and it is difficult to be extracted.Therefore, conventional time domain and frequency domain method (mainly for linear oscillator signal), The most advanced signal processing technology (e.g., wavelet transformation (WT) etc.), it is not easy to bearing work health situation is made standard True assessment.

Along with the development of nonlinear kinetics, many Non-linear analysis technology are applied to identify and predict that bearing is multiple Miscellaneous non-linear dynamic characteristic.Wherein, a kind of the most typical method is that the signal processing technology by some advanced persons is (e.g., little Ripple bag decomposes (WPT), Hilbert transform (HT), empirical mode decomposition (EMD), higher-order spectrum (HOS) etc.) R. concomitans come from In vibration signal extract fault characteristic frequency, and compare with theory characteristic frequency values further assess bearing health (need The micro-judgment of expert to be combined).Along with the development of artificial intelligence, bearing failure diagnosis process is introduced into pattern more and more Identify category, and its diagnosis validity and reliability mainly take but in characterize fault signature dominant characteristics vector choosing Take.Recently, and some methods based on entropy (e.g., approximate entropy (ApEn), Sample Entropy (SampEn), fuzzy entropy (FuzzyEn), classification Entropy (HE), Hierarchical Fuzzy entropy etc.), it is proposed for from bearing vibration signal extracting the dominant characteristics characterizing fault signature Vector, and obtain certain effect.

Generally, after fault signature extracts, need a kind of mode identification technology to realize the automated diagnostic of bearing fault. Now, various mode identification methods have been applied in mechanical fault diagnosis, and wherein, be most widely used surely belongs to artificial neuron Network (ANNs) and support vector machine (SVMs).Wherein, the substantial amounts of sample of training need of artificial neural network (ANNs), this is Actual application is difficult to even can not accomplish, especially comprises the sample of fault signature.Support vector machine (SVMs) base In Statistical Learning Theory the situation of training (be particularly suitable for small sample), more excellent more extensive than artificial neural network (ANNs) has Ability, and can ensure that the optimal solution of local is consistent with the optimal solution of the overall situation.But, support vector machine (SVMs) grader accurate Property depends on the selection of its optimized parameter.For guaranteeing diagnostic accuracy, generally require and incorporate some optimized algorithms and/or be designed to Complicated many class formations make up the effectiveness improving support vector machine (SVMs).

Summary of the invention

The technical problem to be solved in the present invention is: use conventional Time-domain and frequency domain method to be difficult to rolling bearing work health Situation is made and being assessed accurately.

In order to solve above-mentioned technical problem, the technical scheme is that and provide a kind of fractal box based on improvement Algorithm and the Fault Diagnosis of Roller Bearings of self adaptation grey correlation theory algorithm, it is characterised in that comprise the following steps:

Step 1, to object rolling bearing the shaking under normal operating conditions and under different faults pattern in rotating machinery Dynamic signal is sampled, and obtains bearing vibration signal data sample, wherein, the corresponding different fault type of different fault modes And the order of severity, and in bearing vibration signal data sample, different vibration signals and different faults pattern one_to_one corresponding；

Step 2, the fractal box algorithm passing through to improve extract each vibration letter from bearing vibration signal data sample Number the dominant characteristics vector characterizing fault signature, and according to the corresponding relation of different vibration signals with different faults pattern, Corresponding relation between each dominant characteristics vector and corresponding failure pattern, wherein, the fractal box algorithm of improvement includes following Step:

Step 2.1, vibration signal x being carried out resampling, sampling number is 2^K；

Step 2.2, vibration signal x is carried out K phase space reconfiguration, after each phase space reconfiguration, calculate a box dimension of fractals Number, is made up of the dominant characteristics vector of vibration signal x all fractal box obtained；

Step 3, according to dominant characteristics vector and fault mode between corresponding relation set up sample knowledge storehouse；

The real-time vibration signal of the rolling bearing to be diagnosed under step 4, in real time acquisition current operating conditions, and by improving Fractal box algorithm extract from real-time vibration signal real-time dominant characteristics vector, based on step 3 set up sample knowledge Storehouse, utilizes Grey Relation Algorithm to calculate real-time dominant characteristics vector and the degree of association of each dominant characteristics vector in sample knowledge storehouse, The fault mode belonging to rolling bearing to be diagnosed is obtained by the degree of association.

