CN105092239A - Method for detecting early stage fault of gear - Google Patents
Method for detecting early stage fault of gear Download PDFInfo
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- CN105092239A CN105092239A CN201410322151.9A CN201410322151A CN105092239A CN 105092239 A CN105092239 A CN 105092239A CN 201410322151 A CN201410322151 A CN 201410322151A CN 105092239 A CN105092239 A CN 105092239A
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- gear
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- straight line
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- malfunction
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Abstract
A vibration signal x(n) of a gear under each operation state is measured and obtained. A difference sequence delta x(n) of the vibration signal x(n) is obtained by performing a difference operation on the vibration signal. A fluctuation function F(s) of the difference sequence delta x(n) is calculated by using Detrended Fluctuation Analysis (DFA). The minimum value and the maximum value of a logarithmic fluctuation function ln[F(s)] at each operation state are extracted. A picture is drawn by respectively taking the extracted minimum value and maximum value as a horizontal coordinate and a vertical coordinate, and data points corresponding to the fault states are fitted into a ''fault state line'' by using a least square method. A confidence interval is estimated for the ''fault state line'' by using a certain confidence level (1-alpha). Along with the deterioration of gear operation states, the data points are gradually close to the established confidence interval. When the data points are in the confidence interval, it may be considered that a gear box has fault in a certain probability and the fault early warning is sent out, at this time the operation state of the gear box is analyzed detailedly, and if necessary, the shutdown maintenance is needed.
Description
Technical field
The present invention relates to condition monitoring for rotating machinery and fault diagnosis field, be specifically related to a kind of initial failure of gear detection method.
Background technology
Gear is one of critical component of rotating machinery, is widely applied in the industrial production, and its operation conditions not only affects the running of himself, but also can produce directly impact to relevant plant equipment.If gear breaks down, the normal operation of plant equipment gently then can be affected, heavy then great economic loss can be caused, even occur ruining machine accident and casualties.If in the operational process of gear, people effectively can process by the potential potential faults of fault diagnosis technology Timeliness coverage, so not only effectively can ensure the safe operation of plant equipment, and can avoid the generation of major accident.Therefore, strengthen, to the research of initial failure of gear diagnosis and detection technology, there is important theory value and engineer applied value.
Gear case has complicated structure usually, and its fault-signal not only contains a large amount of noises, and usually has non-stationary and nonlinear characteristic.In this case, the mechanical failure diagnostic method such as traditional Time-domain Statistics parameter based on stationarity hypothesis and linear theory, correlation analysis, analysis of spectrum and temporal model is difficult to the requirement meeting Practical Project.In nearest decades, the Non-stationary Signal Analysis methods such as the decomposition of Short Time Fourier Transform, wigner-ville distribution, wavelet transformation, empirical mode decomposition, Cyclostationary analysis, local mean value and intrinsic time Scale Decomposition are widely used in mechanical fault feature extraction and diagnosis.But, said method be mainly used in solve fault signature obvious time mechanical fault diagnosis problem, do not relate to incipient fault detection and the diagnosis problem of gear.When gear distress is in commitment, because fault signature is very faint, the extraction of Fault characteristic parameters is very difficult, and therefore the incipient fault detection of gear and diagnosis are difficult problems.
Current Chinese scholars has carried out some research for this problem, in succession utilizes self-adapting random resonant method, Cyclostationary analysis method, wavelet transformation, signal Its Sparse Decomposition method and independent component analysis to carry out the Incipient Fault Diagnosis of rotating machinery.But said method lacks the analysis to rotating machinery fault dynamic development process generally, do not propose corresponding rotating machinery state estimation criterion, do not set up feasible state monitoring method.In view of the defect of prior art, be necessary to propose incipient fault detection and the diagnosis problem that a kind of new method solves gear.
Summary of the invention
The object of the invention is to the defect overcoming prior art, propose a kind of initial failure of gear detection method.The method, using the fluctuation parameters of gear distress data difference sequence as Fault characteristic parameters, sets up " the malfunction line " of gear, is then assessed the running status of gear case by the relative position of the state point and this " malfunction line " of investigating gear.To achieve these goals, technical scheme of the present invention is as follows.
First acceleration transducer is utilized to measure the vibration signal of gear under normal condition and various typical fault state
x(
n).
To original signal
x(
n) carry out calculus of differences, obtain original signal
x(
n) difference sequence △
x(
n).
△
x(
n)=
x(
n+1)-
x(
n),
n=1,…,
N-1。
Utilization goes trend fluction analysis (DFA) algorithm to calculate difference sequence △
x(
n) wave function
f(
s).
Extract logarithm wave function ln corresponding to often kind of malfunction [
f(
s)] minimum value and maximal value, using these two parameters as original signal
x(
n) characteristic parameter.
By logarithm wave function ln [
f(
s)] minimum value and maximal value map respectively as transverse and longitudinal coordinate.
Adopt least square method that the data point corresponding to malfunction is fitted to straight line, this straight line is called as " malfunction line ".
With certain confidence level (1-
α) estimate a fiducial interval for " malfunction line ".
