CN114861726A - Method and system for predicting wear fault trend of high-speed gear of wind driven generator - Google Patents
Method and system for predicting wear fault trend of high-speed gear of wind driven generator Download PDFInfo
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Abstract
The invention relates to a method and a system for predicting wear failure trend of a high-speed gear of a wind driven generator, wherein the method comprises the following steps: collecting high-speed gear vibration signals of the wind driven generator, constructing a mixed information model, separating approximate components of each independent component information from the mixed signals by adopting an independent component analysis method, and finding useful components according to the characteristics of pure approximate fault source signals; estimating a similar shape amplification factor value range according to the fact that the approximate fault source signal and the fault source signal are similar to each other; determining the corresponding relation between the continuous and unidirectional variation magnification factor value range and the fault degree of the rotating part, establishing a fault degree judgment standard, and judging and predicting the fault degree and the fault tendency by combining a high-speed gear full life cycle fault source signal energy variation trend graph. The method can effectively predict the fault trend of the wind generating set, and can be applied to the technical field of mechanical equipment fault prediction.
Description
Technical Field
The invention relates to the technical field of mechanical equipment fault prediction, in particular to a wind driven generator high-speed gear wear fault trend prediction method and system.
Background
Most faults of large-scale rotating equipment are trend faults with time dependency and predictability, and development changes of the faults can be revealed by adopting a scientific and effective fault prediction method, so that malignant accidents of the equipment can be avoided.
Failure trend prediction is a key technology to avoid catastrophic events and to achieve modern predictive maintenance of electromechanical devices. The wind generating set works under variable working conditions all year round, and the fault characteristic quantity of the rotating component based on the energy form is often coupled with other noise information, so that the fault trend cannot be effectively predicted.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a method and a system for predicting a wear failure trend of a high-speed gear of a wind turbine generator, which can effectively predict a failure trend of the wind turbine generator.
In order to achieve the purpose, the invention adopts the following technical scheme: a wind driven generator high speed gear wear failure trend prediction method comprises the following steps: collecting high-speed gear vibration signals of the wind driven generator, constructing a mixed information model, separating approximate components of each independent component information from the mixed signals by adopting an independent component analysis method, and finding useful components according to the characteristics of pure approximate fault source signals; estimating a similar shape amplification factor value range according to the fact that the approximate fault source signal and the fault source signal are similar to each other; determining the corresponding relation between the continuous and unidirectional variation magnification factor value range and the fault degree of the rotating part, establishing a fault degree judgment standard, and judging and predicting the fault degree and the fault tendency by combining a high-speed gear full life cycle fault source signal energy variation trend graph.
Further, the mixed information model is:
x＝As+ε
wherein x is a vibration signal of the high-speed gear of the wind driven generator; a is an element of R M×L As a mixing matrix, M<L, R represents a real number matrix, M represents the column number of the real number matrix, and L represents the row number of the real number matrix; s is formed by R L Representing source signals, and all the source signals are independent of each other; ε represents additive noise.
Further, the finding of useful components based on the pure approximate fault source signal characteristics comprises:
the independent component analysis result contains a useful characteristic signal which is an approximate signal of the fault source signal;
and (3) detecting the statistical characteristic quantity of the separated pure approximate fault source signals one by one, verifying the statistical characteristic quantity by using priori knowledge, and obtaining the required useful components when the waveform amplitude of the fault source signals is not zero and the waveform mutation frequency is positively correlated with the gear frequency conversion.
Further, the pure approximate fault source signal is a similar form of the fault source signal, and the fault source signal can be obtained by amplifying or reducing the pure approximate fault source signal by a preset multiple theta.
Further, estimating a similar form amplification factor value range according to the fact that the approximate fault source signal and the fault source signal are similar to each other, includes:
calculating the energy of the fault source signal according to the multiple theta relation between the fault source signal vector and the pure approximate fault source signal vector;
and carrying out normal overall mean value interval estimation on the similarity factor theta to obtain a mean value confidence interval of the factor theta, and obtaining an energy interval of the fault source signal according to the confidence interval and the energy of the fault source signal.
Further, the determining the corresponding relationship between the continuous and unidirectional variation amplification factor value range and the fault degree of the rotating component and establishing the fault degree discrimination standard comprises:
the wear failure degree of the high-speed gear and the amplification factor value range have a corresponding relation, and as the failure degree deepens, the end point of the amplification factor value range also becomes larger;
arranging the end points of a series of amplification factor value ranges obtained through calculation in the order from small to large to form the judgment standard, and judging the wear fault degree of the high-speed gear;
the larger the energy of the fault source signal is, the deeper the fault degree of the high-speed gear of the wind driven generator is.