Preferably, in described step 2.2, the box dimension of fractals obtained after vibration signal x is carried out kth time phase space reconfiguration Number is D_k, now, phase space dimension is k+1 dimension, D value of fractal box_kCalculation procedure include:

Step 2.2.1, calculate vibration signal x at the vertical coordinate range scale p (k ε) of current phase space,In formula:

ε is the minimum length of side of the box covering vibration signal x；

N₀For the sum of sampled point, N₀=2^K；

p₁=max{x_k(i-1)+1, x_k(i-1)+2..., x_k(i-1)+k+1, x_k(i-1)+k+1Kth (i-1)+k+1 for vibration signal x The value of individual sampled point；

p₂=min{x_k(i-1)+1, x_k(i-1)+2... x_k(i-1)+k+1}；

The box that step 2.2.2, calculating use the length of side to be k ε covers the minimum box number N of vibration signal x_kε, N_kε=p (k ε)/kε+1；

Step 2.2.3, selection matched curve lgk ε～lgN_kεThe middle linearity preferable one section as non-scaling section, wherein, lgN_kε=d_BLgk ε+b, d_BFor the slope of non-scaling section matched curve, b is the intercept of non-scaling section matched curve；

Step 2.2.4, utilize method of least square to calculate the slope of non-scaling section matched curve, be fractal box D_k。

Preferably, in described step 2.2.4, D value of fractal box_kComputing formula be:

In formula, k₁And k₂It is respectively uncalibrated visual servo The beginning and end of district's matched curve, k₁≤k≤k₂。

Preferably, in described step 4, conventional Grey Relation Algorithm is utilized to calculate real-time dominant characteristics vector and sample The degree of association of each dominant characteristics vector in knowledge base.

Preferably, in described step 4, self adaptation Grey Relation Algorithm is utilized to calculate real-time dominant characteristics vector and sample The degree of association of each dominant characteristics vector in knowledge base, comprises the following steps:

The real-time dominant characteristics vector B that step 4.1, extraction obtain is set toIn formula, D_kFor kth characteristic parameter, k =1,2 ..., K, K are characterized the total number of parameter；

Described sample knowledge storehouse stores and has following data:

$C_1=\left[\begin{array}{c}{c}_1\left(1\right)\\ c_1\left(2\right)\\ \mathrm{...}\\ c_1\left(k\right)\\ \mathrm{...}\\ c_1\left(K\right)\end{array}\right],C_2=\left[\begin{array}{c}{c}_2\left(1\right)\\ c_2\left(2\right)\\ \mathrm{...}\\ c_2\left(k\right)\\ \mathrm{...}\\ c_2\left(K\right)\end{array}\right],...,C_j=\left[\begin{array}{c}{c}_j\left(1\right)\\ c_j\left(2\right)\\ \mathrm{...}\\ c_j\left(k\right)\\ \mathrm{...}\\ c_j\left(K\right)\end{array}\right],\mathrm{...},C_M=\left[\begin{array}{c}{c}_M\left(1\right)\\ c_M\left(2\right)\\ \mathrm{...}\\ c_M\left(k\right)\\ \mathrm{...}\\ c_M\left(K\right)\end{array}\right];$

Wherein, C_jFor jth fault mode, j=1,2 ..., M, M are the total number of fault mode,For with C_jRight The characteristic vector answered, c_jK kth characteristic parameter that () is characterized in vector；

Step 4.2, calculate in real-time dominant characteristics vector B each fault mode pair in each characteristic parameter and sample knowledge storehouse Entropy between the characteristic parameter of relevant position in the characteristic vector answered, wherein, kth feature ginseng in real-time dominant characteristics vector B Number and jth fault mode C_jIn characteristic of correspondence vector, the entropy between kth characteristic parameter is E_jK (), then have:

In formula,And | Δ d_j(k) |=| D_k- c_j(k)|；

Step 4.3, calculate in real-time dominant characteristics vector B each fault mode pair in each characteristic parameter and sample knowledge storehouse The relative entropy of characteristic of correspondence parameter in the characteristic vector answered, wherein, kth characteristic parameter in real-time dominant characteristics vector With jth fault mode C in sample knowledge storehouse_jIn characteristic of correspondence vector, the relative entropy of kth characteristic parameter is e_j(k), e_j (k)=E_j(k)/lnM；

