CN110398364B - Planetary gearbox fault diagnosis method based on resonance sparse decomposition and FastICA algorithm - Google Patents

Planetary gearbox fault diagnosis method based on resonance sparse decomposition and FastICA algorithm Download PDF

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CN110398364B
CN110398364B CN201910604394.4A CN201910604394A CN110398364B CN 110398364 B CN110398364 B CN 110398364B CN 201910604394 A CN201910604394 A CN 201910604394A CN 110398364 B CN110398364 B CN 110398364B
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邓艾东
朱静
邓敏强
孙文卿
程强
刘洋
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Southeast University
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Abstract

The invention discloses a planetary gearbox fault diagnosis method based on resonance sparse decomposition and FastICA algorithm, which comprises the following steps: 1) firstly, decomposing a vibration signal into a high resonance component and a low resonance component by using a resonance sparse decomposition method, and removing the low resonance component containing a broadband signal; 2) taking the high resonance component containing the planetary gear box as an observation signal, and then carrying out primary resonance sparse decomposition on the observation signal to form a virtual channel signal; 3) and processing the observation signal and the virtual channel signal by using a rapid independent component analysis algorithm, and separating effective fault characteristic components so as to identify the fault type. The method can effectively extract the fault characteristic frequency of the planetary gear box, solve the problems of fault information loss and modal aliasing in the EMD denoising process, solve the problem of inaccurate decomposition caused by the difference of the number of source signals and the number of observation signals in the ICA, and accurately and clearly extract the fault characteristic frequency of the planetary gear box.