Further, said height isThe change trend graph of the fault source signal energy in the full life cycle of the speed gear is a change graph of the fault degree along with time, and comprises the following steps: according to the relation between the fault source signal energy and the multiples theta of the fault source signal vector and the pure approximate fault source signal vector, the amplification factor theta, the time t and the fault source signal energy Q z In the formed three-dimensional space, a series of spatial coordinate points (Q) are obtained by utilizing the calculation of the full life cycle wear failure inspection data of the high-speed gear Z Theta, t) are fitted into a fault source signal energy change curve, and the fault source signal energy change curve is used as a curve close to the real fault source signal energy change curve and used for predicting the development trend of the high-speed gear wear fault.
A wind turbine high speed gear wear failure trend prediction system, comprising: the signal acquisition module is used for acquiring a high-speed gear vibration signal of the wind driven generator, constructing a mixed information model, separating approximate components of each independent component information from the mixed signal by adopting an independent component analysis method, and finding useful components according to the characteristics of a pure approximate fault source signal; the estimation module estimates a similar shape amplification factor value range according to the fact that the approximate fault source signal and the fault source signal are similar to each other; and the prediction output module is used for determining the corresponding relation between the continuous and unidirectional-change amplification factor value range and the fault degree of the rotating part, establishing a fault degree judgment standard, and judging and predicting the fault degree and the fault tendency by combining a high-speed gear full life cycle fault source signal energy change trend graph.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the above methods.
A computing device, comprising: one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the above-described methods.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. the method comprises the steps of constructing a mixed information model, separating approximate component information of each piece of independent component information from a mixed signal by using an independent component analysis method, and finding useful components according to the characteristics of a pure approximate fault source signal; estimating a similarity amplification factor value range by using a normal overall mean value interval estimation method according to the similarity between the approximate fault source signal and the fault source signal; determining the corresponding relation between the continuous and unidirectional variation magnification factor value range and the fault degree of the rotating part, establishing a fault degree judgment standard, and judging and predicting the fault degree and the fault tendency by combining a high-speed gear full life cycle fault source signal energy variation trend graph.
2. The method has ideal trend prediction effect for processing the vibration signals characterized by the periodic mutation, and has engineering application value for ensuring the continuous operation of key equipment and implementing scientific maintenance.
Drawings
FIG. 1 is a schematic flow chart of a method for predicting a wear failure trend of a high-speed gear of a wind turbine generator according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating the influence of phase difference according to an embodiment of the present invention;
FIG. 3a is a diagram illustrating the analysis result of the independent components of the mixed signal according to an embodiment of the present invention;
FIG. 3b is a diagram illustrating the analysis of the independent components of the approximate fault source signal according to an embodiment of the present invention;
FIG. 4a is a schematic diagram of a mixed signal according to an embodiment of the present invention;
FIG. 4b is a schematic diagram of a near fault source signal in one embodiment of the present invention;
FIG. 5 is a graph illustrating the variation of the signal energy of the full-life-cycle fault source of the high-speed gear according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The invention provides a method and a system for predicting the wear fault trend of a high-speed gear of a wind driven generator.A mixed information model is constructed by taking the wear fault of the high-speed gear of the wind driven generator as a research object, approximate component information of each piece of independent component information is separated from a mixed signal by using an independent component analysis method, and useful components are found by taking the characteristics of a pure approximate fault source signal as a basis; estimating a similarity amplification factor value range by using a normal overall mean value interval estimation method according to the similarity between the approximate fault source signal and the fault source signal; determining the corresponding relation between the continuous and unidirectional variation amplification factor value range and the fault degree of the rotating part, establishing a fault degree judging standard, and judging and predicting the fault degree and the trend by combining a fault source signal energy variation trend chart of the full life cycle of the high-speed gear. The method is adopted to process the high-speed gear wear fault data acquired in an industrial field, has a relatively ideal trend prediction effect on processing wear fault signals characterized by periodic mutation, and has an engineering application value for ensuring continuous operation of key equipment and implementing scientific maintenance.