Step 4.4, to be calculated in real-time dominant characteristics vector B each characteristic parameter different relative in sample knowledge storehouse The weight coefficient of fault mode, wherein, in real-time dominant characteristics vector B, kth characteristic parameter is relative to jth in sample knowledge storehouse Individual fault mode C_jWeight coefficient be a_j(k),In formula, H_j(k)=1-e_j(k)；

Step 4.5, be calculated real-time dominant characteristics vector B and in sample knowledge storehouse each dominant characteristics vector associate Degree, classifies the real-time vibration signal of real-time rolling bearing to be diagnosed corresponding for dominant characteristics vector B to belonging to most relevance degree Fault mode, wherein, jth fault mode C in real-time dominant characteristics vector B and sample knowledge storehouse_jThe degree of association be ξ (B, C_j),In formula, ξ (D_k, c_j(k)) for being kth in real-time dominant characteristics vector B Characteristic parameter and jth fault mode C_jThe coefficient of association of kth characteristic parameter in characteristic of correspondence vector.

Preferably, in described step 4.5, described ξ (D_k, c_j(k)) computing formula be:

In formula, ρ is resolution ratio.

The present invention proposes a kind of method based on the fractal theory fractal box algorithm of improvement (that is, a kind of) and comes from axle Hold and vibration signal extracts the dominant characteristics vector characterizing fault signature.Fractal theory is that the present age one of nonlinear science is the heaviest One of branch wanted, it is particularly suitable for processing the non-linear of various complexity and Unsteady State, is therefore also applied for bearing The fault signature of vibration signal extracts.

Meanwhile, the contradictory problems of versatility Yu accuracy in order to solve algorithm for pattern recognition, the invention allows for one Plant self adaptation Grey Relation Algorithm and realize Fault Pattern Recognition (under Small Sample Size) accurately.

Present invention have the advantage that

1, the present invention can identify different rolling bearing fault types and fault severity level accurately and effectively；

2, the fractal box algorithm improved in the present invention compares traditional one dimensional fractal box counting dimension algorithm, it is possible to from rolling The vibration signal of bearing extracts the characteristic vector characterizing fault signature of more discrimination；

3, in the present invention, self adaptation Grey Relation Algorithm can reach 100% to the Fault Identification success rate of rolling bearing, And the overall recognition success rate of different faults type and fault severity level also can be reached more than 96%；

4, the present invention in sample knowledge storehouse baseline sample number reduce time, to different faults type and fault severity level Overall recognition success rate can reduce, but remain to keep 100% to Fault Identification success rate；

5, the simple easy programming of self adaptation Grey Relation Algorithm in the present invention, it is possible to preferably solve algorithm for pattern recognition easy-to-use Property with the contradictory problems of accuracy.

Accompanying drawing explanation

Fig. 1 is that the fractal box algorithm passing through when fault diameter is 7mils to improve is from bearing normal condition and difference The characteristic vector extracted in the vibration signal of malfunction；

Fig. 2 is that the fractal box algorithm passing through when fault type is inner ring fault to improve is serious from bearing different faults The characteristic vector extracted in the vibration signal of degree.

Detailed description of the invention

Below in conjunction with specific embodiment, the present invention is expanded on further.Should be understood that these embodiments are merely to illustrate the present invention Rather than restriction the scope of the present invention.In addition, it is to be understood that after having read the content that the present invention lectures, people in the art The present invention can be made various changes or modifications by member, and these equivalent form of values fall within the application appended claims equally and limited Scope.

A kind of based on the fractal box algorithm improved with self adaptation grey correlation theory algorithm the rolling that the present invention provides Dynamic bearing method for diagnosing faults comprises the following steps:

Step 1, to object rolling bearing the shaking under normal operating conditions and under different faults pattern in rotating machinery Dynamic signal is sampled, and obtains bearing vibration signal data sample, wherein, the corresponding different fault type of different fault modes And the order of severity, and in bearing vibration signal data sample, different vibration signals and different faults pattern one_to_one corresponding.

Step 2, the fractal box algorithm passing through to improve extract each vibration letter from bearing vibration signal data sample Number the dominant characteristics vector characterizing fault signature, and according to the corresponding relation of different vibration signals with different faults pattern, Corresponding relation between each dominant characteristics vector and corresponding failure pattern.

