CN106895985A - The fault-signal noise reduction reconstruct characteristic recognition method of high-speed rod-rolling mill - Google Patents
The fault-signal noise reduction reconstruct characteristic recognition method of high-speed rod-rolling mill Download PDFInfo
- Publication number
- CN106895985A CN106895985A CN201710141369.8A CN201710141369A CN106895985A CN 106895985 A CN106895985 A CN 106895985A CN 201710141369 A CN201710141369 A CN 201710141369A CN 106895985 A CN106895985 A CN 106895985A
- Authority
- CN
- China
- Prior art keywords
- singular value
- signal
- singular
- noise
- vector
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M99/00—Subject matter not provided for in other groups of this subclass
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
A kind of fault-signal noise reduction the embodiment of the invention discloses high-speed rod-rolling mill reconstructs characteristic recognition method.Methods described includes：Vibration signal matrix to gathering carries out singular value decomposition, to generate corresponding singular value vector；Singular value Difference Spectrum is constructed according to the singular value vector, and effective singular value order is determined according to the singular value Difference Spectrum；In the range of effective singular value order, Fast Fourier Transform (FFT) FFT is carried out to the singular value vector；There is the amplitude of power frequency and its frequency multiplication feature, to obtain corresponding noise singular in search FFT result sequence；Time-domain signal is reconstructed with the singular value vector for rejecting the noise singular, to obtain fault characteristic signals.The fault recognition method of high-speed rod-rolling mill provided in an embodiment of the present invention can improve the signal to noise ratio of fault characteristic signals, realize the Accurate Diagnosis of high-speed rod-rolling mill failure.
Description
Technical field
A kind of failure the present embodiments relate to Research on failure diagnosis of rolling mill technical field, more particularly to high-speed rod-rolling mill is known
Other method.
Background technology
With the continuous progress of science and technology, the direction hair of the positive high speed of metallurgical equipment, high capacity and automaticity high
Exhibition, but the order of severity of accident is also significantly increased caused by equipment fault.How to ensure the safe operation of equipment, find in time
Hidden danger, eliminates and avoids failure, it has also become unusual urgent problems.Common high-speed rod-rolling mill failure includes：Milling train
Roll mandrel axial float, bevel gear beat disconnected tooth, roll mandrel, shaft coupling disengagement and bevel gear and disengage.
The diagnosis of high-speed rod-rolling mill status fault is the running state information in monitoring device and carries out Fault Identification, it is to avoid
The huge economic losses that failure propagation brings.Sensitivity and the reliability of detection means need to be thus improved, and in existing detection
On device basic, by the digitized processing to institute's detection signal, low cost, the high-speed rod-rolling mill shape of high reliability are capable of achieving
State fault diagnosis.
Often there is random noise disturbance and power supply Hz noise simultaneously in the high-speed rod-rolling mill vibration signal of collection, often
There is the erroneous judgement to true running status in judgement of the rule method to signal.Therefore eliminate or reduce the random noise in vibration signal
With the important content that industrial frequency noise interference is high-speed rod-rolling mill whirling vibration signal transacting.
In the prior art, can be using the mode of singular value decomposition (Singular value decomposition, SVD)
Complete the above-mentioned elimination to random noise and industrial frequency noise.Stress below using the noise canceling procedures of SVD technique.
, it is necessary to the discrete signal of collection is configured into matrix form, such as Hankel matrixes before signal transacting being carried out with SVD
Deng.
Assuming that the discrete signal of collection has following form：
X=[x (1), x (2) ..., x (N)] (1)
The Hankel matrix As of following form can be configured to：
Parameter in formula (2) meets：1<n<N.M=N-n+1 is made, then there are A ∈ Rm×n。
Vectorial uiAnd viMeet following relation：
In formula (3), ui∈Rm×1, vi∈Rn×1, i=1,2,3 ..., q, q=min (m, n).