In one embodiment of the invention, a method for predicting the trend of the abrasion fault of the high-speed gear of the wind driven generator is provided. In this embodiment, as shown in fig. 1, the method includes the following steps:
1) collecting high-speed gear vibration signals of the wind driven generator, constructing a mixed information model, separating approximate components of each independent component information from the mixed signals by adopting an independent component analysis method, and finding useful components according to the characteristics of pure approximate fault source signals;
2) estimating a similar shape amplification factor value range according to the fact that the approximate fault source signal and the fault source signal are similar to each other;
3) determining the corresponding relation between the continuous and unidirectional variation magnification factor value range and the fault degree of the rotating part, establishing a fault degree judgment standard, and judging and predicting the fault degree and the fault tendency by combining a high-speed gear full life cycle fault source signal energy variation trend graph.
In the step 1), in the process of state monitoring and trend prediction, the vibration signal x collected by the sensor belongs to R M Inevitably disturbed by noise epsilon. Therefore, the mixed information model is:
x＝As+ε (1)
wherein x is a vibration signal of the high-speed gear of the wind driven generator; a is an element of R M×L As a mixing matrix, M<L, R represents a real number matrix, M represents the column number of the real number matrix, and L represents the row number of the real number matrix; s is formed by R L Representing source signals, and all the source signals are independent of each other; ε represents additive noise, modeled with a Gaussian distribution of zero mean and covariance matrix ∑.
In the step 1), finding useful components based on the characteristics of the pure approximate fault source signal includes the following steps:
1.1) the independent component analysis result contains a useful characteristic signal which is an approximate signal of a fault source signal;
the method specifically comprises the following steps:
if vibration signals emitted by a plurality of vibration sources which are completely independent from each other exist, acquiring the signals by adopting n channels, thereby obtaining a group of data:
where i represents a time series.
Then the mathematical relationship between S and X is:
X (i) ＝AS (i) or
Wherein j is 1,2 … n, X (i) For a known signal matrix, matrix A represents an unknown mixing matrix, X (i) Each component of
All can be composed of S i Component (b) of
And (4) linear representation.
The sampled signal needs to be de-centered and bleached before blind source separation can be performed. Wherein the decentralization is: the signal vector X ' is centered, i.e. X ' -E (X ') -X ", where E (X ') represents the mathematical expectation of X '.
Whitening comprises the following steps:
(1) solving the covariance matrix C of the vector X ″ x In which C is x ＝E[X″(X″) T ]；
(2) According to a covariance matrix C x To obtain a covariance matrix C x The unit norm eigenvector of (a) is a matrix of columns:
F＝(e 1 Λe n ) (2)
wherein e is j Is a covariance matrix C x The unit norm eigenvector of (a);
(3) according to the covariance matrix C x To obtain a covariance matrix C x Is a diagonal matrix D of diagonal elements:
D＝diag(d 1 Λd j ) (3)
wherein d is j Is a covariance matrix C x A characteristic value of (d);
(4) substituting the matrix F and the diagonal matrix D in the step (2) and the step (3) into a whitening formula:
get used for solving
Vector of (2)
Maximum likelihood estimation of ICA:
let W be A -1 And then:
and W can be represented as:
wherein, w j ∈R n And then obtaining:
let F X (x) As a function of the probability of the argument x, then:
F X (x)＝P(X≤x)＝P(AS≤x)＝P(S≤W x )＝F S (Wx) (6)
wherein, F S (Wx) is a probability function of an argument s, and P is additionally provided X (x) As a function of the probability density of the argument x, then:
P X (x)＝F′ X (x)＝F′ S (Wx)＝P S (Wx)|W| (7)
wherein, P S (Wx) is the probability density function of the argument s.
By
Is independent of each other to obtain S (i) The joint probability density function of (a) is:
combining (7) formula to obtain sampling signal
The probability density of (a) is:
assume that the probability function for the argument s is:
obtaining after derivation:
p S (s) is the probability density function for s, and s ∈ R.
Due to p S (s) is an even function, so the mean value of s, E(s), is 0, then E (x), E (a) s ) 0, i.e. the mean value of x is also 0. Now the known samples:
combining the equation (7), the log-likelihood estimate of the sample is obtained as:
since the derivation formula of the matrix W is: delta W |W|＝|W|(W -1 ) T And [ logp ] S (s)]′＝1-2F S (s), then, the derivation is made across the equal sign of equation (11):
where η is the gradient rise rate, specified artificially.
When the W is obtained by iteration, it can be obtained
To recover an approximate source signal.