Fractal box algorithm can use tradition fractal box algorithm, and concrete calculating process is as follows:

If A is belonging to theorem in Euclid space RⁿIn a certain non-NULL to be calculated compact, N (A, ε) is to cover with box that the length of side is ε Minimum box number needed for A, then definition box counting dimension D is:

$D=\underset{\mathrm{\ϵ}\→0}{lim}\frac{logN(A,\mathrm{\ϵ})}{log(1/\mathrm{\ϵ})}---\left(1\right)$

The bearing vibration signal obtained for actual samples, owing to there is sample frequency, the minimum length of side of box generally takes For sampling interval σ, i.e. ε=σ.

If vibration signal is x, approximation method is used to make box minimum edge a length of sampling interval σ of covering vibration signal x, meter Calculate the box using the length of side to be k σ and cover the minimum box number N of vibration signal x_kσ, then:

p₁=max{x_k(i-1)+1, x_k(i-1)+2... x_k(i-1)+k+1} (2)

p₂=min{x_k(i-1)+1, x_k(i-1)+2... x_k(i-1)+k+1} (3)

$p\left(k\mathrm{\σ}\right)=\underset{i=1}{\overset{N_0/k}{\Σ}}\left|p_1-p_2\right|---\left(4\right)$

In formula (2), (3), i=1,2 ..., N₀/ k, N₀It is sampled point number, k=1,2 ... K, K ＜ N₀, x_k(i-1)+k+1 It is kth (i-1)+k+1 the sampled point of vibration signal x；In formula (4), p (k σ) is the range scale of the vertical coordinate of x (i), Then N_kσIt is expressed as:

N_kσ=p (k σ)/k σ+1 (5)

Select matched curve lgk σ～lgN_kσThe middle linearity preferable one section as non-scaling section, then:

lgN_kσ=d_Blgkσ+b (6)

In formula (6), d_BBeing the slope of non-scaling section matched curve, b is the intercept of non-scaling section matched curve.

Generally, method of least square is utilized to calculate the slope of non-scaling section, it is simply that the tradition of vibration signal x to be calculated D value of fractal box:

$D=-\frac{(k_2-k_1+1)\Σ\left(lgk\right)\·log{N}_{k\mathrm{\σ}}-\Σ\left(lgk\right)\·\Σlg{N}_{k\mathrm{\σ}}}{(k_2-k_1+1)\Σ{\lg }^{2}k-{(\Σlgk)}^2}---\left(7\right)$

In formula (7), k₁And k₂It is respectively the beginning and end of non-scaling section, k₁≤k≤k₂。

The present invention proposes the fractal box algorithm of a kind of improvement, comprises the following steps:

Step 2.1, in order to reduce box minimum length of side ε, to improve computational accuracy, vibration signal x is carried out resampling, adopts Number of samples is 2^K；

Step 2.2, vibration signal x is carried out K phase space reconfiguration, according to sampled point number determine phase space reconstruction repeatedly For dimension, then iteration dimension is respectively 2,3,4 ... lg2^K+1.If the value of the ith sample point of vibration signal x is x_i, i=1, 2 ..., N₀/ k, then the derivation method of the box number covering this vibration signal is as follows:

As k=1:

P₁=max{x_i, x_i+1, P₂=min{x_i, x_i+1, now phase space reconstruction dimension is 2 dimensions.

As k=2:

P₁=max{x_2i-1, x_2i, x_2i+1, P₂=min{x_2i-1, x_2i, x_2i+1, now phase space reconstruction dimension is 3-dimensional.

As k=3:

P₁=max{x_3i-2, x_3i-1, x_3i, x_3i+1, P₂=min{x_3i-2, x_3i-1, x_3i, x_3i+1, now phase space reconstruction dimension Number is 4 dimensions.

……

As k=K:

P₁=max{x_Ki-K+1, x_Ki-K+2..., x_Ki+1, P₂=min{x_Ki-K+1, x_Ki-K+2..., x_Ki+1, now reconstruct empty mutually Between dimension be K+1.

Being derived from above, vibration signal x has carried out K phase space reconfiguration altogether, each phase space reconfiguration can be right A lgN should be obtained_kσ, so can draw out lgk σ～lgN_kσGraph of relation.Do not have strict due to matched curve Linear relationship, therefore, use improve fractal box algorithm, to the non-scaling section derivation at K the point obtained, obtain not With slope of curve D at point₁, D₂, D₃…D_K, it is the fractal box at different phase space reconfiguration.The D that will try to achieve₁, D₂, D₃…D_KAs K characteristic parameter in the characteristic vector of fault mode corresponding for sign current vibration signal x, roll as object The foundation of bearing fault pattern recognition.