Description

Planetary gearbox fault diagnosis method based on resonance sparse decomposition and FastICA algorithm
Technical Field
The invention belongs to the field of fault diagnosis of rotating machinery, and particularly relates to a fault diagnosis method of a planetary gearbox based on resonance sparse decomposition and FastICA algorithm.
Background
The planetary gear box is widely applied to various transmission systems of aviation, engineering machinery, wind power generation and the like, the working environment is complex, alternating load is borne on the surface, fault diagnosis and monitoring are carried out on the planetary gear box, and the planetary gear box has important significance for ensuring normal work and safe operation of a mechanical system.
In practical engineering application, a vibration signal of the planetary gearbox is coupling of multiple excitation factors, the frequency components of the vibration signal are very complex, and the vibration signal not only contains the rotation frequency of each part, the meshing frequency and the frequency multiplication of the gear pair, but also contains the natural frequency of excited equipment; the planet gears are meshed with the sun gear and other planet gears, and the meshing mode causes certain characteristic frequencies to be low; the relative position of the planet wheel and the sensor changes along with the operation, the vibration transmission path also changes continuously, the amplitude or frequency modulation of signals can be caused by installation and manufacturing errors, the passing effect of the planet wheel and the like, so that the side frequency band is complicated, in addition, the influence of environmental noise is caused, the fault frequency is easily submerged, and great difficulty is brought to the vibration analysis.
Aiming at the characteristics of strong background noise, nonlinearity and non-stability of the fault signal of the planetary gear box, the method for extracting the fault characteristic by utilizing time-frequency analysis is widely developed. A method combining order ratio analysis and ICA successfully extracts fault characteristic information of a rotor. However, the ICA method requires that the number of sensor channels is not less than the number of independent vibration sources, and in practical engineering application, the actual number of vibration sources is far larger than the number of sensor channels due to convolution mixing of signals of the vibration sources, serious installation errors of experimental equipment, serious background noise interference and the like, so that ICA separation fails.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems that ICA separation fails and the like caused by experimental equipment installation errors, serious background noise interference and the like in the prior art, the invention provides a planetary gearbox fault diagnosis method based on resonance sparse decomposition and FastICA algorithm.
The technical scheme is as follows: the invention provides a fault diagnosis method for a planetary gearbox based on resonance sparse decomposition and FastICA algorithm, which comprises the following steps:
step 1: obtaining a vibration signal of the planetary gearbox by using a vibration sensor;
step 2: carrying out resonance sparse decomposition on the vibration signal to obtain a high resonance component and a low resonance component, and removing the low resonance component;
and step 3: taking a high resonance component containing a fault signal of the planetary gear box as an observation signal, and carrying out resonance sparse decomposition on the observation signal to obtain a new high resonance component, namely a virtual channel signal;
and 4, step 4: based on the constructed virtual channel signal, processing an observation signal by using a FastICA algorithm, and separating an effective fault characteristic component;
and 5: and carrying out envelope spectrum analysis on the screened fault characteristic components, and extracting fault characteristic frequency so as to identify the fault type.
Further, the resonance sparse decomposition adopts a morphological component analysis method, and the specific method is as follows: let x be1Including a high resonant component of the sustained oscillation signal, x2A low resonance component including transient impacts; and the input signal x is x1、x2Summing; the specific formula is shown as follows;
x＝x1+x2 x,x1,x2∈RN
will assume a signal x1And x2Respectively with S1And S2The objective function to establish the morphological component is shown below:
Figure GDA0003011517700000021
wherein S1、S2Respectively representing a base function library containing high-quality factor and low-quality factor transformation; w1、W2Respectively represent signals x1、x2In the frame S1、S2A transform coefficient of down; m and n are the number of high and low resonance components, lambda1,iRegularization parameter, λ, for the ith component of the high-resonance component2,jA regularization parameter for the jth component of the low resonance components;
solving the target function by utilizing a splitting and amplifying Lagrange search algorithm with the aim of minimizing the target function; to obtain W1And W2Of (2) an optimal solution
Figure GDA0003011517700000022
And
Figure GDA0003011517700000023
thereby obtaining the estimated values of the high resonance component and the low resonance component
Figure GDA0003011517700000024
The expressions of (a) are respectively as follows:
Figure GDA0003011517700000025
further, the library of basis functions S1And S2The method is obtained by decomposing an input signal by using the quality factor adjustable wavelet transform.
Further, the FastICA algorithm in the step 4 adopts a FastICA algorithm based on the maximum negative entropy.
Further, the specific method for separating the effective fault feature component in step 4 is as follows:
step 4.1: normalizing and whitening the observation signal X;
step 4.2: setting the iteration number t to be 1,2,3, … P; p is the total number of iterations;
step 4.3: initializing a random vector, namely an independent component W;
step 4.4: the t-th iteration calculation is carried out, and E { Xg (W) is calculated by using a Newton methodTX) } + β W ═ 0; wherein β ═ E { W ═TXg(WTX) }; obtaining the value W of W*And saving the value of W;
Figure GDA0003011517700000031
wherein E { } is a mean operation, and g (·) is a nonlinear function;
step 4.5: standardizing W
Figure GDA0003011517700000032
Step 4.6: judging whether W is converged, if not, returning to the step 4.4, otherwise, turning to the step 4.7;
step 4.7: judging whether t is smaller than P; if t is less than P, t +1 and go to step 4.4; otherwise, stopping calculation;
p iterative calculations are carried out to obtain P W; i.e. P independent components, and calculating a linear combination W of the P W and the observed signal XTAnd X, selecting the linear combination with the maximum negative entropy as the fault characteristic component.
Has the advantages that: the method can combine the advantages of two algorithms of resonance sparse decomposition and rapid independent component analysis, and can solve the problem that the number of sensors in independent component analysis is not more than that of independent vibration sources; the fault type of the planetary gearbox can be effectively judged, and the fault characteristics of the planetary gearbox are extracted; and the method has no endpoint effect and modal aliasing phenomenon, and has a solid theoretical basis and a complete mathematical model.
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FIG. 1 is a flow chart of the present invention.
FIG. 2 is a schematic diagram of an experiment according to the present invention.
FIG. 3 is a waveform diagram of a simulated signal after FastICA processing.
FIG. 4 is an envelope spectrum of a simulated signal after FastICA processing.
FIG. 5 is a waveform diagram of a simulated signal processed by the RSSD and FastICA combined method.