1.2) the ICA result contains a useful characteristic signal, and in the fault detection process of the rotating machinery, the useful characteristic signal is actually an approximate signal of a fault source signal and has the essential characteristic of the fault source signal, so the useful signal can be used for analyzing and acquiring the characteristic information of a fault component. However, the ICA result ordering is uncertain, so the statistical characteristic quantity of the separated pure approximate fault source signals is detected one by one, the statistical characteristic quantity is verified by using the prior knowledge, and when the waveform amplitude of the fault source signals is not zero and the waveform mutation frequency is positively correlated with the gear frequency conversion, the signals are required useful components.
In an ideal state, when a gear fault part does not participate in meshing, the waveform amplitude of a fault source signal is 0, when the fault part participates in meshing, the waveform amplitude of the fault source signal is delta (delta is not equal to 0), and the waveform mutation frequency is positively correlated with the gear frequency conversion. Therefore, when searching for a useful component, the component closest to this ideal state can be regarded as the useful component.
The pure approximate fault source signal is similar to the fault source signal, and the fault source signal can be obtained by amplifying or reducing the pure approximate fault source signal by a preset multiple theta.
In this embodiment, the statistical characteristic quantity may be a graph characteristic and a numerical characteristic of the waveform, and the characteristic graph includes a time domain graph, a frequency domain graph, a trend graph, and the like, and the characteristic numerical value includes a peak value, a mean value, a variance, a period, and the like.
In the step 2), estimating the similar form amplification factor value range according to the similar form of the approximate fault source signal and the fault source signal, which comprises the following steps:
2.1) calculating the energy of the fault source signal according to the multiple theta relation between the fault source signal vector and the pure approximate fault source signal vector;
in this embodiment, the source of the failure is the signalEnergy Q z The calculation method comprises the following steps:
the energy Q of a segment of signal can be represented by the elements a of the signal vector i (where i is 1,2 … r, r denotes the number of truncated samples) is expressed as:
because the approximate source signal with fixed length only has unit energy and the sign is not definite, the energy of each independent component can not be obtained by calculating the variance of the separated approximate fault source signal, but the pure approximate fault source signal is the similar shape of the fault source signal, the former can obtain the latter by amplifying or reducing the former by a certain multiple theta, and if A is set i And a i The points corresponding to the fault source signal vector and the approximate fault source signal vector respectively have the following values:
A i ＝θa i or
And is
The known approximate fault source signal energy Q j The fault source signal energy taken from equation (1) together with equation (14) to equation (13) is:
Q z ＝θ 2 (15)
as can be seen from the above formula, Q z Independent of the sign of the approximate source signal, at Q j Within the constraints of ≡ 1, Q z The value is affected by the r value, so for uniform dimension, the r value needs to be fixed, and the signal vector elements of 5-10 periods are generally taken.
2.2) carrying out normal overall mean value interval estimation on the similarity factor theta to obtain a mean value confidence interval of the factor theta, and obtaining an energy interval of the fault source signal according to the confidence interval and the energy of the fault source signal.
In this embodiment, if the default fault is not degraded in a very short period of time, θ is a fixed valueHowever, under the influence of noise, the value of θ fluctuates around the true value, and the sample value Y thereof approximately follows the variance σ 2 Unknown gaussian distributions, namely: y to N (mu, sigma) 2 ) The value range of the mean value mu can be estimated by applying the interval estimation method of the normal overall mean value, and the method comprises the following steps:
assuming that theta belongs to phi (phi represents the value range of theta), 2m fault characteristic points can be found in m periods, and a sample with the capacity of 2m of the parameter theta is obtained by comparing the amplitude of the fault characteristic points of the mixed signal and the approximate fault source signal: y ═ Y (Y) 1 ，y 2 …y 2m ) For a given value of α (0)<α<1) If from the sample Y from Y 1 ，y 2 …y 2m Two statistics determined
And
for any θ ∈ Φ:
because of the mean value of the sample Y
Is an unbiased estimate of the mean μ of θ, and has:
where σ represents the standard deviation of the parameter θ. Taking into account the variance S of the sample Y in the case of unknown σ 2 Is the variance σ of θ 2 Then:
distribution t (2 m)-1) independent of any unknown parameters, use
The pivot quantity can be:
namely:
thus, a confidence interval with a confidence level of 1- α for μ is:
and substituting (15) the two end points of the confidence interval to obtain the energy interval of the fault source signal.