Step 3, according to dominant characteristics vector and fault mode between corresponding relation set up sample knowledge storehouse, at sample knowledge Storehouse stores and has following data:

$C_1=\left[\begin{array}{c}{c}_1\left(1\right)\\ c_1\left(2\right)\\ \mathrm{...}\\ c_1\left(k\right)\\ \mathrm{...}\\ c_1\left(K\right)\end{array}\right],C_2=\left[\begin{array}{c}{c}_2\left(1\right)\\ c_2\left(2\right)\\ \mathrm{...}\\ c_2\left(k\right)\\ \mathrm{...}\\ c_2\left(K\right)\end{array}\right],...,C_j=\left[\begin{array}{c}{c}_j\left(1\right)\\ c_j\left(2\right)\\ \mathrm{...}\\ c_j\left(k\right)\\ \mathrm{...}\\ c_j\left(K\right)\end{array}\right],\mathrm{...},C_M=\left[\begin{array}{c}{c}_M\left(1\right)\\ c_M\left(2\right)\\ \mathrm{...}\\ c_M\left(k\right)\\ \mathrm{...}\\ c_M\left(K\right)\end{array}\right];$

Wherein, C_jFor jth fault mode, j=1,2 ..., M, M are the total number of fault mode,For with C_jRight The characteristic vector answered, c_jK kth characteristic parameter that () is characterized in vector.

The real-time vibration signal of the rolling bearing to be diagnosed under step 4, in real time acquisition current operating conditions, and by improving Fractal box algorithm extract from real-time vibration signal real-time dominant characteristics vector, based on step 3 set up sample knowledge Storehouse, utilizes Grey Relation Algorithm to calculate real-time dominant characteristics vector and the degree of association of each dominant characteristics vector in sample knowledge storehouse, The fault mode of rolling bearing to be diagnosed is obtained by the degree of association.

In step 4, the fractal box algorithm of improvement equally as described in step 2 use tradition box dimension of fractals Figure method, or the fractal box algorithm of the improvement of present invention offer is provided.

In step 4, Grey Relation Algorithm can use common Grey Relation Algorithm, and concrete calculating process is as follows:

If extracting the real-time dominant characteristics vector B obtained it isIn formula, D_kFor kth characteristic parameter, k=1,2 ..., K, K are characterized the total number of parameter.

For resolution ratio ρ ∈ (0,1), have:

$\mathrm{\ξ}(D_k,c_i(k\left)\right)=\frac{\underset{j}{\min }\underset{k}{\min }|D_k-c_j\left(k\right)|+\mathrm{\ρ}\·\underset{j}{\max }\underset{k}{\max }|D_k-c_j\left(k\right)|}{|D_k-c_j\left(k\right)|+\mathrm{\ρ}\·\underset{j}{\max }\underset{k}{\max }|D_k-c_j\left(k\right)|}---\left(8\right);$

$\mathrm{\ξ}(B,C_j)=\frac{1}{K}\underset{k=1}{\overset{K}{\Σ}}\mathrm{\ξ}(D_{k,i},c_j(k\left)\right)---\left(9\right);$

ξ(D_k, c_j(k)) for being kth characteristic parameter and jth fault mode C in real-time dominant characteristics vector B_jCorresponding The coefficient of association of kth characteristic parameter in characteristic vector, ξ (B, C_j) it is real-time dominant characteristics vector B and jth in sample knowledge storehouse Individual fault mode C_jGrey relational grade.

After trying to achieve the real-time dominant characteristics vector B degree of association vectorial with each dominant characteristics in sample knowledge storehouse, it is possible to will The real-time vibration signal of rolling bearing to be diagnosed the most corresponding for dominant characteristics vector B is classified to the fault belonging to most relevance degree Pattern.