FIG. 6 is a simulated signal envelope spectrum after RSSD and FastICA combined processing.
FIG. 7 is a waveform diagram of vibration signals in a fault state of a 600r/min planetary gearbox.
FIG. 8 is a vibration signal envelope spectrogram in a fault state of a 600r/min planetary gearbox.
FIG. 9 is a waveform diagram of a planetary gearbox tooth breakage fault signal processed by the RSSD and FastICA combined method.
FIG. 10 is a planetary gearbox tooth breakage fault signal envelope spectrogram processed by the RSSD and FastICA combined method.
Description of the drawings: 1. a drive motor; 2. a torque and rotation speed sensor A; 3. a coupling; 4. a rolling bearing A; 5. a planetary gear box; 6. a cylindrical gear case; 7. a rolling bearing B; 8. a load motor.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention.
The embodiment provides a fault diagnosis method for a planetary gearbox based on resonance sparse decomposition and FastICA algorithm,
the schematic diagram of the simulation test bench of the present embodiment is shown in fig. 2. The driving motor simulates the torque input of the wind wheel and is connected with the load motor after being transmitted by the secondary gear. In order to simulate the time-varying characteristic of the rotating speed of the wind wheel, a frequency converter is adopted to control the rotating speed of the motor. Various types of faults of the planetary gearbox are prepared in a defect manual processing mode, and the test bed can simulate the vibration conditions of the bearing, the main shaft and the gearbox when the rolling bearing and the gearbox have common faults. The planet gear broken tooth fault studied here is also prepared manually, and vibration sensors are installed on both sides of the planet gear box base. The preparation of the broken tooth fault of the planetary gear box is to grind and flatten the tooth length of 0.5 time of a certain tooth on a planetary gear by an electric spark machining technology. Some parameters of the planetary gearbox are shown in Table 1
TABLE 1 planetary gearbox parameters
Table1 Planetary gearbox parameters
Figure GDA0003011517700000041
In the test of the planetary gearbox, the rotating speed of the motor is set to be 600r/min, namely the rotating frequency of the planet wheel is 10Hz, the rotating frequency of the planet carrier is 3.137Hz, and the rotating frequency of the sun wheel is 10 Hz. The sampling frequency is 3140, and the number of sampling points is 8192.
Calculating the meshing frequency of the planetary gearbox at 600r/min
fm＝fcZr＝3.137×72＝225.8Hz
In the formula (f)cThe rotational frequency Z of the planet carrierrNumber of teeth of the ring gear
Planet wheel fault characteristic frequency:
Figure GDA0003011517700000042
fmis the rotational speed of the gear shaft, zpThe number of teeth of the planet wheel.
The specific method of the embodiment comprises the following steps:
step 1: obtaining a vibration signal of the planetary gearbox by using a vibration sensor;
step 2: performing resonance sparse decomposition (RSSD) on the vibration signal to obtain a high resonance component and a low resonance component, and removing the low resonance component;
and step 3: taking a high resonance component containing a fault signal of the planetary gear box as an observation signal, and carrying out resonance sparse decomposition on the observation signal to obtain a new high resonance component, namely a virtual channel signal;
and 4, step 4: based on the constructed virtual channel signal, processing an observation signal by using a fast independent component analysis (FastICA) algorithm, and separating an effective fault characteristic component;
and 5: and carrying out envelope spectrum analysis on the screened fault characteristic components, and extracting fault characteristic frequency so as to identify the fault type.
In step 2, the resonance sparse decomposition decomposes the complex signal into a high resonance component mainly based on the continuous oscillation signal and a low resonance component mainly based on the transient impact signal according to the difference of quality factors Q of the continuous oscillation signal and the transient impact signal. The fault signal of the planetary gear box is a narrow-band signal with frequency modulation and amplitude modulation, and belongs to high resonance components. The method can effectively reduce the influence of broadband signals (such as bearing fault signals and eccentricity) containing transient impact, and the like, and realize the dimension reduction of the number of vibration source signals in the signals.
The resonance sparse decomposition method of the signal mainly adopts morphological component analysis to decompose the signal, MCA sets that the signal to be processed is formed by linearly superposing components with obvious differences of various morphological characteristics, different overcomplete dictionaries are respectively selected to perform sparse representation on each component according to the characteristics of different component components, and the signal is finally decomposed into components with different morphological characteristics through multiple decomposition iterations. Hypothesis inputThe incoming signal x is formed by linearly combining a plurality of different morphological components, each component xkAll correspond to an overcomplete dictionary SkThen the signal x can be decomposed into
Figure GDA0003011517700000051
In resonance sparse decomposition, it is assumed that the input signal x can be represented as two signals x1、x2Sum, x1Comprising mainly a high resonance component, x, of the sustained oscillation signal2Mainly including the low resonance component of the transient impulse.
x＝x1+x2 x,x1,x2∈RN
The objective of the morphological component analysis is to estimate the source signals x with different resonance properties from the input signal x1And x2And the smaller the coupling degree of the two separated parts, the better. Assume signal number x1And x2Respectively available basis function library S1And S2(S1、S2Filter banks representing high and low quality factor tunable wavelets obtained by the TQWT method) sparse representation, the objective function of the morphological component is set as:
Figure GDA0003011517700000061
wherein S is1、S2Respectively representing a base function library containing high-quality factor and low-quality factor transformation; w1、W2Respectively is a representation signal x1、x2In the frame S1、S2A transform coefficient of down; m and n are the numbers of high and low resonance components respectively, and m is set to be 28 and n is set to be 11 according to experience in the embodiment; lambda [ alpha ]1,iRegularization parameter, λ, for the ith component of the high-resonance component2,jA regularization parameter for the jth component of the low resonance components; lambda [ alpha ]1,iAnd λ2,jThe value of (a) has an influence on the energy distribution of the resolved high and low resonance components,if both values are increased, the residual signal energy will be increased.
Because norm in the above formula is not microminiature and has more parameters, the resonance sparse decomposition method utilizes a split-augmented Lagrange search algorithm to update the transformation coefficient W through iteration1、W2The objective function J is minimized, and finally an effective separation of the high and low resonance components is achieved. When the objective function is minimized, the corresponding transformation coefficients of the high resonance component and the low resonance component are respectively
Figure GDA0003011517700000062
Then the estimated values of the high resonance component and the low resonance component are respectively:
Figure GDA0003011517700000063