In the above step, the phase difference (as shown in fig. 2, if the rotation speed of the rotating component is not changed, it is assumed that sampling points of three periods (an "X" point or an "O" point) are arbitrarily intercepted twice for a section of sampling signal, wherein one section or two sections of data are translated on the time axis, so that the two sections of data are overlapped to the maximum extent, thereby generating the phase difference between AB (or CD)) to the energy Q of the fault source signal z The values have the following effects:
continuous sampling pair Q with constant rotating speed z The magnitude of the value has no effect. If the rotating speed of the rotating component is not changed, assuming that sampling points (the 'X' point or the 'O' point) of three periods between the AC and the BD are intercepted twice in sequence for a segment of sampling signals, as shown in FIG. 2, so that a phase difference between the AB (or the CD) is generated, the 'X' point (or the 'O' point) between the phase difference AB can be exactly translated between the CDs when viewed from an image, the sampling of the AC segment becomes the sampling of the BD segment, and the size of Q and the vector element a are known from the formula (13) i Is independent of the order of arrangement of the two, so that the rotation speed is constant for successive sampling pairs Q z Large value ofSmall and will not affect.
The real influence of the phase difference when the rotation speed is not changed is generated by interleaving the BD segment sampling points and the AC segment sampling points intercepted from the discontinuous sampling signals (as shown in fig. 2, the interleaving of the "X" point and the "O" point), if the phase cannot be calibrated, the error caused by the phase offset can be reduced as much as possible only by setting a higher sampling frequency and reducing the interval between adjacent sampling points so that the "O" point in fig. 2 becomes an approximate point of the "X" point.
In the step 3), determining the corresponding relation between the continuous and unidirectional-change magnification factor value range and the fault degree of the rotating part, and establishing a fault degree judgment standard; the discrimination standard is a discrimination basis for evaluating the wear degree of the high-speed gear, the approximate fault source signal and the fault source signal are similar to each other, and a maximum likelihood estimation algorithm is used for calculating a similar amplification factor value range. The method specifically comprises the following steps:
the wear failure degree of the high-speed gear and the amplification factor value range of the similar shape have a corresponding relation, and as the failure degree deepens, the end point of the amplification factor value range (interval) also becomes larger;
arranging the end points of a series of amplification factor value ranges (intervals) obtained by calculation in the order from small to large to form a judgment standard, and accurately judging the wear failure degree of the high-speed gear; the larger the energy of the fault source signal is, the deeper the fault degree of the high-speed gear of the wind driven generator is.
In the step 3), the change trend chart of the signal energy of the fault source in the full life cycle of the high-speed gear is a change chart of the fault degree along with time, and the change chart is as follows: according to the relation between the fault source signal energy and the multiples theta of the fault source signal vector and the pure approximate fault source signal vector, the amplification factor theta, the time t and the fault source signal energy Q z In the formed three-dimensional space, a series of spatial coordinate points (Q) are obtained by utilizing the calculation of the full life cycle wear failure inspection data of the high-speed gear Z Theta, t) are fitted into a fault source signal energy change curve (shown in figure 5) which is close to the real fault source signal energy change curve and is used for carrying out the development trend of the high-speed gear wear faultAnd (6) predicting.
For example, when the high-speed gear of another wind turbine generator system has a wear failure again, the amplification factor θ can be calculated and obtained, and the degree of failure of the high-speed gear, the degradation trend of the next failure over time, and the remaining service life can be determined by combining the space curve shown in fig. 5.
Example (b):
the method of the present invention is validated and further described based on field data.
1) Acquisition of approximate fault source signals
And processing the state monitoring data of the high-speed gear of the wind driven generator. In the sampling process, three vibration sensors are used for carrying out state monitoring on the high-speed end of the gearbox. Some technical parameters are listed as follows: the model of the gear box: PPSC 1290; the transmission ratio is as follows: 104.125, respectively; input revolution number: 17.4 r/min; output revolution number: 1810 r/min; sampling frequency: 20.48 kHz; single sampling time: for 1 min.
Deducing the number r of sampling points in each period according to the above parameters T 679, let r in formula (13) be 5120, and perform independent component analysis to obtain independent component analysis results as shown in fig. 3a and 3 b. The periodic sudden changes present in the map are known to be caused by early wear failure of the high speed gears.
In an ideal state, when a gear fault part does not participate in meshing, the waveform amplitude of a fault source signal is 0, when the fault part participates in meshing, the waveform amplitude of the fault source signal is delta (delta is not equal to 0), and the waveform mutation frequency is positively correlated with the gear frequency conversion. Observing the three graphs in fig. 3b, only the first graph is closest to the ideal state, so the first graph is determined to be approximate fault source signals, and the second graph is determined to be fault sensitive mixed signals because the fault characteristics of the second graph in fig. 3a are obvious, so that the mixed signals and the approximate fault source signals are obtained, as shown in fig. 4a and fig. 4 b.