If signal x (i) to be expressed as the combination of DC component, AC compounent and noise component(s), have：
X (j)=z (j)+s (j)+ξ (j) (4)
In formula (4), j=1,2,3 ..., N, z (j) represents the corresponding DC component signals of x (j), and s (j) represents x (j) correspondences
AC compounent signal, ξ (j) represents x (j) corresponding AC compounent signal.Thus, the Hankel matrix As constructed by x (j) can
To be expressed as：
A=Az+As+Aξ (5)
Wherein, AzIt is the Hankel matrixes constructed according to DC component signal z (j), AsIt is according to AC compounent signal s (j)
The Hankel matrixes of construction, AξIt is the Hankel matrixes constructed according to noise component(s) ξ (j), and above three matrix is satisfied by
Az、As、Aξ∈Rm×n。
All it is a section intercepted from original signal per a line in Hankel matrixes, and adjacent vector is only delayed one
Point, therefore, the adjacent rows information of the Hankel matrixes being made up of cyclic component is closely related, and its order is much smaller than q=min
(m,n).Then coherence is weak for two row informations of the Hankel matrixes constructed by random noise sequences.
In restructuring matrix A, the selection of reconstruct order k is most important, and the too small meeting of order value causes that distorted signals is serious, its value
It is excessive, cause more noise.It is y that the signal for being formed is superimposed by the sinusoidal signal x and small noise w of standard, is constructed by y
The singular value σ of Hankel matrixes Yi(Y), the singular value σ of matrix Xi(X), the singular value σ of matrix Wi(W) meet as follows between three
Relation：
σi(X)≤σi(Y)≤σi(X)+σi(W) (6)
Formula (6) shows, in the singular value generated by signals and associated noises y, preceding r value will be far longer than m-r value below,
R is reconstruct order k values.
Thus the concept of singular value Difference Spectrum is drawn.Assuming that the unusual value sequence of matrix Y is, definition：
bi=σi-σi+1
Wherein, i=1,2,3 ..., m-1.Due to the definition in above formula, then by biThe sequence b of composition is referred to as singular value difference
Spectrum.Sequence number according to corresponding to maximum in singular value Difference Spectrum determines reconstruct order k, and k component carries out signal weight before choosing
Structure.That is, reconstruction signalWith following form：
The conventional method for carrying out signal de-noising treatment using SVD methods is that the singular value obtained by singular value decomposition is divided into two
Part a, part is the smaller singular value for reflecting noise, and another part is the larger singular value for reflecting useful feature signal, then
Will smaller unusual value part zero setting, the larger unusual value part of reservation, so as to reach the purpose of noise reduction and feature extraction.
But, inventor has found above-mentioned existing in the research process of the existing characteristic extraction procedure based on SVD
The noise reduction of mode is not fine, is mainly manifested in the frequency domain character not in view of noise, thus can not be accurate
Completion fault characteristic signals extraction.
The content of the invention
For above-mentioned technical problem, a kind of fault-signal noise reduction reconstruct of high-speed rod-rolling mill is the embodiment of the invention provides
Characteristic recognition method, to improve the signal to noise ratio of fault characteristic signals, realizes the Accurate Diagnosis of high-speed rod-rolling mill failure.
A kind of fault-signal noise reduction reconstruct characteristic recognition method of high-speed rod-rolling mill is the embodiment of the invention provides, it is described
Method includes：
Vibration signal matrix to gathering carries out singular value decomposition, to generate corresponding singular value vector；
Singular value Difference Spectrum is constructed according to the singular value vector, and is determined according to the singular value Difference Spectrum effectively unusual
Value order, to reduce random noise；
In the range of effective singular value order, Fast Fourier Transform (FFT) FFT is carried out to the singular value vector；
There is the amplitude of power frequency and its frequency multiplication feature, to obtain corresponding noise singular in search FFT result sequence；
Time-domain signal is reconstructed with the singular value vector for rejecting the noise singular, to obtain fault characteristic signals.