In step 4, Grey Relation Algorithm can also use a kind of self adaptation Grey Relation Algorithm that the present invention provides, bag Include following steps:

Step 4.1, extracted the real-time dominant characteristics vector B that obtains by described step 2.1 to step 2.3 and be set to In formula, D_kFor kth characteristic parameter, k=1,2 ..., K, K are characterized the total number of parameter；

Step 4.2, calculate in real-time dominant characteristics vector B each fault mode pair in each characteristic parameter and sample knowledge storehouse Entropy between the characteristic parameter of relevant position in the characteristic vector answered, wherein, kth feature ginseng in real-time dominant characteristics vector B Number and jth fault mode C_jIn characteristic of correspondence vector, the entropy between kth characteristic parameter is E_jK (), then have:

In formula (10),And | Δ d_j(k) |=| D_k-c_j(k)|；

Step 4.3, calculate in real-time dominant characteristics vector B each fault mode pair in each characteristic parameter and sample knowledge storehouse The relative entropy of characteristic of correspondence parameter in the characteristic vector answered, wherein, kth characteristic parameter in real-time dominant characteristics vector With jth fault mode C in sample knowledge storehouse_jIn characteristic of correspondence vector, the relative entropy of kth characteristic parameter is e_j(k), e_j (k)=E_j(k)/lnM (11)；

Step 4.4, to be calculated in real-time dominant characteristics vector B each characteristic parameter different relative in sample knowledge storehouse The weight coefficient of fault mode, wherein, in real-time dominant characteristics vector B, kth characteristic parameter is relative to jth in sample knowledge storehouse Individual fault mode C_jWeight coefficient be a_j(k), In formula (12), H_j(k)=1-e_j (k)；

Step 4.5, it is calculated the degree of association of each fault mode in real-time dominant characteristics vector B and sample knowledge storehouse, its In, real-time dominant characteristics vector B and jth fault mode C in sample knowledge storehouse_jThe degree of association be ξ (B, C_j),In formula, ξ (D_k, c_j(k)) for being kth feature in real-time dominant characteristics vector B Parameter and jth fault mode C_jThe coefficient of association of kth characteristic parameter in characteristic of correspondence vector, In formula (13), ρ is resolution ratio.

A kind of based on improvement fractal box algorithm proposed by the invention and self adaptation grey correlation theory algorithm The detailed description of the invention of Fault Diagnosis of Roller Bearings is examined with the rolling bearing fault of bearing data center of Xi Chu university of the U.S. As a example by Duan, detailed process is as follows:

This rolling bearing fault diagnosis experimental provision is by a torquemeter, energy meter, a three phase induction motor Deng composition, load power and rotating speed are recorded by sensor.Desired torque load can be obtained by elastic calibration device.Electronic The rotor of machine drive end is supported by test bearing (i.e. diagnosing object), and is provided with single-point by electro-discharge machining in test bearing Fault, fault diameter includes 7mils, 14mils, 21mils and 28mils (i.e. fault severity level), and fault type includes inner ring Fault, rolling element fault, outer ring fault.One bandwidth is installed on motor-driven end shield shell and is up to the accelerometer of 5000Hz, And by monitor collecting test bearing vibration data under different operating state, wherein sample frequency is 12kHz.Examination Grooved antifriction bearing model used in testing is 6205-2RS JEM SKF.

When control torque load is adjusted to 0 horsepower and motor speed is 1797r/min, start collecting test bearing Vibration data.Gather the vibration data under bearing normal condition and different faults type and fault severity level for diagnosis point Analysis, as shown in table 1, is subdivided into 11 classes according to different fault types and fault severity level by fault mode.The test gathered The vibration data of bearing is divided into 550 data samples, and each data sample comprises 2048 sample number strong points, and each two number According to the most overlapping between sample.In these 550 data samples, randomly select 110 data samples for setting up sample knowledge storehouse, Remaining 440 data samples, as test sample, are used for verifying effectiveness of the invention.

Table 1 is for the vibration data of the test bearing of diagnostic analysis

When fault diameter is 7mils by tradition fractal box algorithm from bearing normal condition and different faults state Vibration signal in the characteristic vector extracted as shown in table 2, figured by the box dimension of fractals of improvement when fault diameter is 7mils The characteristic vector that method is extracted from the vibration signal of bearing normal condition and different faults state is as shown in Figure 1；Work as fault type For the spy extracted from the vibration signal of the bearing different faults order of severity by tradition fractal box algorithm during inner ring fault Levy vector as shown in table 3, when fault type is inner ring fault by the fractal box algorithm of improvement from bearing different faults The characteristic vector extracted in the vibration signal of the order of severity is as shown in Figure 2.