2) Given that m is 7, 14 abrupt points can be found in fig. 4a and 4b, and table 1 is made according to the positions and amplitudes of the two sets of fault feature points:
TABLE 1 Fault feature Point location and amplitude
Wherein A is 1 Representing the approximate fault source signal amplitude, A 2 Representing the mixed signal amplitude.
As can be seen from Table 1, the distance between two adjacent wave crests (wave troughs) is 685-688 sampling points, which is basically consistent with the previously estimated 679, and also indicates that the fluctuation amplitude of the high-speed gear is not large. The method according to the invention obtains a set of 14 samples of the parameter θ: y (62.8133, 65.6947, 64.5356, 64.8766, 61.4753, 64.2725, 65.6999, 67.6925, 65.2599, 66.0972, 62.2220, 67.6429, 62.1025, 64.7434), then the sample mean value
Standard deviation S is 1.9400, the confidence level of the sample is set to 0.95, α is 0.05, and there is t 0.025 (13) 2.1604, these parameter values are substituted into equation (21), the confidence interval of the mean value μ of the parameter θ at the confidence level of 0.95 is obtained as (63.5319, 65.7721), and the two end points of the interval are substituted into equation (15), and the energy interval of the fault source signal is obtained as (4036.3023, 4325.9691).
With the time of data acquisition as a reference, the same method is applied to process data acquired under the same working condition before 5 days, after 5 days and after 10 days, so as to obtain energy intervals of other three fault source signals, and the energy intervals are arranged according to time sequence to obtain a table 2:
TABLE 2 Fault deterioration evaluation Table
Table 2 shows that the degree of failure of the high-speed gear is deepened and the energy of the failure source signal is gradually increased as time goes by.
The upper table is only a fragment of the whole fault process of the high-speed gear, in order to perfect the upper table, the data in the early stage and the later stage of the fault are continuously collected, the data collection density is increased, the upper table forms a continuous fault degradation interval table of the whole fault process of the high-speed gear, and when a new similar shape amplification factor mean value is obtained, the fault degree of the high-speed gear can be known according to the fault interval where the 2-time power of the mean value is located.
3) Analysis of energy variation trend of fault source signal
For the study of the failure trend prediction, the degree of failure is studied first, and then the change rate of the failure degree along with the time is explored. Q is represented by the formula (15) z The change of theta is closely related to time, so that the three-dimensional space (magnification theta, time T and energy Q) can be realized z ) A fault source signal energy change curve is plotted as shown in fig. 5.
As shown in FIG. 5, 0 to t 1 The time period represents the normal use period of the high-speed gear, and no energy change curve exists in the time period; from t 1 At the moment, the high-speed gear is in failure, and then an energy change curve with the O point as a starting point begins to appear, but the O point is not equal to the point (0, t) 1 0) coincide but approach infinity to this point because when the high speed gear is operating without a fault, both the fault source signal and the approximate fault source signal are absent and therefore both energies are 0, then the meaningless equation occurs: θ is 0/0, so 0 to t 1 No fault source signal energy change curve exists at any moment; when a fault just occurs, because the fluctuation of a fault source signal is weak, the waveform approaches to a straight line, and the signal energy of an approximate fault source signal is 1 due to a signal preprocessing algorithm, the situation that theta approaches to 0 infinitely appears at the moment, so that the point O is an infinite approaching point (0, t is a point with a constant value) 1 0); when the fault is more severe to the end of the fault component lifecycle z A maximum occurs at the same time as theta, so that the energy interval of the full life cycle of the high-speed gear is (0, tau), wherein 0<τ<∞。
Under the existing conditions, the sample mean value mu can be used for replacing theta, so that a curve close to a real fault source signal energy change curve is fitted by calculating and analyzing state monitoring data of the whole life cycle of the high-speed gear, and the method is very favorable for predicting the development trend of the wear fault of the high-speed gear.
In conclusion, the invention establishes a high-speed gear wear fault evaluation system, provides a high-speed gear wear fault trend prediction method based on independent component analysis, and verifies the effectiveness of the method through industrial field data.