The fault-signal noise reduction reconstruct characteristic recognition method of high-speed rod-rolling mill provided in an embodiment of the present invention, by adopting
The vibration signal matrix of collection carries out singular value decomposition, to generate corresponding singular value vector, is constructed according to the singular value vector
Singular value Difference Spectrum, and effective singular value order is determined according to the singular value Difference Spectrum, in the scope of effective singular value order
It is interior, Fast Fourier Transform (FFT) FFT is carried out to the singular value vector, have power frequency and its frequency multiplication special in search FFT result sequence
The amplitude levied, to obtain corresponding noise singular, time-domain signal is reconstructed with the singular value vector for rejecting the noise singular,
So as to realize the extraction of fault characteristic signals by way of singular value and singular value vector are combined, fault signature is improve
The signal to noise ratio of signal, realizes the Accurate Diagnosis of high-speed rod-rolling mill failure.
Brief description of the drawings
By the detailed description made to non-limiting example made with reference to the following drawings of reading, it is of the invention other
Feature, objects and advantages will become more apparent upon：
Figure 1A is the oscillogram of primary signal；
Figure 1B is the spectrogram of primary signal；
Fig. 2 is preceding 30 singular values of signal；
Fig. 3 is the graph of a relation of singular value and amplitude；
Fig. 4 is the graph of a relation of singular value and frequency；
Fig. 5 is the graph of a relation of singular value and phase；
Fig. 6 is the graph of a relation of component singular value and amplitude；
Fig. 7 is the graph of a relation of signal singular values and matrix columns；
Fig. 8 is the stream of the fault-signal noise reduction reconstruct characteristic recognition method of high-speed rod-rolling mill provided in an embodiment of the present invention
Cheng Tu；
Fig. 9 A are the signal waveforms that engineering provided in an embodiment of the present invention gathers signal；
Fig. 9 B are the signal spectrum figures that engineering provided in an embodiment of the present invention gathers signal；
Figure 10 is the singular value spectrogram that engineering provided in an embodiment of the present invention gathers signal；
Figure 11 is the singular value Difference Spectrum that engineering provided in an embodiment of the present invention gathers signal；
Figure 12 A are the oscillograms of reconstruction signal provided in an embodiment of the present invention；
Figure 12 B are the spectrograms of reconstruction signal provided in an embodiment of the present invention；
Figure 13 is the left singular vector frequency spectrum of signal section after engineering collection signal reconstruction provided in an embodiment of the present invention；
Figure 14 A are the signal waveforms after engineering collection signal reconstruction noise reduction provided in an embodiment of the present invention；
Figure 14 B are the spectrograms after engineering collection signal reconstruction noise reduction provided in an embodiment of the present invention.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is used only for explaining the present invention, rather than limitation of the invention.It also should be noted that, in order to just
Part rather than entire infrastructure related to the present invention is illustrate only in description, accompanying drawing.
The fault-signal noise reduction reconstruct characteristic recognition method of high-speed rod-rolling mill provided in an embodiment of the present invention is introduced first
General principle.
In formula (3), orderIf AiFirst row vector be Pi,1, Vi,nIt is AiLast column vector is gone
Fall the sub- column vector after its first element, by Pi,1And Vi,nTransposition end to end form component signal Pi, i.e.,：
In formula (8), Pi,n∈R1×n, Vi,n∈R(m-1)×1.An important decomposition for constituting primary signal X.
AiUse row vector Pi,1,Pi,2,…,Pi,mRepresent, Pi,m∈R(m-1)×1；Matrix A row vector represents X1,X2,…,Xm,
Xm∈R1×n, then the row vector of A is equal to all AiCorresponding row vector superposition, i.e.,：
X1=P1,1+P2,1+…+Pr,1 (9)
If column vector V in AnRepresent, Vn∈R(m-1)×1, VnEqual to all AiIn corresponding column vector Vi,nSuperposition, its turn
Put and similarly set up, i.e.,：
Signal X vector formsRepresent, and component signal PiUse vector formTable
Show, then all component signals and be：
Also just have：
P1+P2+…+Pr=X (11)
Primary signal is configured to Hankel matrixes and singular value decomposition is carried out, primary signal component signal can be decomposed into
Linear superposition form.Phase invariant of each component signal separated from original signal in original signal, i.e., with zero
Phase offset characteristic.The several components for choosing detection frequency carry out simple linear superposition, such that it is able to realize to signal characteristic
The extraction of information.