in this embodiment, S is obtained by using quality factor adjustable wavelet transform1、S2(ii) a Binary wavelet transform can effectively perform sparse representation on segmented smooth signals, but the frequency resolution is low in signal analysis due to the fact that the quality factor is low. Compared with binary wavelet, the wavelet transform with adjustable quality factors has the characteristics of simple concept, complete dispersion, perfect reconstruction and moderate completeness, and by utilizing 2-based fast Fourier transform, the calculation is more efficient, and the quality factors and the redundancy are easier to quantify. Therefore, the resonance sparse decomposition method respectively obtains the basis function libraries transformed by the high-quality factors and the low-quality factors by using the quality factor adjustable wavelet transform, and calculates the corresponding transform coefficients. The quality factor adjustable wavelet transform realizes signal decomposition in an iterative mode by utilizing a two-channel decomposition filter bank consisting of a decomposition filter bank and a synthesis filter bank; the calculation formula of the number of decomposition layers is as follows:
Figure GDA0003011517700000064
in the formula, β is a high-pass scale factor, α is a low-pass scale factor, N is a signal scale, the greater the number of decomposition layers, the finer the decomposition, and the larger the calculation time, and according to engineering experience, the number of layers with high resonance components is 28 and the number of layers with low resonance components is 11.
The ICA method requires that the number of sensor channels is not less than the number of independent vibration sources, and in practical engineering application, the actual number of the vibration sources is far larger than the number of the sensor channels due to convolution mixing of signals of the vibration sources, serious installation errors of experimental equipment, serious background noise interference and the like, so that ICA separation is invalid. Step 3, taking the observation signal as an input signal, and carrying out resonance sparse decomposition on the observation signal to obtain a new high resonance component, namely a virtual channel signal;
in the step 4: FastICA is a fast-finding iterative algorithm that estimates independent components that are not gaussian with the objective function controlling the algorithm convergence. There are forms based on kurtosis, based on maximum likelihood estimation, based on negative entropy maximization, etc., and the fast ica algorithm based on negative entropy maximization is adopted in the embodiment.
According to the central limit theorem, the distribution of the sum of a plurality of variables is closer to a Gaussian distribution than the distribution of any one of the variables. The gaussian nature of the variables can thus be used to indirectly measure the independence of the variables. As can be known from the information theory, among all random variables with equal variance, the entropy of the Gaussian variable is the largest, so that the non-Gaussian property can be measured by using the entropy, and the larger the negative entropy is, the stronger the non-Gaussian property is. The negative entropy is defined as follows
Ng(Y)＝H(YGauss)-H(Y)
Wherein, YGAussIs a Gaussian random variable having the same variance as Y, and H (Y) is the differential entropy of the random variable
H(Y)＝-∫pY(ξ)lgpY(ξ)dξ
The probability density function is calculated by adopting negative entropy definition solution, and the following approximate formula is provided for simplifying the calculation
Ng(Y)＝{E[g(Y)]-E[g(YGauss)]}2 (2-5)
Wherein E {. is a mean operation, g (& gt) is a nonlinear function, and g can be taken1(y)＝tanh(a1y) or g2(y)＝yexp(-y2/2) or g3(y)＝y3Equal non-linear function, where 1 ≦ a12 or less, usually a is taken1＝1。
The objective function is equivalent to finding the linear combination W of XTMaximum negative entropy of X, such that Ng [ W ]TX]At maximum, only E [ g (W) is requiredTX)]At maximum, according to Kuhn-Tucker conditions, at E [ (W)TX)2]＝||W||2Under the constraint of 1, E [ g (W)TX)]And when the maximum value is reached, the following conditions are satisfied:
E{Xg(WTX)}+βW＝0
wherein β ═ E { W ═TXg(WTX) } is a constant value. FastICA pairs E [ g (W) using classical Newton's methodTX)]To obtain extreme values, i.e.
Figure GDA0003011517700000071
Obtaining:
Figure GDA0003011517700000081
the specific method for separating the effective fault characteristic component by using the FastICA algorithm in the step 4 comprises the following steps:
step 4.1: normalizing and whitening the observation signal X;
step 4.2: setting the iteration number t to be 1,2,3, … P; p is the total number of iterations;
step 4.3: initializing a random vector, namely an independent component W;
step 4.4: the t-th iteration calculation is carried out, and E { Xg (W) is calculated by using a Newton methodTX) } + β W ═ 0; wherein β ═ E { W ═TXg(WTX) }; obtaining the value W of W*And saving the value of W;
Figure GDA0003011517700000082
wherein E { } is a mean operation, and g (·) is a nonlinear function;
step 4.5: standardizing W
Figure GDA0003011517700000083
Step 4.6: judging whether W is converged, if not, returning to the step 4.4, otherwise, turning to the step 4.7;
step 4.7: judging whether t is smaller than P; if t is less than P, t +1 and go to step 4.4; otherwise, stopping calculation;
p iterative calculations are carried out to obtain P W; i.e. P independent components, and calculating a linear combination W of the P W and the observed signal XTAnd X, selecting the linear combination with the maximum negative entropy as the fault characteristic component.
Fig. 3 is a time domain waveform of the simulation signal after the rapid independent component analysis, fig. 4 is a signal frequency spectrum diagram of the simulation signal after the rapid independent component analysis, fig. 5 is a waveform diagram of the simulation signal after the resonance sparse decomposition and the rapid independent component analysis, and fig. 6 is a waveform of the simulation signal after the resonance sparse decomposition and the rapid independent component analysis. Comparing the signal waveform diagram 3 based on the FastICA processing with the signal waveform diagram 5 based on the RSSD and FastICA processing, it can be seen that the signal impulse component is more and the waveform period is not obvious after the FastICA processing is directly used. The signal impact after the dimension reduction of the RSSD is more prominent, and the periodicity of the waveform is more obvious. As shown in fig. 4 and 6, both methods can extract the fault characteristic frequency of the simulation signal, but RSSD and FastICA-based methods can effectively improve the frequency modulation at the fault characteristic frequency.
The preparation of the broken tooth fault of the planetary gear box is to grind and flatten the tooth length of 0.5 time of a certain tooth on a planetary gear by an electric spark machining technology. The method used herein is adopted to process the broken tooth fault signal of the planetary gearbox, and fig. 9 is a waveform diagram of the broken tooth fault signal processed by the method. Compared with fig. 7, the pulse impact and the periodicity in the graph are more obvious, fig. 10 is a broken tooth fault signal envelope spectrum processed by the embodiment, main components in the graph can be obtained at the rotation frequency (point a) and the frequency doubling (point B) of the planet carrier, the rotation frequency (point C) of solar energy and the fault characteristic frequency (point D) of the planet wheel, and the resonance sparse decomposition can reduce the dimension of an original signal, so that the rapid independent component analysis method can more accurately decompose each independent component, and is favorable for extracting the fault characteristic frequency of the planetary gearbox.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. The invention is not described in detail in order to avoid unnecessary repetition.