Table 2 passes through tradition fractal box algorithm from bearing normal condition and different faults when fault diameter is 7mils The characteristic vector extracted in the vibration signal of state

Table 3 passes through tradition fractal box algorithm from the bearing different faults order of severity when fault type is inner ring fault Vibration signal in extract characteristic vector

From table 2 and table 3, the characteristic vector characterizing fault signature extracted by tradition fractal box algorithm is only For one-dimensional, and characterize between different faults type and the characteristic vector of the order of severity the most close, not there is significantly differentiation Degree.And from Fig. 1 and Fig. 2, the characteristic vector being extracted sign fault signature by the fractal box improved has multidimensional, And between sign different faults type and the characteristic vector of the order of severity, there is significant discrimination.

According to failure symptom (the dominant characteristics vector the most extracted) and the fault mode (fault of i.e. known rolling bearing Type and the order of severity) relation sets up sample knowledge storehouse, as the benchmark knowledge base of self adaptation Grey Relation Algorithm model.To treat Identify is defeated from the dominant characteristics vector (the fractal box algorithm by improving) characterizing fault signature of test sample extraction Enter in self adaptation Grey Relation Algorithm model, output diagnostic result (i.e. fault type and the order of severity), as shown in table 4.

Table 4 diagnostic result

As shown in Table 4, the present invention can identify different rolling bearing fault types and the serious journey of fault accurately and effectively Degree；The fractal box algorithm improved in the present invention compares tradition fractal box algorithm, it is possible to believe from the vibration of rolling bearing Extracting the characteristic vector characterizing fault signature of more discrimination in number, therefore diagnosis success rate is greatly improved；In the present invention Self adaptation Grey Relation Algorithm can reach 100% to the Fault Identification success rate of rolling bearing, and to different faults type and The overall recognition success rate of fault severity level also can reach more than 96%；In the present invention, self adaptation Grey Relation Algorithm is the easiest Programming, it is possible to preferably solve the contradictory problems of algorithm for pattern recognition ease for use and accuracy.

Claims (6)

1. a rolling bearing fault diagnosis based on the fractal box algorithm improved with self adaptation grey correlation theory algorithm Method, it is characterised in that comprise the following steps:

Step 1, to the object rolling bearing in rotating machinery under normal operating conditions and under different faults pattern vibration letter Number sample, obtain bearing vibration signal data sample, wherein, the corresponding different fault type of different fault modes and tight Weight degree, and in bearing vibration signal data sample, different vibration signals and different faults pattern one_to_one corresponding；

Step 2, the fractal box algorithm passing through to improve extract each vibration signal from bearing vibration signal data sample Characterize the dominant characteristics vector of fault signature, and according to the corresponding relation of different vibration signals with different faults pattern, obtain each Corresponding relation between dominant characteristics vector and corresponding failure pattern, wherein, the fractal box algorithm of improvement comprises the following steps:

Step 2.1, vibration signal x being carried out resampling, sampling number is 2^K；

Step 2.2, vibration signal x is carried out K phase space reconfiguration, after each phase space reconfiguration, calculates a fractal box, The dominant characteristics vector of vibration signal x it is made up of all fractal box obtained；

Step 3, according to dominant characteristics vector and fault mode between corresponding relation set up sample knowledge storehouse；

The real-time vibration signal of the rolling bearing to be diagnosed under step 4, in real time acquisition current operating conditions, and by dividing of improving Shape box counting dimension algorithm extracts real-time dominant characteristics vector, the sample knowledge storehouse set up based on step 3, profit from real-time vibration signal Calculate real-time dominant characteristics vector and the degree of association of each fault mode in sample knowledge storehouse with Grey Relation Algorithm, pass through the degree of association Obtain the fault mode of rolling bearing to be diagnosed.

A kind of fractal box algorithm based on improvement and self adaptation grey correlation theory algorithm Fault Diagnosis of Roller Bearings, it is characterised in that in described step 2.2, carries out kth time phase space weight to vibration signal x The fractal box obtained after structure is D_k, now, phase space dimension is k+1 dimension, D value of fractal box_kCalculation procedure include:

Step 2.2.1, calculate vibration signal x at the vertical coordinate range scale p (k ε) of current phase space,In formula:

ε is the minimum length of side of the box covering vibration signal x；

N₀For the sum of sampled point, N₀=2^K；

p₁=max{x_k(i-1)+1, x_k(i-1)+2..., x_k(i-1)+k+1, x_k(i-1)+k+1Kth (i-1)+k+1 the sampling for vibration signal x The value of point；

p₂=min{x_k(i-1)+1, x_k(i-1)+2... x_k(i-1)+k+1}；

The box that step 2.2.2, calculating use the length of side to be k ε covers the minimum box number N of vibration signal x_kε, N_kε=p (k ε)/k ε+ 1；