According to formula (8), the energy that can calculate component signal Pi is：
In formula (12), ui1It is vectorial ui1st coordinate, u of uii1<1；vi,nIt is vector viN-th coordinate, vi,n<1.By
Formula (12) understands：I.e.：
|Pi|∝σi (13)
If signal X is made up of DC component, AC compounent and noise, AC compounent is expressed as Pi=asin (2 π ft+ φ),
Then：
Due to PiIt is periodic signal, the cycle is T (data points), and the signal period number included in hits N is N0(it is whole
Number), if N=TN0, then ε is 0；If N ≠ TN0, then | ε |<N-TN0<T/2, when hits N includes multiple signal periods, then
T/2<<N, can obtain：
Namely：
Can be obtained by formula (13) and formula (16)：
σi∝a (17)
σi∝N (18)
It follows that：The singular value of cyclical component signal is directly proportional with its amplitude, and with signal frequency and phase
It is unrelated；When cyclical component signal is constant, singular value is directly proportional to signal sampling number.
For further instruction problem, also by simulating, verifying singular value and signal amplitude, frequency and phase it
Between relation.
Take emulation signal s=s0+ ξ (n), s0=a1sin(2πf1t+φ1)+a2sin(2πf2t+φ2) it is test signal；Ginseng
Number is a1=3, f1=50, φ1=0.2 π, a2=2, f2=115, φ2=1.3 π；Sample rate fs=1024, hits fN=
1024.ξ (n) is white Gaussian noise that intensity is 1.Emulation signal waveform and frequency spectrum are as illustrated in figures 1A and ib.
(1) relation between singular value and signal
Construction Hankel matrixes, line number m=512, columns n=N-m+1=513.SVD decomposition is carried out to Hankel matrixes,
The singular value of generation is as shown in Fig. 2 the singular value totally 512 of SVD treatment generations, gives preceding 30 singular values in figure.1st, 2
It is the component of 50Hz that individual singular value represents frequency, and it is the component of 115Hz that the 3rd, 4 singular values represent frequency, remaining singular value generation
Table noise.
In the case where there is noise conditions, preceding 8 singular values are followed successively by：759.74,758.00,511.88,510.74,47.59,
47.57,46.02,45.97.1st and the 2nd singular value size is approached, and the 3rd and the 4th singular value size is approached, and the 5th~8 is strange
Different value size is approached.It is easy analysis, previous singular value is only taken in the close singular value of two sizes.
In order to more accurately observe the same amplitude of singular value, frequency and the Changing Pattern of phase, erased noise component ξ (n) divides
Analysis s0Changing Pattern.
Singular value is with the Changing Pattern between amplitude：Only change signal s0Middle parameter a1, remaining parameter constant, singular value with
Amplitude a1Relation as shown in figure 3, wherein singular value is with amplitude a1It is directly proportional.
The relation of singular value same frequency：Only change signal s0Middle parameter f1, remaining parameter constant, f1In the analysis of signal highest
Uniform value, obtains Fig. 4 in frequency.As can be seen from Fig., singular value does not change substantially and changes with frequency.
Influence of the phase place change to singular value：Only change phase1, other parameters are constant, between singular value same-phase
Relation is as shown in Figure 5.Phase place change, singular value keeps constant, illustrates that singular value is unrelated with phase.
a1=3, f1=50, φ1=0.2 π keeps constant, changes a2, f2Or φ2Identical conclusion can be drawn.
When two components of research take identical amplitude, singular value is with amplitude variation relation.That is a1=a2, signal s0In other ginseng
Number is constant：f1=50, φ1=0.2 π；f2=115, φ2=1.3 π.Variation relation such as Fig. 6 of two component amplitudes and singular value
Shown, singular value is identical when component amplitude is identical, and both linearly change.
The above results show that the singular value of cyclical signal is directly proportional to its amplitude, and unrelated with signal frequency and phase.
Therefore, the size according to component amplitude can determine its position of the corresponding singular value in unusual value sequence, enter
And can extract the elimination that the singular value realizes corresponding interference components.
(2) relation of singular value and matrix structure
Signal s0Middle parameter sets as follows：a1=3, f1=50, φ1=0.2 π；a2=2, f2=115, φ2=1.3 π.