Claims (4)

1. The planetary gearbox fault diagnosis method based on resonance sparse decomposition and FastICA algorithm is characterized by comprising the following steps of:
step 1: obtaining a vibration signal of the planetary gearbox by using a vibration sensor;
step 2: carrying out resonance sparse decomposition on the vibration signal to obtain a high resonance component and a low resonance component, and removing the low resonance component;
and step 3: taking a high resonance component containing a fault signal of the planetary gear box as an observation signal, and carrying out resonance sparse decomposition on the observation signal to obtain a new high resonance component, namely a virtual channel signal;
and 4, step 4: based on the constructed virtual channel signal, processing an observation signal by using a FastICA algorithm, and separating an effective fault characteristic component;
and 5: and carrying out envelope spectrum analysis on the screened fault characteristic components, and extracting fault characteristic frequency so as to identify the fault type.
2. The method according to claim 1, wherein the resonance sparse decomposition adopts a morphological component analysis method, and the specific method is as follows: let input signal x be x1+x2Wherein x is1To include a high resonant component of the continuous oscillation signal, x2Low resonance components including transient impacts;
will signal x1And x2Respectively with S1And S2Representing, establishing a formThe objective function of the components is as follows:
Figure FDA0003011517690000011
wherein S1、S2Respectively representing a base function library containing high-quality factor and low-quality factor transformation; w1、W2Respectively represent signals x1、x2In the frame S1、S2A transform coefficient of down; m and n are the number of high and low resonance components, lambda1,iRegularization parameter, λ, for the ith component of the high-resonance component2,jA regularization parameter for the jth component of the low resonance components;
solving the target function by utilizing a splitting and amplifying Lagrange search algorithm with the aim of minimizing the target function; to obtain W1And W2Of (2) an optimal solution
Figure FDA0003011517690000012
And
Figure FDA0003011517690000013
thereby obtaining the estimated values of the high resonance component and the low resonance component
Figure FDA0003011517690000014
The expressions of (a) are respectively as follows:
Figure FDA0003011517690000015
3. the method of claim 2, wherein the library of basis functions S1And S2The method is obtained by decomposing an input signal by using the quality factor adjustable wavelet transform.
4. The method according to claim 1, wherein the FastICA algorithm in the step 4 adopts a FastICA algorithm based on negative entropy maximization.
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Families Citing this family (4)