Along with the deterioration of gear running status; data point is gradually near the fiducial interval of above-mentioned foundation; when data point enters into fiducial interval; can think that this gear has occurred the concurrent early warning of being out of order of fault with certain probability; at this moment to carry out labor to the running status of gear, need maintenance down if desired.
Core of the present invention is minimum value and the maximal value of the wave function logarithm value extracting gear distress data difference sequence, then the data point corresponding to malfunction is fitted to one " malfunction line ", then with certain confidence level (1-
α) estimate a fiducial interval for " malfunction line "; when gear running status runs down; data point corresponding to it can gradually near this fiducial interval; when data point enters this fiducial interval; can think that this gear has occurred the concurrent early warning of being out of order of fault with certain probability; at this moment to carry out labor to the running status of gear case, need maintenance down if desired.
Accompanying drawing explanation
Fig. 1 is process flow diagram of the present invention.
The analogous diagram that Fig. 2 is formed for " malfunction line ".
Fig. 3 is that example 1 four kinds of Gearbox vibration signal figure, (a) ~ (d) represent normally respectively, mild wear, moderate are worn and torn and broken teeth vibration signal.
Fig. 4 is example 1 gear condition observation process figure, and the amplitude that upper triangle, the left triangle of lower trigonometric sum represent added noise is respectively 0.2,0.5 and 0.8 times of normal state signal amplitude standard deviation.
Fig. 5 is that example 2 four kinds of Gearbox vibration signal figure, (a) ~ (d) represent normally respectively, slight cut, moderate cut and severe cut signal.
Fig. 6 is example 2 gear condition observation process figure, and the amplitude that upper triangle, the left triangle of lower trigonometric sum represent added noise is respectively 0.2,0.5 and 0.8 times of normal state signal amplitude standard deviation.
Embodiment
Gather the vibration signal of gear under various running status
x(
n).
To original signal
x(
n) carry out calculus of differences, obtain original signal
x(
n) difference sequence △
x(
n).
△
x(
n)=
x(
n+1)-
x(
n),
n=1,…,
N-1。
Utilization goes trend fluction analysis (DFA) algorithm to calculate difference sequence △
x(
n) wave function
f(
s).
Extract logarithm wave function ln corresponding to often kind of malfunction [
f(
s)] minimum value and maximal value, using these two parameters as original signal
x(
n) Fault characteristic parameters.
By logarithm wave function ln [
f(
s)] minimum value and maximal value map respectively as transverse and longitudinal coordinate.
Adopt least square method that the data point corresponding to malfunction is fitted to straight line, this straight line is called as " malfunction line ".
With certain confidence level (1-
α) estimate a fiducial interval for " malfunction line ".
Along with the deterioration of gear running status; data point is gradually near the fiducial interval of above-mentioned foundation; when data point enters into fiducial interval; can think that this gear has occurred the concurrent early warning of being out of order of fault with certain probability; at this moment to carry out labor to the running status of gear, need maintenance down if desired.
In order to prove the correctness of the method for the invention, provide a simulation example and two instantiations further illustrate.
simulation example
The evolution of gear distress simulated by the model building a Gearbox vibration signal.In general, gear distress signal comprises two parts substantially: a part is the quasi-periodic signal with amplitude modulationfrequency modulation characteristic, and another part is noise.Due to amplitude-modulation frequency-modulation signal always can be similar to write as several simpler quasi-periodic signal and form, therefore in order to study conveniently, use simple sinusoidal signal to simulate this part here.Like this, Gearbox vibration signal can be write as simply.
x n =sin[2π
f(
n-1)/
f s ]+
a×
stdvalue×
WN n ，
n=1,2,3,…,
N。
Wherein
wN n represent the white noise that amplitude is 1,
stdvaluerepresent the amplitude standard deviation of sinusoidal signal,
arepresent the amplitude coefficient of white noise,
nthe length of representative data, is arranged
f=100,
f s =1000,
n=10000.
By regulating the amplitude coefficient of white noise
asimulate the deteriorating course of gear running status.Analyze simulate signal according to the flow process shown in Fig. 1, result as shown in Figure 2.As can be seen from Figure 2, along with the signal to noise ratio (S/N ratio) of simulate signal is more and more less, the data point corresponding to running status forms straight line gradually, and running status gets over difference strong point the closer to this straight line.
case study on implementation 1
Utilize the gear distress data measured from gear case experiment table to verify the practicality of the method for the invention.The number of teeth of the active and passive gear of this gear case is respectively 24 and 29, and driving gear rotating speed is 1420RPM, and sample frequency is 16384Hz.This gear case simulates mild wear, moderate wearing and tearing and broken teeth fault respectively.Utilize method of the present invention to analyze the gear case vibration signal under normal condition and above-mentioned three kinds of malfunctions, result as shown in Figure 4.As can be seen from Figure 4, the data point corresponding to three kinds of malfunctions can approximate fits be one " malfunction line ", and the data point corresponding to normal condition then obviously departs from this straight line.In addition, because the running status of gear and signal to noise ratio (S/N ratio) have close relationship, in general, the running status of the lower then gear of signal to noise ratio (S/N ratio) is poorer, therefore can be simulated the deteriorating course of gear running status by the method for adding varying strength white noise to normal vibration data.As can be seen from Figure 4, along with the deterioration of gear running status, data point is close to " malfunction line " gradually, and the relative position therefore between data point and " malfunction line " can as the standard assessing gear running status.Region between dotted line shown in Fig. 4 is a confidence level of " malfunction line " is the fiducial interval of 95%; when data point enters into this interval; can think this gear case with 95% possibility there is fault; at this moment should send fault pre-alarming and labor is carried out to the running status of gear case, needing maintenance down if desired.