By hits N set gradually for：500,760,1000,1500,1800,2000, then construct Hankel matrixes：Row
It is n=N/2, behavior m=N-n+1.SVD decomposition is carried out, matrix columns is obtained with the relation between singular value, as shown in Figure 7.Figure
In two singular values of component all with the change of Hankel matrix columns linear change, keep constant in each component of signal
When, singular value is directly proportional to signal sampling number.
According to conclusion above, the present invention combines both singular values and singular vectors, the general theory based on SVD
Framework, while eliminating random noise and industrial frequency noise in vibration signal.After signal is through SVD, advised according to unusual Distribution value first
Rule determines effective singular value order, to reduce the random noise in signal, then the singular vector in the order scale is carried out
Fast Fourier Transform (FFT), search has the amplitude spectrum of power frequency and its frequency multiplication feature to obtain corresponding singular vector, because unusual
Value and singular vector are one-to-one, and then obtain singular value, and time-domain signal is reconstructed with remaining singular values and singular vectors,
So as to further remove industrial frequency noise.This inventive method can eliminate power frequency and other noises in the detection signal for obtaining, just
In dominant extraction fault characteristic signals.
The fault-signal noise reduction that the embodiment of the invention provides high-speed rod-rolling mill reconstructs a kind of skill of characteristic recognition method
Art scheme.Referring to Fig. 1, the fault recognition method of the high-speed rod-rolling mill includes：
S81, the analog vibration signal to gathering is digitized.
In a testing experiment, the rotating speed for testing axle is 3500 revs/min, with 1000Hz sample rates continuous acquisition 60 seconds,
1000 signals are analyzed in the middle of interception, and the signal of collection is as shown in Fig. 9 A and Fig. 9 B.
S82, the vibration signal matrix is constructed according to the vibration signal that digitlization is obtained.
In the present embodiment, by constructing construction of the Hankel matrixes completion to vibration signal matrix.
S83, the vibration signal matrix to gathering carries out singular value decomposition, to generate corresponding singular value vector.
The signal construction that will be collected and carries out singular values and singular vectors Combined Processing at Hankel matrixes, generation it is unusual
Value spectrum is as shown in Figure 10, and preceding 50 singular values are listed in figure.
S84, singular value Difference Spectrum is constructed according to the singular value vector, and is determined according to the singular value Difference Spectrum effective
Singular value order, to reduce random noise.
In the present embodiment, singular value Difference Spectrum is constructed according to equation below：
bi=σi-σi+1 (19)
In formula (19), biIt is i-th difference value in the singular value Difference Spectrum, σiIt is i-th in the singular value vector
Individual singular value, σi+1It is the i+1 singular value in the singular value vector.
Singular value Difference Spectrum, namely in Figure 11, first maximum turned left from the right side is 21, it is thus determined that effectively singular value
Order is 21.According to this effective singular value order reconstruction signal, signal waveform and frequency spectrum such as Figure 12 A of reconstruct and Figure 12 B institutes
Show.Compare Fig. 9 and Figure 12 it can be found that random noise has very big reduction.
S85, in the range of effective singular value order, Fast Fourier Transform (FFT) FFT is carried out to the singular value vector.
To left singular vector U1―U21Make FFT treatment, preceding 6 amplitude spectrums and U are shown in Figure 1312And U13Frequency spectrum.
S86, the amplitude in search FFT result sequence with power frequency and its frequency multiplication feature is unusual to obtain corresponding noise
Value.
In fig. 13, U1Frequency be zero, represent DC component；U2And U3Frequency be 64Hz, this is the fundamental frequency point of main shaft
Amount；And U4And U5Frequency be 50Hz, this is power frequency component, and U12And U13Frequency be 150Hz, show the power frequency in the presence of 3 times
Component；U6And other vectors represent other spectrum components of main shaft.
According to analysis above, it should by U4、U5、U12、U13Singular value after corresponding FFT by converting after it is unusual
Rejected in value vector.
S87, reconstructs time-domain signal, to obtain fault characteristic signals with the singular value vector for rejecting the noise singular.