* Cited by examiner, † Cited by third party
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CN111458141B (en) * 2020-04-10 2022-03-18 中国工程物理研究院机械制造工艺研究所 Vibration source number estimation method based on empirical mode decomposition and singular value decomposition
CN111638055B (en) * 2020-05-18 2021-11-16 东南大学 Gearbox fault diagnosis method based on resonance sparse decomposition improved algorithm
CN111693279B (en) * 2020-05-22 2021-06-01 电子科技大学 Mechanical fault diagnosis method based on MPGA parametric resonance sparse decomposition
CN112179475B (en) * 2020-08-20 2021-09-07 电子科技大学 Separation method of multi-source aliasing distributed optical fiber vibration sensing signals

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103575523A (en) * 2013-11-14 2014-02-12 哈尔滨工程大学 Rotating machine fault diagnosis method based on Fast ICA-spectrum kurtosis-envelope spectrum analysis
CN104485113A (en) * 2014-12-23 2015-04-01 长沙理工大学 Multi-fault-source acoustic emission signal separation method
CN106441871A (en) * 2016-10-20 2017-02-22 哈尔滨工业大学 Wind power gearbox fault diagnosis method based on self-adaptive resonance sparse decomposition theory
CN107024352A (en) * 2017-05-03 2017-08-08 哈尔滨理工大学 A kind of Rolling Bearing Fault Character extracting method based on slip entropy ICA algorithm
CN108152033A (en) * 2017-12-14 2018-06-12 东华大学 A kind of compound Weak fault diagnostic method of the gear-box of sparse disjunctive model
CN108805059A (en) * 2018-05-29 2018-11-13 东华大学 The Fault Diagnosis of Gear Case method that sparse regularization filtering is decomposed with adaptive sparse

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103575523A (en) * 2013-11-14 2014-02-12 哈尔滨工程大学 Rotating machine fault diagnosis method based on Fast ICA-spectrum kurtosis-envelope spectrum analysis
CN104485113A (en) * 2014-12-23 2015-04-01 长沙理工大学 Multi-fault-source acoustic emission signal separation method
CN106441871A (en) * 2016-10-20 2017-02-22 哈尔滨工业大学 Wind power gearbox fault diagnosis method based on self-adaptive resonance sparse decomposition theory
CN107024352A (en) * 2017-05-03 2017-08-08 哈尔滨理工大学 A kind of Rolling Bearing Fault Character extracting method based on slip entropy ICA algorithm
CN108152033A (en) * 2017-12-14 2018-06-12 东华大学 A kind of compound Weak fault diagnostic method of the gear-box of sparse disjunctive model
CN108805059A (en) * 2018-05-29 2018-11-13 东华大学 The Fault Diagnosis of Gear Case method that sparse regularization filtering is decomposed with adaptive sparse

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Resonance-Based Sparse Signal Decomposition and Its Application in Mechanical Fault Diagnosis:A Review;Wentao Huang et al;《sensors》;20171231;1-27 *
并联双重Q因子在齿轮箱复合故障净化提取与盲分离中的应用;莫代一;《仪器仪表学报》;20130930;第34卷(第9期);2013-2020 *

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