In one embodiment of the present invention, a wind turbine high speed gear wear failure trend prediction system is provided, which includes:
the signal acquisition module is used for acquiring a high-speed gear vibration signal of the wind driven generator, constructing a mixed information model, separating approximate components of each independent component information from the mixed signal by adopting an independent component analysis method, and finding useful components according to the characteristics of a pure approximate fault source signal;
the estimation module estimates a similar shape amplification factor value range according to the fact that the approximate fault source signal and the fault source signal are similar to each other;
and the prediction output module is used for determining the corresponding relation between the continuous and unidirectional-change amplification factor value range and the fault degree of the rotating part, establishing a fault degree judgment standard, and judging and predicting the fault degree and the fault tendency by combining a high-speed gear full life cycle fault source signal energy change trend graph.
The system provided in this embodiment is used for executing the above method embodiments, and for details of the process and the details, reference is made to the above embodiments, which are not described herein again.
In the computing device structure provided in an embodiment of the present invention, the computing device may be a terminal, and the computing device may include: a processor (processor), a communication Interface (communication Interface), a memory (memory), a display screen and an input device. The processor, the communication interface and the memory are communicated with each other through a communication bus. The processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium, an internal memory, the non-volatile storage medium storing an operating system and a computer program that when executed by the processor implements a predictive method; the internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a manager network, NFC (near field communication) or other technologies. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the computing equipment, an external keyboard, a touch pad or a mouse and the like. The processor may call logic instructions in memory to perform the following method: collecting high-speed gear vibration signals of the wind driven generator, constructing a mixed information model, separating approximate components of each independent component information from the mixed signals by adopting an independent component analysis method, and finding useful components according to the characteristics of pure approximate fault source signals; estimating a similarity amplification factor value range according to the similarity between the approximate fault source signal and the fault source signal; determining the corresponding relation between the continuous and unidirectional variation magnification factor value range and the fault degree of the rotating part, establishing a fault degree judgment standard, and judging and predicting the fault degree and the fault tendency by combining a high-speed gear full life cycle fault source signal energy variation trend graph.
In addition, the logic instructions in the memory may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that the above-described configurations of computing devices are merely some of the configurations associated with the present application and do not constitute limitations on the computing devices to which the present application may be applied, as a particular computing device may include more or fewer components, or some components in combination, or have a different arrangement of components.
In one embodiment of the invention, a computer program product is provided, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions that, when executed by a computer, enable the computer to perform the methods provided by the above-described method embodiments, for example, comprising: collecting high-speed gear vibration signals of the wind driven generator, constructing a mixed information model, separating approximate components of each independent component information from the mixed signals by adopting an independent component analysis method, and finding useful components according to the characteristics of pure approximate fault source signals; estimating a similar shape amplification factor value range according to the fact that the approximate fault source signal and the fault source signal are similar to each other; determining the corresponding relation between the continuous and unidirectional variation magnification factor value range and the fault degree of the rotating part, establishing a fault degree judgment standard, and judging and predicting the fault degree and the fault tendency by combining a high-speed gear full life cycle fault source signal energy variation trend graph.
In one embodiment of the invention, a non-transitory computer-readable storage medium is provided, which stores server instructions that cause a computer to perform the methods provided by the above embodiments, for example, including: collecting high-speed gear vibration signals of the wind driven generator, constructing a mixed information model, separating approximate components of each independent component information from the mixed signals by adopting an independent component analysis method, and finding useful components according to the characteristics of pure approximate fault source signals; estimating a similarity amplification factor value range according to the similarity between the approximate fault source signal and the fault source signal; determining the corresponding relation between the continuous and unidirectional variation magnification factor value range and the fault degree of the rotating part, establishing a fault degree judgment standard, and judging and predicting the fault degree and the fault tendency by combining a high-speed gear full life cycle fault source signal energy variation trend graph.
The implementation principle and technical effect of the computer-readable storage medium provided by the above embodiments are similar to those of the above method embodiments, and are not described herein again.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for predicting the wear failure trend of a high-speed gear of a wind driven generator is characterized by comprising the following steps:
collecting high-speed gear vibration signals of the wind driven generator, constructing a mixed information model, separating approximate components of each independent component information from the mixed signals by adopting an independent component analysis method, and finding useful components according to the characteristics of pure approximate fault source signals;
estimating a similar shape amplification factor value range according to the fact that the approximate fault source signal and the fault source signal are similar to each other;
determining the corresponding relation between the continuous and unidirectional variation magnification factor value range and the fault degree of the rotating part, establishing a fault degree judgment standard, and judging and predicting the fault degree and the fault tendency by combining a high-speed gear full life cycle fault source signal energy variation trend graph.