The reconstruct of time-domain signal is the inverse process digitized by collection signal to FFT.And, the reconstruct of above-mentioned time-domain signal
Process includes IFFT map functions.
Use singular value σi(i=1,2,3,6 ..., 11,14 ..., 21) and singular vector reconstruct time-domain signal, waveform and frequency
Spectrum is as shown in Figure 14 A and Figure 14 B.Compare Figure 12 and Figure 14 it can be found that not only the random noise in the signal after noise reduction is able to
Removal, Hz noise is also eliminated, and obtains clean rotor fundamental frequency and its frequency-doubled signal, follow-up to rotor fortune so as to be conducive to
The analysis and diagnosis of row state.
The embodiment of the present invention combines singular value and singular value vector, and using the singular value vector after FFT
Determine the corresponding noise singular of industrial frequency noise, and noise singular is rejected from the singular value vector after FFT, according to
Singular value vector reconstruct time-domain signal after rejecting, improves the signal to noise ratio of fault characteristic signals, realizes high-speed rod-rolling mill
The Accurate Diagnosis of failure.
The preferred embodiments of the present invention are the foregoing is only, is not intended to limit the invention, for those skilled in the art
For, the present invention can have various changes and change.It is all any modifications made within spirit and principles of the present invention, equivalent
Replace, improve etc., should be included within the scope of the present invention.
Claims (5)
1. a kind of fault-signal noise reduction of high-speed rod-rolling mill reconstructs characteristic recognition method, it is characterised in that including：
Vibration signal matrix to gathering carries out singular value decomposition, to generate corresponding singular value vector；
Singular value Difference Spectrum is constructed according to the singular value vector, and effective singular value rank is determined according to the singular value Difference Spectrum
It is secondary, to reduce random noise；
In the range of effective singular value order, Fast Fourier Transform (FFT) FFT is carried out to the singular value vector；
There is the amplitude of power frequency and its frequency multiplication feature, to obtain corresponding noise singular in search FFT result sequence；
Time-domain signal is reconstructed with the singular value vector for rejecting the noise singular, to obtain fault characteristic signals.
2. method according to claim 1, it is characterised in that also include：
Before singular value decomposition is carried out to the vibration signal for gathering, the analog vibration signal to gathering is digitized；And
The vibration signal matrix is constructed according to the vibration signal that digitlization is obtained.
3. method according to claim 2, it is characterised in that the vibration is constructed according to the vibration signal that digitlization is obtained
Signal matrix includes：
By constructing Hankel matrixes, the vibration signal matrix is constructed.
4. method according to claim 1, it is characterised in that singular value Difference Spectrum bag is constructed according to the singular value vector
Include：
The singular value Difference Spectrum is constructed according to equation below：
bi=σi-σi+1
Wherein, biIt is i-th difference value in the singular value Difference Spectrum, σiFor i-th in the singular value vector is unusual
Value, σi+1It is the i+1 singular value in the singular value vector.
5. method according to claim 1, it is characterised in that reconstructed with the singular value vector for rejecting the noise singular
Time-domain signal, is included with obtaining fault characteristic signals：
Time-domain signal is reconstructed by carrying out Fast Fourier Transform Inverse IFFT to the singular value vector for rejecting the noise singular,
To obtain fault characteristic signals.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710141369.8A CN106895985B (en) | 2017-03-10 | 2017-03-10 | The fault-signal noise reduction of high-speed rod-rolling mill reconstructs characteristic recognition method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710141369.8A CN106895985B (en) | 2017-03-10 | 2017-03-10 | The fault-signal noise reduction of high-speed rod-rolling mill reconstructs characteristic recognition method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106895985A true CN106895985A (en) | 2017-06-27 |