Step 2.2.3, selection matched curve lgk ε～lgN_kεThe middle linearity preferable one section as non-scaling section, wherein, lgN_kε= d_BLgk ε+b, d_BFor the slope of non-scaling section matched curve, b is the intercept of non-scaling section matched curve；

Step 2.2.4, utilize method of least square to calculate the slope of non-scaling section, be D value of fractal box_k。

A kind of fractal box algorithm based on improvement and self adaptation grey correlation theory algorithm Fault Diagnosis of Roller Bearings, it is characterised in that in described step 2.2.4, D value of fractal box_kComputing formula be:

In formula, k₁And k₂It is respectively non-scaling section Beginning and end, k₁≤k≤k₂。

A kind of fractal box algorithm based on improvement and self adaptation grey correlation theory algorithm Fault Diagnosis of Roller Bearings, it is characterised in that in described step 4, utilizes conventional Grey Relation Algorithm to calculate in real time Dominant characteristics vector and the degree of association of each dominant characteristics vector in sample knowledge storehouse.

A kind of fractal box algorithm based on improvement and self adaptation grey correlation theory algorithm Fault Diagnosis of Roller Bearings, it is characterised in that in described step 4, utilizes self adaptation Grey Relation Algorithm to calculate in real time Dominant characteristics vector and the degree of association of each dominant characteristics vector in sample knowledge storehouse, comprise the following steps:

The real-time dominant characteristics vector B that step 4.1, extraction obtain is set toIn formula, D_kFor kth characteristic parameter, k=1, 2 ..., K, K are characterized the total number of parameter；

Described sample knowledge storehouse stores and has following data:

Wherein, C_jFor jth fault mode, j=1,2 ..., M, M are the total number of fault mode,For with C_jCorresponding Characteristic vector, c_jK kth characteristic parameter that () is characterized in vector；

Step 4.2, to calculate each characteristic parameter in real-time dominant characteristics vector B corresponding with each fault mode in sample knowledge storehouse Entropy between the characteristic parameter of relevant position in characteristic vector, wherein, in real-time dominant characteristics vector B kth characteristic parameter with Jth fault mode C_jIn characteristic of correspondence vector, the entropy between kth characteristic parameter is E_jK (), then have:

In formula,And | Δ d_j(k) |=| D_k-c_j(k)|；

Step 4.3, to calculate each characteristic parameter in real-time dominant characteristics vector B corresponding with each fault mode in sample knowledge storehouse The relative entropy of characteristic of correspondence parameter in characteristic vector, wherein, in real-time dominant characteristics vector kth characteristic parameter with Jth fault mode C in sample knowledge storehouse_jIn characteristic of correspondence vector, the relative entropy of kth characteristic parameter is e_j(k), e_j (k)=E_j(k)/lnM；

Step 4.4, it is calculated in real-time dominant characteristics vector B each characteristic parameter relative to different faults in sample knowledge storehouse The weight coefficient of pattern, wherein, in real-time dominant characteristics vector B, kth characteristic parameter is relative to jth event in sample knowledge storehouse Barrier pattern C_jWeight coefficient be a_j(k),In formula, H_j(k)=1-e_j(k)；

Step 4.5, it is calculated the degree of association of each dominant characteristics vector in real-time dominant characteristics vector B and sample knowledge storehouse, will The real-time vibration signal of rolling bearing to be diagnosed the most corresponding for dominant characteristics vector B is classified to the fault belonging to most relevance degree Pattern, wherein, real-time dominant characteristics vector B and jth fault mode C in sample knowledge storehouse_jThe degree of association be ξ (B, C_j),In formula, ξ (D_k, c_j(k)) for being kth spy in real-time dominant characteristics vector B Levy parameter and jth fault mode C_jThe coefficient of association of kth characteristic parameter in characteristic of correspondence vector.

A kind of fractal box algorithm based on improvement and self adaptation grey correlation theory algorithm Fault Diagnosis of Roller Bearings, it is characterised in that in described step 4.5, described ξ (D_k, c_j(k)) computing formula be:

In formula, ρ is resolution ratio.