CN109297705A - Epicyclic gearbox vibration signal method for diagnosing faults based on MED and fuzzy entropy - Google Patents
Epicyclic gearbox vibration signal method for diagnosing faults based on MED and fuzzy entropy Download PDFInfo
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- G01M13/00—Testing of machine parts
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Abstract
The invention discloses a kind of epicyclic gearbox vibration signal method for diagnosing faults based on MED and fuzzy entropy, which is characterized in that build Wind turbines experimental provision, pass through Wind turbines experimental provision and acquire epicyclic gearbox vibration signal；Denoising is carried out to epicyclic gearbox vibration signal using MED；EMD decomposition is carried out to epicyclic gearbox vibration signal, rejects reactive component, obtains multiple effective IMF components, calculate each effectively fuzzy entropy of IMF component and retains the fuzzy the smallest IMF component of entropy；The signal of IMF component the smallest to fuzzy entropy carries out envelope spectrum analysis, analyzes fault characteristic frequency.This method can effectively extract fault characteristic frequency, and carry out the adverse effect of fault diagnosis phenomena such as can effectively filter out interference of the noise to characteristic frequency, effectively eliminate modal overlap, end effect to epicyclic gearbox signal.
Description
Technical field
The invention belongs to rotary machinery fault diagnosis fields, are based especially on the epicyclic gearbox vibration of MED and fuzzy entropy
Signal fault diagnosis method.
Background technique
Compared with fixed axis gear case, epicyclic gearbox is with small in size, light-weight, bearing capacity is strong, transmission ratio is big and passes
The advantages that efficiency of movement is high is therefore widely used in all kinds of transmission systems such as aviation, engineering machinery, wind-power electricity generation, vehicle.
Since epicyclic gearbox is usually operated in the environment of low-speed heave-load, surface is subjected to the effect of alternating load, therefore crucial
Position is easy to appear failure.The vibration signal of epicyclic gearbox is the coupling of a variety of motivators, and frequency content is sufficiently complex,
It not only include the speed of each component, gear pair meshing frequency and its frequency multiplication, the intrinsic frequency being also ignited containing equipment；Row
Star-wheel was not only engaged with sun gear, but also was engaged with other planetary gears, and this engagement system causes certain characteristic frequencies very low；Planetary gear
With sensor relative position with the variation of operating, vibration transfer path constantly changes, and installation and foozle, planetary gear pass through
Effect etc. can all cause the amplitude or frequency modulation(PFM) of signal, cause sideband complicated, add Environmental Noise Influence, failure-frequency
It is easy to be submerged, brings very big difficulty to vibration analysis.
Summary of the invention
Goal of the invention:, effectively eliminate modal overlap, end effect phenomena such as pair of the existing technology in order to solve the problems, such as
Adverse effect caused by fault diagnosis, the present invention provide a kind of epicyclic gearbox vibration signal failure based on MED and fuzzy entropy
Diagnostic method.
Technical solution: a kind of epicyclic gearbox vibration signal method for diagnosing faults based on MED and fuzzy entropy, including it is following
Step:
(1) Wind turbines experimental provision is built, epicyclic gearbox vibration signal is acquired by Wind turbines experimental provision；
(2) denoising is carried out to epicyclic gearbox vibration signal using MED；
(3) EMD decomposition is carried out to epicyclic gearbox vibration signal, rejects reactive component, obtains multiple effective IMF components,
It calculates each effectively fuzzy entropy of IMF component and retains the fuzzy the smallest IMF component of entropy；
(4) signal of IMF component the smallest to fuzzy entropy carries out envelope spectrum analysis, analyzes fault characteristic frequency.
Further, in step (1), the expression formula of epicyclic gearbox vibration signal are as follows:
Y (n)=h (n) x (n)+e (n) (1)
In formula, y (n) is epicyclic gearbox vibration signal, and h (n) is transmission function, and x (n) is epicyclic gearbox impact sequence
Column, e (n) are noise.
Further, it in step (2), constructs inverse filter ω (n), there is following relationship between ω (n) and y (n), x (n):
In formula, l indicates first point of filter, and L is the length of inverse filter ω (n)；
Objective function K (ω (n)):
Wherein x (i) is i-th of value of sequence x (n)；
The value for taking optimal filter ω (n) to make objective function K (ω (n)) is maximum, and the entropy after filtering signal is minimum.
Further, in step (3), the result of EMD decomposition is carried out to epicyclic gearbox vibration signal are as follows:
Wherein, J is the sum of the IMF decomposited, IMFm,1, IMFm,2……IMFm,jIt is the j intrinsic mode letters decomposited
Number, rmIt (t) is residual components；
It repeats to carry out same group of epicyclic gearbox vibration signal EMD decomposition up to population mean number K, obtains K group IMF letter
Number, obtained intrinsic mode function carry out population mean, i.e.,
Cs(t) s-th of the IMF component come is decomposited for EMD.
Further, in step (3), the method for rejecting reactive component are as follows: find out mutual between each IMF component and original signal
Correlation function value, given threshold, the IMF component for cross-correlation function value lower than threshold value is determined as reactive component, and is picked
It removes.
Further, in step (3), the method for the fuzzy entropy of calculating IMF component are as follows:
It is assumed that the mode dimension for the data sequence Y=[y (1), y (2) ... y (Ni)] that sampling number is Ni is mi, then weigh
Structure generates one group of mi dimensional vector: Ymi(i)=[y (i), y (i+1) ... y (i+mi-1)]-u (i), i=1 in formula, 2 ... Ni-mi
+ 1, enabling u (i) is the mean value of vector, i.e.,
If vector Xmi(i),XmiThe distance between (j)Formula
Middle i, j=1,2 ... Ni-mi+1；
Two vector X are acquired by the fuzzy membership function that chaos pseudo random sequence complexity is predictedmi(i)、Xmi(j) between
Similarity is
Define intermediate functionφmi(r):
Above step is repeated to mi+1 dimensional pattern dimension
The fuzzy entropy for obtaining former time series is
FuzzyEn (mi, r, N)=ln φmi(r)-lnφmi+1(r) (11)
R is similar tolerance.
Further, mode dimension m takes 2, and similar tolerance r=0.2*SD, SD are each in epicyclic gearbox vibration signal
The standard deviation of sampled point.
The utility model has the advantages that a kind of epicyclic gearbox vibration signal fault diagnosis based on MED and fuzzy entropy provided by the invention
Method, to vibration signals collecting, and to signal carry out MED denoising after, carry out EMD decomposition, after decomposition, in conjunction with EMD high frequency
Decomposition feature and fuzzy entropy to low frequency analyze vibration signal the screening of IMF component.This method can be extracted effectively
Fault characteristic frequency, and phenomena such as interference of the noise to characteristic frequency can be effectively filtered out, effectively eliminate modal overlap, end effect
The adverse effect of fault diagnosis is carried out to epicyclic gearbox signal.
Detailed description of the invention
Fig. 1 is the structural schematic diagram of Wind turbines experimental provision；
Fig. 2 is that epicyclic gearbox MED removes dryness front and back comparison diagram；
Fig. 3 (a) is 1000r normal condition lower planetary gear case vibration signal envelope spectrogram；
Fig. 3 (b) is 1000r malfunction lower planetary gear case vibration signal envelope spectrogram；
Fig. 4 (a) is that the normal epicyclic gearbox vibration signal EMD of 1000r obscures entropy maximum IMF component envelope spectrum；
Fig. 4 (b) is that 1000r broken teeth epicyclic gearbox vibration signal EMD obscures entropy maximum IMF component envelope spectrum.
Specific embodiment
The present invention will be further explained below with reference to the attached drawings and specific examples.
A kind of epicyclic gearbox vibration signal method for diagnosing faults based on MED and fuzzy entropy, comprising the following steps:
(1) Wind turbines experimental provision is built, Wind turbines experimental provision schematic diagram is as shown in Figure 1, Wind turbines are tested
Device include driving motor 1, main shaft 2, the first torque rotary speed sensor 3, homopolar bevel gear case 4, single-pinion planetary gear case 5,
First rolling bearing 6, first shaft coupling 7, the second torque rotary speed sensor 8, the first rolling bearing 9, first shaft coupling 10 and negative
Carry motor 11.Driving motor simulates wind wheel torque input, connects load motor after secondary gear is driven.Turn for simulation wind wheel
The time-varying characteristics of speed, control motor speed using frequency converter.Epicyclic gearbox is prepared by artificial defect mode
All types of failures, the vibration shape of bearing, main shaft and gear-box when the testing stand analog rolling bearing and gear-box most common failure
Condition.The planetary gear broken teeth failure studied herein installs PAC-UT1000 low frequency also by manually preparing, in epicyclic gearbox seat two sides
Wide-band vibration sensor.Vibration signal acquisition system is by Polar9300e portable industrial pc, built-in PCI-2 vibration acquisition
Card, using UT-1000 sensor, it is 5230 that vibration signal sample frequency, which is arranged, sampled point 16384.
Epicyclic gearbox vibration signal, the expression formula of epicyclic gearbox vibration signal are acquired by Wind turbines experimental provision
Are as follows:
Y (n)=h (n) x (n)+e (n) (1)
In formula, y (n) is epicyclic gearbox vibration signal, and h (n) is transmission function, and x (n) is epicyclic gearbox impact sequence
Column, e (n) are noise.
(2) it since the vibration signal of epicyclic gearbox has stronger ambient noise, before signal extraction failure, needs
Denoising is carried out to signal.The basic principle of MED is by the prominent sharp pulse of solution deconvolution, using kurtosis maximum value as changing
The termination condition in generation.According to kurtosis principle, kurtosis value shows that more greatly impact ingredient specific gravity is more in signal, this characteristic can
Prominent shock characteristic well, therefore MED method is very suitable for the noise reduction process of rotating machinery impact fault-signal.The step
It is rapid that denoising is carried out to epicyclic gearbox vibration signal using MED.The purpose of MED algorithm mainly finds a kind of inverse filter
ω (n), so that output signal y (n) shock characteristic as much as possible for restoring x (n).That is:
In formula, l indicates first point of filter, and L is the length of inverse filter ω (n)；
IfSequence for the estimated value of ω (n), after x (n) deconvolutionCloser to epicyclic gearbox
Impact signal x (n), then entropy is smaller, simultaneouslyTend to be optimal, therefore measures the big of entropy using the norm of x (n) here
It is small, defined function K (ω (n)):
Wherein x (i) is i-th of value of sequence x (n)；
The value for taking optimal filter ω (n) to make objective function K (ω (n)) is maximum, i.e., the entropy after filtering signal is minimum.
(3) EMD decomposition, the result of decomposition are carried out to epicyclic gearbox vibration signal are as follows:
Wherein, J is the sum of the IMF decomposited, IMFm,1, IMFm,2……IMFm,jIt is the j intrinsic mode letters decomposited
Number, rmIt (t) is residual components；
It repeats to carry out same group of epicyclic gearbox vibration signal EMD decomposition up to population mean number K, obtains K group IMF letter
Number, obtained intrinsic mode function carry out population mean, i.e.,
Cs(t) s-th of the IMF component come is decomposited for EMD.
The method for rejecting reactive component are as follows: find out the cross-correlation function value between each IMF component and original signal, set threshold
Value, the IMF component for cross-correlation function value lower than threshold value is determined as reactive component, and is rejected.Wherein cross-correlation function
Is defined as: cross-correlation function is the phase for describing random signal f (t), g (t) between the value of any two different moments n, m
Pass degree,
Reactive component is rejected, multiple effective IMF components are obtained；
It calculates each effectively fuzzy entropy of IMF component and retains the fuzzy the smallest IMF component of entropy；
Fuzzy entropy is the parameter for reflecting the unordered degree of One-dimension Time Series, it advises sample characteristics using Fuzzy Criteria
The classification of rule property has very high sensibility to the variation of signal, is capable of the dynamics mutation of detection of complex system well.If row
There are local damages for some gear teeth in star-wheel, then in a swing circle relative to gear ring, planetary gear will be with sun gear
Generate the shock of regularity respectively between gear ring, impact is more obvious, and sequence is more regular.And EMD decomposition be by signal from
High band is decomposed to low-frequency range, finds out the fuzzy entropy of each IMF component, it can be determined that goes out the strongest IMF of impact points
Amount.
Calculate the method that IMF component must obscure entropy are as follows:
It is assumed that the mode dimension for the data sequence Y=[y (1), y (2) ... y (Ni)] that sampling number is Ni is mi, then weigh
Structure generates one group of mi dimensional vector: Ymi(i)=[y (i), y (i+1) ... y (i+mi-1)]-u (i), i=1 in formula, 2 ... Ni-mi
+ 1, enabling u (i) is the mean value of vector, i.e.,
If vector Xmi(i),XmiThe distance between (j)Formula
Middle i, j=1,2 ... Ni-mi+1；
Two vector X are acquired by the fuzzy membership function that chaos pseudo random sequence complexity is predictedmi(i)、Xmi(j) between
Similarity is
Define intermediate functionφmi(r):
Above step is repeated to mi+1 dimensional pattern dimension
The fuzzy entropy for obtaining former time series is
FuzzyEn (mi, r, N)=ln φmi(r)-lnφmi+1(r) (12)
R is similar tolerance.
In engineering, as mode dimension m=1 or 2, similar tolerance r=(0.1-0.25) * SD, the classifying quality of fuzzy entropy
Preferably, there is good statistics.The present embodiment calculates the fuzzy of epicyclic gearbox vibration signal in m=2, r=0.2*SD
Entropy, SD are the standard deviation of each sampled point in epicyclic gearbox vibration signal.
(4) signal of IMF component the smallest to fuzzy entropy carries out envelope spectrum analysis, analyzes fault characteristic frequency.It uses
Fuzzy entropy measures the order of component, carries out envelope spectrum analysis to fuzzy entropy minimum component, thus reach to vibration signal into
The purpose of row fault diagnosis.
The preparation of epicyclic gearbox broken teeth failure is by spark erosion technique by 0.5 times of some tooth on planetary gear
Tine length polishes.The fault characteristic frequency of epicyclic gearbox:
fmFor the revolving speed of gear shaft, zpFor the number of teeth of gear.
Epicyclic gearbox parameter: the sun gear number of teeth is 33, and the planetary gear number of teeth is 19, and the gear ring number of teeth is 72, input power
3KW.Calculating meshing frequency of the epicyclic gearbox at 1000r/min by above formula is 375HZ, fault characteristic frequency 39Hz.
Vibration signal such as Fig. 2 institute of normal epicyclic gearbox and broken teeth failure gear-box when the speed of mainshaft is 1000r/min
Show.
From figure 2 it can be seen that untreated broken teeth fault-signal is shown and the visibly different spy of normal signal
Sign, but the fault signature that envelope spectrum directly extracts is 11Hz, still has a certain distance with theoretical fault characteristic frequency 39Hz,
It is for further processing using the method based on MED to normal and failure.
Firstly, by vibration signal processing, figure after obtained MED denoising, decomposition vibration signal, greater than ignoring for threshold value.
The fuzzy entropy of each IMF component except residual components is calculated, modulus pastes the smallest component of entropy and carries out signal reconstruction.To 0-
The frequency range of 10000Hz is amplified, and the signal of acquisition is as follows:
Normal signal impact is unobvious it can be seen from Fig. 3 (a), Fig. 3 (b), due to the influence of environment and mechanical noise
And the mixed and disorderly impact generated and frequency content are filtered out.
According to above with same method extract broken teeth gear-box signal fault signature, and to the frequency of 0-1000Hz at
Divide and amplifies.Fig. 4 (a), Fig. 4 (b) are the vibration envelope spectrogram of broken teeth failure gear-box after being decomposed based on MED, with normal shape
Envelope spectrum under state is compared, and occurs the planet carrier speed of 4.7Hz, the sun gear speed of 15Hz respectively, at 41HZ
Fault characteristic frequency, actual fault characteristic frequency and theory characteristic frequency are not exactly the same, mainly due to planetary gear
There are deviations between the actual operating mode and theoretical value of case, in addition, velocity-measuring system error, experimental bench installation error, signal pass through
MED and synthesis after with information loss existing for original signal, can all influence the accuracy of characteristic frequency.
It can see from the above analysis, in 1000r/min, using the analysis of MED proposed in this paper and fuzzy entropy
Method can remove noise jamming well, capture the fault signature of epicyclic gearbox.
Claims (7)
1. a kind of epicyclic gearbox vibration signal method for diagnosing faults based on MED and fuzzy entropy, which is characterized in that including following
Step:
(1) Wind turbines experimental provision is built, epicyclic gearbox vibration signal is acquired by Wind turbines experimental provision；
(2) denoising is carried out to epicyclic gearbox vibration signal using MED；
(3) EMD decomposition is carried out to epicyclic gearbox vibration signal, rejects reactive component, obtains multiple effective IMF components, calculated
Each effectively fuzzy entropy of IMF component simultaneously retains the fuzzy the smallest IMF component of entropy；
(4) signal of IMF component the smallest to fuzzy entropy carries out envelope spectrum analysis, analyzes fault characteristic frequency.
2. the epicyclic gearbox vibration signal method for diagnosing faults based on MED and fuzzy entropy as described in claim 1, feature
It is, in step (1), the expression formula of epicyclic gearbox vibration signal are as follows:
Y (n)=h (n) x (n)+e (n) (1)
In formula, y (n) is epicyclic gearbox vibration signal, and h (n) is transmission function, and x (n) is epicyclic gearbox sequence of impacts, e
It (n) is noise.
3. the epicyclic gearbox vibration signal method for diagnosing faults based on MED and fuzzy entropy as claimed in claim 2, feature
It is, in step (2), constructs inverse filter ω (n), there is following relationship between ω (n) and y (n), x (n):
In formula, l indicates first point of filter, and L is the length of inverse filter ω (n)；
Objective function K (ω (n)):
Wherein x (i) is i-th of value of sequence x (n)；
The value for taking optimal filter ω (n) to make objective function K (ω (n)) is maximum, and the entropy after filtering signal is minimum.
4. the epicyclic gearbox vibration signal method for diagnosing faults based on MED and fuzzy entropy as described in claim 1, feature
It is, in step (3), the result of EMD decomposition is carried out to epicyclic gearbox vibration signal are as follows:
Wherein, J is the sum of the IMF decomposited, IMFm,1, IMFm,2……IMFm,jIt is the j intrinsic mode function decomposited, rm
It (t) is residual components；
It repeats to carry out same group of epicyclic gearbox vibration signal EMD decomposition up to population mean number K, obtains K group IMF function,
Obtained intrinsic mode function carries out population mean, i.e.,
Cs(t) s-th of the IMF component come is decomposited for EMD.
5. the epicyclic gearbox vibration signal method for diagnosing faults based on MED and fuzzy entropy as described in claim 1, feature
It is, in step (3), the method for rejecting reactive component are as follows: the cross-correlation function value between each IMF component and original signal is found out,
Given threshold, the IMF component for cross-correlation function value lower than threshold value is determined as reactive component, and is rejected.
6. the epicyclic gearbox vibration signal method for diagnosing faults based on MED and fuzzy entropy as described in claim 1, feature
It is, in step (3), the method for the fuzzy entropy of calculating IMF component are as follows:
It is assumed that the mode dimension for the data sequence Y=[y (1), y (2) ... y (Ni)] that sampling number is Ni is mi, then life is reconstructed
At one group of mi dimensional vector: Ymi(i)=[y (i), y (i+1) ... y (i+mi-1)]-u (i), i=1 in formula, 2 ... Ni-mi+1,
Enabling u (i) is the mean value of vector, i.e.,
If vector Xmi(i),XmiThe distance between (j)I in formula, j
=1,2 ... Ni-mi+1；
Two vector X are acquired by the fuzzy membership function that chaos pseudo random sequence complexity is predictedmi(i)、Xmi(j) similarity between
For
Define intermediate functionφmi(r):
Above step is repeated to mi+1 dimensional pattern dimension
The fuzzy entropy for obtaining former time series is
FuzzyEn (mi, r, N)=ln φmi(r)-lnφmi+1(r) (11)
R is similar tolerance.
7. the epicyclic gearbox vibration signal method for diagnosing faults based on MED and fuzzy entropy as claimed in claim 6, feature
It is, mode dimension m takes 2, and similar tolerance r=0.2*SD, SD are the standard of each sampled point in epicyclic gearbox vibration signal
Difference.
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