| CN106895985B CN106895985B (en) | 2019-10-25 |
Family
ID=59192545
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710141369.8A Active CN106895985B (en) | 2017-03-10 | 2017-03-10 | The fault-signal noise reduction of high-speed rod-rolling mill reconstructs characteristic recognition method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106895985B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109799718A (en) * | 2019-01-29 | 2019-05-24 | 特斯联（北京）科技有限公司 | A kind of wisdom building control method and system based on Internet of Things sensing |
| CN111611870A (en) * | 2020-04-26 | 2020-09-01 | 广东机电职业技术学院 | Fault feature identification method, system, device and medium for tandem cold mill |
| CN112924175A (en) * | 2021-01-22 | 2021-06-08 | 南京信息工程大学 | Bearing fault signal noise reduction method based on singular value decomposition |
| CN113361331A (en) * | 2021-05-17 | 2021-09-07 | 广东机电职业技术学院 | Power frequency interference elimination method, system and medium based on windowing interpolation FFT |
| CN113565666A (en) * | 2021-07-05 | 2021-10-29 | 江西洪屏抽水蓄能有限公司 | Method for judging unit operation state based on vibration data characteristics of hydroelectric generating set |
| CN114659791A (en) * | 2022-02-28 | 2022-06-24 | 广东机电职业技术学院 | Steam turbine fault detection method, system, device and storage medium |
| CN113565666B (en) * | 2021-07-05 | 2024-05-14 | 江西洪屏抽水蓄能有限公司 | Method for judging running state of unit based on vibration data characteristics of water turbine generator unit |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009288100A (en) * | 2008-05-29 | 2009-12-10 | Mitsubishi Heavy Ind Ltd | Soundness diagnosis method and program, and soundness diagnosis apparatus of windmill |
| US20110118950A1 (en) * | 2006-05-12 | 2011-05-19 | U.S. Government As Represented By The Secretary Of The Army | Method for estimation of at least one engine parameter |
| CN102135558A (en) * | 2010-11-05 | 2011-07-27 | 重庆市电力公司綦南供电局 | Acquisition and hierarchical identification system of overvoltage data and hierarchical pattern identification method of overvoltage types |
| CN103454528A (en) * | 2013-08-30 | 2013-12-18 | 华南理工大学 | Method for detecting and identifying electric system element fault based on form singular entropy |
| CN103674550A (en) * | 2013-12-16 | 2014-03-26 | 南京航空航天大学 | Real-time mixing denoising method for rolling bearing static monitoring signal |
-
2017
- 2017-03-10 CN CN201710141369.8A patent/CN106895985B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110118950A1 (en) * | 2006-05-12 | 2011-05-19 | U.S. Government As Represented By The Secretary Of The Army | Method for estimation of at least one engine parameter |
| JP2009288100A (en) * | 2008-05-29 | 2009-12-10 | Mitsubishi Heavy Ind Ltd | Soundness diagnosis method and program, and soundness diagnosis apparatus of windmill |
| CN102135558A (en) * | 2010-11-05 | 2011-07-27 | 重庆市电力公司綦南供电局 | Acquisition and hierarchical identification system of overvoltage data and hierarchical pattern identification method of overvoltage types |
| CN103454528A (en) * | 2013-08-30 | 2013-12-18 | 华南理工大学 | Method for detecting and identifying electric system element fault based on form singular entropy |
| CN103674550A (en) * | 2013-12-16 | 2014-03-26 | 南京航空航天大学 | Real-time mixing denoising method for rolling bearing static monitoring signal |
Non-Patent Citations (2)
| Title |
|---|
| 张晓涛 等: "基于谐波小波和SVD的可倾瓦滑动轴承工频干扰抑制", 《华南理工大学学报( 自然科学版)》 * |
| 赵学智 等: "奇异值差分谱理论及其在车床主轴箱故障诊断中的应用", 《机械工程学报》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109799718A (en) * | 2019-01-29 | 2019-05-24 | 特斯联（北京）科技有限公司 | A kind of wisdom building control method and system based on Internet of Things sensing |
| CN111611870A (en) * | 2020-04-26 | 2020-09-01 | 广东机电职业技术学院 | Fault feature identification method, system, device and medium for tandem cold mill |
| CN111611870B (en) * | 2020-04-26 | 2023-05-09 | 广东机电职业技术学院 | Fault feature identification method, system, device and medium for cold tandem mill |
| CN112924175A (en) * | 2021-01-22 | 2021-06-08 | 南京信息工程大学 | Bearing fault signal noise reduction method based on singular value decomposition |