case study on implementation 2
Experiment gear case manufactures the gear distress of the different order of severity to simulate the evolutionary process of gear distress.Utilize the gear distress data measured to verify the practicality of the method for the invention.The number of teeth of the active and passive gear of this gear case is respectively 25 and 40, manufacturing fault on driving gear, and driving gear rotating speed is 1600RPM.This gear simulates slight cut, moderate cut and severe cut fault respectively.Utilize method of the present invention to analyze the Gearbox vibration signal under normal condition and above-mentioned three kinds of malfunctions, result as shown in Figure 6.As can be seen from Figure 6, the data point corresponding to three kinds of malfunctions can approximate fits be one " malfunction line ", and the data point corresponding to normal condition then obviously departs from this straight line.In addition, the method by adding varying strength white noise to normal vibration data simulates the deteriorating course of gear running status.As can be seen from Figure 6, along with the deterioration of gear running status, data point is close to " malfunction line " gradually, and the relative position therefore between data point and " malfunction line " can as the standard assessing gear running status.Region between dotted line shown in Fig. 6 is a confidence level of " malfunction line " is the fiducial interval of 95%; when data point enters into this interval; can think this gear case with 95% possibility there is fault; at this moment should send fault pre-alarming and labor is carried out to the running status of gear case, needing maintenance down if desired.
Claims (3)
1. an initial failure of gear detection method, is characterized in that, comprises the following steps:
(1) vibration signal of gear under various typical fault state is gathered
x(
n);
(2) to original signal
x(
n) carry out calculus of differences, obtain original signal
x(
n) difference sequence △
x(
n)
△
x(
n)=
x(
n+1)-
x(
n),
n=1,…,
N-1；
(3) trend fluction analysis (DFA) Algorithm Analysis difference sequence △ is removed in utilization
x(
n) wave function
f(
s);
(4) extract logarithm wave function ln corresponding to often kind of malfunction [
f(
s)] minimum value and maximal value, using these two parameters as original signal
x(
n) Fault characteristic parameters;
(5) by logarithm wave function ln [
f(
s)] minimum value and maximal value map respectively as transverse and longitudinal coordinate;
(6) adopt least square method that the data point corresponding to malfunction is fitted to straight line, this straight line is called as " malfunction line ";
(7) with certain confidence level (1-
α) estimate a fiducial interval for " malfunction line ";
(8) along with the deterioration of running state of gear box; data point is gradually near the fiducial interval of above-mentioned foundation; when data point enters into fiducial interval; can think that this gear case has occurred the concurrent early warning of being out of order of fault with certain probability; at this moment to carry out labor to the running status of gear case, need maintenance down if desired.
2. a kind of initial failure of gear detection method according to claim 1, described step (3) comprises the following steps:
1. time series is constructed
x(
i) average of going tire out difference sequence
x(
i):
，
；
2. difference sequence will be tired out
x(
i) be divided into
ksegment length is
snonoverlapping data segment, every segment data is designated as
x k (
i) (
k=1,2 ...,
k,
i=1,2 ...,
s);
3. adopt the polynomial trend of each segment data of least square method difference matching, the general trend finally obtained is designated as
t s (
i) (
i=1,2 ...,
n);
4. tired difference sequence is calculated
x(
i) mean square value wave function
5. time scale is changed
ssize, repeat above-mentioned steps 1. ~ 4., if time series
x(
i) there is time-length interrelation, then work as time scale
swhen changing within the scope of certain, wave function
f(
s) and time scale
smeet scaling law relation below
Parameter in above formula
αfor time series
x(
i) scaling exponent.
3. a kind of initial failure of gear detection method according to claim 1, described step (7) comprises the following steps: because the slope of fitting a straight line is approximately 1, therefore the position of fitting a straight line is only relevant with its intercept, therefore the fiducial interval of evaluation fitting straight line is just equivalent to the fiducial interval of evaluation fitting Linear intercept, uses symbol
brepresent the intercept of fitting a straight line, assuming that
bnormal Distribution
n(
μ,
σ 2), theoretical according to interval estimation, work as sample variance
σ 2time unknown, in order to sample estimates average
μfiducial interval, should use
tstatistic is estimated, namely
，
，
So, average
μa confidence level be (1-
α) fiducial interval be
。
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Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105588717A (en) * | 2015-12-10 | 2016-05-18 | 潍坊学院 | Gearbox fault diagnosis method |
| CN105759784A (en) * | 2016-02-04 | 2016-07-13 | 北京宇航系统工程研究所 | Fault diagnosis method based on data envelopment analysis |
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