2. The wind turbine high-speed gear wear failure trend prediction method according to claim 1, wherein the mixed information model is as follows:
x＝As+ε
wherein x is a vibration signal of the high-speed gear of the wind driven generator; a is an element of R M×L As a mixing matrix, M<L, R represents a real number matrix, M represents the column number of the real number matrix, and L represents the row number of the real number matrix; s is formed by R L Representing source signals, and all the source signals are independent of each other; epsilon represents additive noise.
3. The method for predicting the wear failure trend of the high-speed gear of the wind driven generator as claimed in claim 1, wherein the finding of the useful component based on the characteristics of the pure approximate failure source signal comprises the following steps:
the independent component analysis result contains a useful characteristic signal which is an approximate signal of the fault source signal;
and (3) detecting the statistical characteristic quantity of the separated pure approximate fault source signals one by one, verifying the statistical characteristic quantity by using priori knowledge, and obtaining the required useful components when the waveform amplitude of the fault source signals is not zero and the waveform mutation frequency is positively correlated with the gear frequency conversion.
4. The method for predicting the wear failure trend of the high-speed gear of the wind driven generator as claimed in claim 3, wherein the pure approximate failure source signal is a similar form of the failure source signal, and the failure source signal can be obtained by amplifying or reducing the pure approximate failure source signal by a preset multiple θ.
5. The method for predicting the wear failure trend of the high-speed gear of the wind driven generator according to claim 1, wherein the estimating the similarity amplification value range according to the similarity between the approximate failure source signal and the failure source signal comprises the following steps:
calculating the energy of the fault source signal according to the multiple theta relation between the fault source signal vector and the pure approximate fault source signal vector;
and carrying out normal overall mean value interval estimation on the similarity shape multiple theta to obtain a mean value confidence interval of the multiple theta, and obtaining an energy interval of the fault source signal according to the confidence interval and the fault source signal energy.
6. The method for predicting the wear failure trend of the high-speed gear of the wind driven generator according to claim 5, wherein the step of determining the correspondence between the continuously and unidirectionally changing magnification factor value range and the failure degree of the rotating component and establishing the failure degree judgment standard comprises the following steps:
the wear failure degree of the high-speed gear and the amplification factor value range have a corresponding relation, and as the failure degree deepens, the end point of the amplification factor value range also becomes larger;
arranging the end points of a series of amplification factor value ranges obtained through calculation in the order from small to large to form the judgment standard, and judging the wear fault degree of the high-speed gear;
the larger the energy of the fault source signal is, the deeper the fault degree of the high-speed gear of the wind driven generator is.
7. The method for predicting the wear failure trend of the high-speed gear of the wind driven generator according to claim 1, wherein the high-speed gear full life cycle failure source signal energy change trend graph is a failure degree change graph with time, and the method comprises the following steps:
according to the relation between the fault source signal energy and the multiples theta of the fault source signal vector and the pure approximate fault source signal vector, the amplification factor theta, the time t and the fault source signal energy Q z In the formed three-dimensional space, a series of spatial coordinate points (Q) are obtained by utilizing the calculation of the full life cycle wear failure inspection data of the high-speed gear Z Theta, t) are fitted into a fault source signal energy change curve, and the fault source signal energy change curve is used as a curve close to the real fault source signal energy change curve and used for predicting the development trend of the high-speed gear wear fault.
8. A wind driven generator high speed gear wear failure trend prediction system, comprising:
the signal acquisition module is used for acquiring a high-speed gear vibration signal of the wind driven generator, constructing a mixed information model, separating approximate components of each independent component information from the mixed signal by adopting an independent component analysis method, and finding useful components according to the characteristics of a pure approximate fault source signal;
the estimation module estimates a similar shape amplification factor value range according to the fact that the approximate fault source signal and the fault source signal are similar to each other;
and the prediction output module is used for determining the corresponding relation between the continuous and unidirectional-change amplification factor value range and the fault degree of the rotating part, establishing a fault degree judgment standard, and judging and predicting the fault degree and the fault tendency by combining a high-speed gear full life cycle fault source signal energy change trend graph.
9. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-7.
10. A computing device, comprising: one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-7.
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| CN117874471B (en) * | 2024-03-11 | 2024-05-14 | 四川能投云电科技有限公司 | Water and electricity safety early warning and fault diagnosis method and system |
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