| CN113361331A (en) * | 2021-05-17 | 2021-09-07 | 广东机电职业技术学院 | Power frequency interference elimination method, system and medium based on windowing interpolation FFT |
| CN113361331B (en) * | 2021-05-17 | 2023-08-25 | 广东机电职业技术学院 | Power Frequency Interference Elimination Method, System and Medium Based on Windowed Interpolation FFT |
| CN113565666A (en) * | 2021-07-05 | 2021-10-29 | 江西洪屏抽水蓄能有限公司 | Method for judging unit operation state based on vibration data characteristics of hydroelectric generating set |
| CN113565666B (en) * | 2021-07-05 | 2024-05-14 | 江西洪屏抽水蓄能有限公司 | Method for judging running state of unit based on vibration data characteristics of water turbine generator unit |
| CN114659791A (en) * | 2022-02-28 | 2022-06-24 | 广东机电职业技术学院 | Steam turbine fault detection method, system, device and storage medium |
| CN114659791B (en) * | 2022-02-28 | 2023-07-04 | 广东机电职业技术学院 | Turbine fault detection method, system, device and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106895985B (en) | 2019-10-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106895985B (en) | The fault-signal noise reduction of high-speed rod-rolling mill reconstructs characteristic recognition method | |
| Liang et al. | Maximum average kurtosis deconvolution and its application for the impulsive fault feature enhancement of rotating machinery | |
| Yan et al. | Compound fault diagnosis of rotating machinery based on OVMD and a 1.5-dimension envelope spectrum | |
| Hu et al. | An adaptive spectral kurtosis method and its application to fault detection of rolling element bearings | |
| Gu et al. | Rolling element bearing faults diagnosis based on kurtogram and frequency domain correlated kurtosis | |
| Osman et al. | An enhanced Hilbert–Huang transform technique for bearing condition monitoring | |
| Jiang et al. | A time-frequency spectral amplitude modulation method and its applications in rolling bearing fault diagnosis | |
| Han et al. | The fault feature extraction of rolling bearing based on EMD and difference spectrum of singular value | |
| Feng et al. | Spectral negentropy based sidebands and demodulation analysis for planet bearing fault diagnosis | |
| Wang et al. | Periodical sparse low-rank matrix estimation algorithm for fault detection of rolling bearings | |
| Cui et al. | Spectrum-based, full-band preprocessing, and two-dimensional separation of bearing and gear compound faults diagnosis | |
| Xu et al. | An adaptive spectrum segmentation method to optimize empirical wavelet transform for rolling bearings fault diagnosis | |
| Ugwiri et al. | Benefits of enhanced techniques combining negentropy, spectral correlation and kurtogram for bearing fault diagnosis | |
| Yuan et al. | High-fidelity noise-reconstructed empirical mode decomposition for mechanical multiple and weak fault extractions | |
| Deng et al. | Adaptive parametric dictionary design of sparse representation based on fault impulse matching for rotating machinery weak fault detection | |
| Yuan et al. | Dual-core denoised synchrosqueezing wavelet transform for gear fault detection | |
| Ma et al. | An improved intrinsic time-scale decomposition method based on adaptive noise and its application in bearing fault feature extraction | |
| Ma et al. | An improved time-frequency analysis method for instantaneous frequency estimation of rolling bearing | |
| Zhang et al. | Research on the fault diagnosis method for rolling bearings based on improved VMD and automatic IMF acquisition | |
| Yan et al. | A bearing fault feature extraction method based on optimized singular spectrum decomposition and linear predictor | |
| Wang et al. | Low-rank enforced fault feature extraction of rolling bearings in a complex noisy environment: A perspective of statistical modeling of noises | |
| Zhang et al. | Feature extraction under bounded noise background and its application in low speed bearing fault diagnosis | |
| Xiong et al. | Early fault diagnosis of rolling bearings based on parameter-adaptive multipoint optimal minimum entropy deconvolution adjusted and dynamic mode decomposition | |
| An et al. | A parameter-optimized variational mode decomposition investigation for fault feature extraction of rolling element bearings | |
| Xu et al. | Rolling bearing fault feature extraction via improved SSD and a singular-value energy autocorrelation coefficient spectrum |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |