CN112464811A - Method for accurately filtering high-frequency random noise in pumped storage unit runout signal - Google Patents
Method for accurately filtering high-frequency random noise in pumped storage unit runout signal Download PDFInfo
- Publication number
- CN112464811A CN112464811A CN202011353478.4A CN202011353478A CN112464811A CN 112464811 A CN112464811 A CN 112464811A CN 202011353478 A CN202011353478 A CN 202011353478A CN 112464811 A CN112464811 A CN 112464811A
- Authority
- CN
- China
- Prior art keywords
- signal
- singular value
- random noise
- runout
- storage unit
- 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.)
- Pending
Links
- 238000001914 filtration Methods 0.000 title claims abstract description 33
- 238000003860 storage Methods 0.000 title claims abstract description 33
- 238000000034 method Methods 0.000 title claims abstract description 20
- 239000011159 matrix material Substances 0.000 claims abstract description 53
- 238000012544 monitoring process Methods 0.000 claims abstract description 41
- 230000010355 oscillation Effects 0.000 claims abstract description 18
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 13
- 238000012216 screening Methods 0.000 claims abstract description 9
- 238000010276 construction Methods 0.000 claims abstract description 7
- 238000005311 autocorrelation function Methods 0.000 claims abstract description 4
- 108010076504 Protein Sorting Signals Proteins 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000005314 correlation function Methods 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 claims description 3
- 238000004146 energy storage Methods 0.000 description 5
- 238000005086 pumping Methods 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 238000011156 evaluation Methods 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000011946 reduction process Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000033228 biological regulation Effects 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000010977 unit operation Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention relates to the field of hydroelectric generating sets of power systems, and discloses a method for accurately filtering high-frequency random noise in a pumped storage unit runout signal, which comprises the following steps: 1) constructing a Hankel matrix of the unit runout monitoring signal, and obtaining a diagonal matrix containing a singular value sequence after singular value decomposition; 2) based on a Hankel matrix construction principle, signal components related to a single singular value are inverted in sequence; 3) respectively calculating the autocorrelation function of each signal component and the original oscillation monitoring signal, and gradually calculating the correlation coefficient between the signal components and the original oscillation monitoring signal; 4) and screening a threshold value according to a preset correlation coefficient, filtering high-frequency random noise sequence components, and reconstructing to obtain effective oscillation monitoring signal components. The invention can meet the requirement that the pumped storage unit accurately reduces the noise of the oscillation monitoring signal under the operation of a strong background noise environment, obtains a relatively pure oscillation signal, and provides a reliable data basis for analyzing and evaluating the state of the unit and improving the safe and stable operation performance of the unit.
Description
Technical Field
The invention relates to the field of hydroelectric generating sets of power systems, in particular to a method for accurately filtering high-frequency random noise in a pumped storage unit runout signal.
Background
Along with the rapid development of economy and society, the power load is rapidly increased, the peak-to-valley difference is continuously increased, the power grid has higher requirements on stability, and the insufficient peak regulation capability becomes a key problem restricting the development of a power system. The pumped storage power station is taken as a universally recognized peak shaving power supply which is flexible and reliable in operation, plays the functions of adjusting load, promoting energy conservation of a power system and maintaining safe and stable operation of a power grid by virtue of the unique operation characteristics of peak shaving and valley filling of the pumped storage power station, and gradually becomes an effective and indispensable adjusting means of the power system. The oscillation monitoring signal which can be used for analyzing and evaluating the state of the unit is easily submerged by strong high-frequency random noise under the influence of various factors such as the operating environment of the pumped storage unit, measuring equipment, local impact and the like. In order to effectively monitor the running state of the unit and ensure the safe and stable running of the unit, the research of a feasible and effective method for accurately filtering the high-frequency random noise of the oscillation signal of the pumped storage unit becomes an important work in the current improvement of the state analysis performance of the pumped storage unit.
The existing signal denoising method is mainly based on algorithm theories such as blind signal separation, wavelet transformation, empirical mode decomposition and the like, and is easily limited by factors such as source number, wavelet type and threshold selection, mode aliasing and the like in practical application; meanwhile, aiming at high-frequency random noise components contained in the vibration signals, the noise reduction algorithm is properly tried in the aspect of noise filtering, but the problem of insufficient denoising performance generally exists. At present, for the problem of accurately filtering high-frequency random noise in the pumping energy storage unit runout signal, related research is not deeply and systematically carried out, a scientific and effective pumping energy storage unit runout signal high-frequency random noise accurate filtering theoretical system is lacked, and the functional requirement for accurately analyzing and evaluating the unit running state cannot be met.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the prior art, the invention provides a method for accurately filtering high-frequency random noise in a pumped storage unit runout signal, the idea of high-frequency random noise self-adaption accurate filtering is introduced into the field of noise reduction of the pumped storage unit runout signal, the influence mechanism of random noise characteristics on monitoring signals is comprehensively considered, and the limitation that the noise reduction process is excessively dependent on artificial experience and priori knowledge is overcome.
The technical scheme is as follows: the invention provides a method for accurately filtering high-frequency random noise in a pumped storage unit runout signal, which comprises the following steps:
step 1: constructing a Hankel matrix of an original runout monitoring signal of the pumped storage unit, and performing singular value decomposition on the Hankel matrix to obtain a diagonal matrix containing a singular value sequence;
step 2: based on a Hankel matrix construction principle, sequentially inverting to obtain each signal component related to a single singular value;
and step 3: respectively calculating the autocorrelation function of each signal component and the original oscillation monitoring signal in the step 2, and solving the correlation coefficient of each signal component and the original oscillation monitoring signal one by one;
and 4, step 4: and screening a threshold value according to a preset correlation coefficient, filtering high-frequency random noise sequence components, updating a singular value sequence, and reconstructing to obtain effective oscillation monitoring signal components.
Further, the step 1 comprises the following sub-steps:
1-1: acquiring a unit runout monitoring signal sequence X based on a pumped storage unit online monitoring and production real-time systemS；
1-2: according to the phase space reconstruction principle, a unit runout monitoring signal X is constructedSCorresponding Hankel matrix HS；
1-3: for the constructed Hankel matrix HSSingular value decomposition is carried out, and a diagonal matrix Lambda ═ diag (Lambda) containing singular value sequences is obtained1,λ2,…,λr) Wherein r represents a matrix HSIs determined.
Further, the step 2 comprises the following sub-steps:
2-1: sequentially taking singular values lambda in diagonal matrix lambdaiLet λ bejConstruct a diagonal matrix Λ containing only single singular values ≠ i ═ 0, j ≠ ii′＝diag(0,…,λi,…,0)；
2-2: according to the constructed diagonal matrix Lambdai', calculating the corresponding Hankel matrix HSi′：
HSi′＝PΛi′Q
Wherein P, Q denotes a Hankel matrix HSObtaining an orthogonal matrix after singular value decomposition;
2-3: based on Hankel matrix construction principle, single singular value lambda related to is obtained through matrix inversioniSignal component X ofSi(i＝1,2,…,r)。
Further, the step 3 comprises the following sub-steps:
3-1: respectively calculating each of the correlations to a single singular value lambdaiSignal component X ofSi(i-1, 2, …, r) and the original runout monitoring signal XSIs self-correlation function of
The calculation formula is as follows:
wherein, M represents the length of the signal sequence, tau is a time delay coefficient, k is 1,2, 3, M-1;
3-2: calculating the signal components X one by oneSiWith the original runout monitoring signal XSCorrelation coefficient ofi：
Further, the step 4 comprises the following sub-steps:
4-1: presetting a screening threshold alpha of a high-frequency random noise correlation coefficient of a unit runout signal;
4-2: if deltaiIf the component is more than or equal to alpha, the singular value corresponding to the component is reserved; otherwise, setting the singular value corresponding to the component to 0, and characterizing and filtering the high-frequency random noise sequence component;
4-3: obtaining effective runout monitoring signal component X through reconstruction based on updated singular value sequenceS′。
Has the advantages that:
1. the invention introduces the thought of high-frequency random noise self-adaptive accurate filtering into the field of pumped storage unit runout signal noise reduction for the first time, comprehensively considers the influence mechanism of random noise characteristics on monitoring signals, overcomes the limitation that the noise reduction process excessively depends on artificial experience and priori knowledge, fills the blank that no scientific theoretical basis and technical guidance exist yet for accurate filtering of the high-frequency random noise of the unit runout signals, effectively improves the extraction precision of the unit pure runout monitoring signals, improves the reliability of unit state analysis and evaluation, and ensures safe, reliable and efficient operation of a power station.
2. The method provided by the invention firstly considers that the singular value decomposition method is adopted to complete the decomposition of the original oscillation monitoring signal sequence of the pumped storage unit, and the screening and filtering of the high-frequency random noise sequence component are realized based on the single signal component inversion and the correlation analysis theory, so that the internal rule of the unit running state evolution is fully captured, the filtering precision of the high-frequency random noise in the oscillation signal of the unit is obviously improved, the technical problem of noise interference existing in the unit state evaluation is solved, the evaluation level of the unit state change is effectively improved, and reliable theoretical guidance and technical support are provided for the intelligent state maintenance of the pumped storage unit.
Drawings
FIG. 1 is a flow chart of a method for accurately filtering high-frequency random noise in a pumped storage unit runout signal according to the present invention;
FIG. 2 is a variation curve of a pure vibration signal sequence of the pumped storage unit according to the embodiment of the present invention;
FIG. 3 is a high-frequency random noise sequence variation curve in a vibration signal of a pumped storage group according to an embodiment of the present invention;
FIG. 4 is a sequence variation curve of a pure vibration signal of the pumped storage unit after noise processing in the embodiment of the present invention;
fig. 5 is a high-frequency random noise filtering result curve in a vibration signal of the pumped storage group in the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention takes filtering of high-frequency random noise in a pumping energy storage unit runout signal of a certain domestic power station as an analysis case, and fig. 1 is a flow chart of a method for accurately filtering the high-frequency random noise in the pumping energy storage unit runout signal, which is provided by the invention and specifically comprises the following steps:
step 1: and constructing a Hankel matrix of the original runout monitoring signal of the pumped storage unit, and performing singular value decomposition to obtain a diagonal matrix containing a singular value sequence.
1-1: acquiring a unit runout monitoring signal sequence X based on a pumped storage unit online monitoring and production real-time systemS. The method is characterized in that high-frequency random noise in a pumping energy storage unit vibration signal of a certain domestic power station is filtered to serve as an analysis case, and a unit pure vibration signal and high-frequency random noise are obtained through simulation according to the unit operation environment, structural parameters and vibration characteristic frequency. Fig. 2 shows a sequence variation curve of the unit pure vibration signal, fig. 3 shows a sequence variation curve of the high-frequency random noise sequence, fig. 4 shows a sequence variation curve of the unit pure vibration signal after noise processing, and the sequence length M in the graph is 1026 sampling data points.
1-2: according to the phase space reconstruction principle, a unit runout monitoring signal X is constructedSCorresponding Hankel matrix HS。
1-3: for the constructed Hankel matrix HSSingular value decomposition is carried out to obtain pairs containing singular value sequencesAngle matrix Λ ═ diag (λ)1,λ2,…,λr) Wherein r represents a matrix HSIs determined.
From the above process, the calculated diagonal matrix Λ has the form, where r 513:
step 2: based on the Hankel matrix construction principle, signal components related to a single singular value are obtained through inversion in sequence.
2-1: sequentially taking singular values lambda in diagonal matrix lambdaiLet λ bej0(j ≠ i), constructing a diagonal matrix Λ that contains only single singular valuesi′＝diag(0,…,λi,…,0)；
E.g. for singular values lambda in the diagonal matrix lambda1A diagonal matrix Lambda containing single singular values may be constructed1′：
2-2: according to the constructed diagonal matrix Λ'iCalculating the corresponding Hankel matrix HSi′：
HSi′＝PΛi′Q
Wherein P, Q denotes a Hankel matrix HSAnd obtaining an orthogonal matrix after singular value decomposition.
2-3: based on Hankel matrix construction principle, single singular value lambda related to is obtained through matrix inversioniSignal component X ofSi(i＝1,2,…,r)。
And step 3: and respectively calculating the autocorrelation function of each signal component and the original oscillation monitoring signal, and on the basis, calculating the correlation coefficient of each signal component and the oscillation monitoring signal one by one.
3-1: calculating each signal component X separatelySi(i-1, 2, …, r) and the original runout monitoring signal XSIs self-correlation function of
Wherein, M represents the length of the signal sequence, τ is the delay coefficient, k is 1,2, 3, M-1.
3-2: one by one, obtaining each signal component XSiWith the original runout monitoring signal XSCorrelation coefficient ofi：
And 4, step 4: and screening a threshold value according to a preset correlation coefficient, filtering high-frequency random noise sequence components, and reconstructing to obtain effective oscillation monitoring signal components.
4-1: and determining a screening threshold alpha of the high-frequency random noise correlation coefficient of the unit runout signal according to numerical calculation or expert experience. In the embodiment of the invention, the screening threshold value of the high-frequency random noise correlation coefficient of the unit runout signal can be determined as follows: α is 0.1.
4-2: automatic discrimination conditions: deltai≥α；
If the condition is not met, setting the singular value corresponding to the component to be 0, and characterizing and filtering the high-frequency random noise sequence component;
if the condition is satisfied, the singular value corresponding to this component is retained.
Based on the above determination condition, the signal component X in step 3-2SiWith the original runout monitoring signal XSCorrelation coefficient ofiAs a result of the calculation, in the present embodiment, λ in Λ is defined as1～λ12Inverting the resulting signal component with XSCorrelation coefficient of1～δ12The values of the conditions satisfying the automatic discrimination conditions are distributed as shown in table 1:
TABLE 1 correlation coefficient value distribution
Retention of lambda in lambda1～λ12Will be λ13～λ513And setting 0, and updating the lambda to obtain an updated singular value sequence.
4-3: obtaining effective runout monitoring signal component X through reconstruction based on updated singular value sequenceS′。
Fig. 5 shows a high-frequency random noise filtering result curve in a vibration signal of the pumped storage group. According to the graphic result, the finally obtained signal curve can well fit the original pure vibration signal sequence change curve of the unit, and the effectiveness of the high-frequency random noise precise filtering method is fully verified.
The above embodiments are merely illustrative of the technical concepts and features of the present invention, and the purpose of the embodiments is to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the protection scope of the present invention. All equivalent changes and modifications made according to the spirit of the present invention should be covered within the protection scope of the present invention.
Claims (5)
1. A method for accurately filtering high-frequency random noise in a pumped storage unit runout signal is characterized by comprising the following steps:
step 1: constructing a Hankel matrix of an original runout monitoring signal of the pumped storage unit, and performing singular value decomposition on the Hankel matrix to obtain a diagonal matrix containing a singular value sequence;
step 2: based on a Hankel matrix construction principle, sequentially inverting to obtain each signal component related to a single singular value;
and step 3: respectively calculating the autocorrelation function of each signal component and the original oscillation monitoring signal in the step 2, and solving the correlation coefficient of each signal component and the original oscillation monitoring signal one by one;
and 4, step 4: and screening a threshold value according to a preset correlation coefficient, filtering high-frequency random noise sequence components, updating a singular value sequence, and reconstructing to obtain effective oscillation monitoring signal components.
2. The method for accurately filtering high-frequency random noise in pumped storage unit runout signals according to claim 1, wherein the step 1 comprises the following substeps:
1-1: acquiring a unit runout monitoring signal sequence X based on a pumped storage unit online monitoring and production real-time systemS；
1-2: according to the phase space reconstruction principle, a unit runout monitoring signal X is constructedSCorresponding Hankel matrix HS；
1-3: for the constructed Hankel matrix HSSingular value decomposition is carried out, and a diagonal matrix Lambda ═ diag (Lambda) containing singular value sequences is obtained1,λ2,…,λr) Wherein r represents a matrix HSIs determined.
3. The method for accurately filtering high-frequency random noise in the pumped storage unit runout signal according to claim 2, wherein the step 2 comprises the following substeps:
2-1: sequentially taking singular values lambda in diagonal matrix lambdaiLet λ bej0, j ≠ i, constructing a diagonal matrix Λ 'containing only single singular values'i＝diag(0,…,λi,…,0)；
2-2: according to the constructed diagonal matrix Λ'iCalculating the corresponding Hankel matrix HSi′：
HSi′＝PΛi′Q
Wherein P, Q denotes a Hankel matrix HSObtaining an orthogonal matrix after singular value decomposition;
2-3: based on Hankel matrix construction principle, obtaining lambda related to single singular value through Hankel matrix inversioniSignal component X ofSi(i＝1,2,…,r)。
4. The method for accurately filtering high-frequency random noise in pumped storage unit runout signals according to claim 3, wherein the step 3 comprises the following substeps:
3-1: respectively calculating each of the correlations to a single singular value lambdaiSignal component X ofSi(i-1, 2, …, r) and the original runout monitoring signal XSIs self-correlation function of
The calculation formula is as follows:
wherein, M represents the length of the signal sequence, tau is a time delay coefficient, k is 1,2, 3, M-1;
3-2: calculating the signal components X one by oneSiWith the original runout monitoring signal XSCorrelation coefficient ofi：
5. The method for accurately filtering high-frequency random noise in the pumped storage unit runout signal according to claim 4, wherein the step 4 comprises the following substeps:
4-1: presetting a screening threshold alpha of a high-frequency random noise correlation coefficient of a unit runout signal;
4-2: if deltaiIf the component is more than or equal to alpha, the singular value corresponding to the component is reserved; otherwise, setting the singular value corresponding to the component to 0, and characterizing and filtering the high-frequency random noise sequence component;
4-3: obtaining effective runout monitoring signal component X through reconstruction based on updated singular value sequenceS′。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011353478.4A CN112464811A (en) | 2020-11-26 | 2020-11-26 | Method for accurately filtering high-frequency random noise in pumped storage unit runout signal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011353478.4A CN112464811A (en) | 2020-11-26 | 2020-11-26 | Method for accurately filtering high-frequency random noise in pumped storage unit runout signal |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112464811A true CN112464811A (en) | 2021-03-09 |
Family
ID=74808694
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011353478.4A Pending CN112464811A (en) | 2020-11-26 | 2020-11-26 | Method for accurately filtering high-frequency random noise in pumped storage unit runout signal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112464811A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115165081A (en) * | 2022-07-29 | 2022-10-11 | 东北大学 | System and method for mining machinery vibration signal acquisition and working condition identification |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020126834A1 (en) * | 2001-01-11 | 2002-09-12 | Michael Seibert | Double-talk and path change detection using a matrix of correlation coefficients |
| CN101876546A (en) * | 2009-11-27 | 2010-11-03 | 北京航空航天大学 | MEMS (Micro Electronic Mechanical System) gyro data processing method based on wavelet threshold de-noising and FAR (Finite Automaton Recognizable) model |
| US20110077886A1 (en) * | 2009-09-30 | 2011-03-31 | Electronics And Telecommunications Research Institute | System and method of selecting white gaussian noise sub-band using singular value decomposition |
| CN106446829A (en) * | 2016-09-22 | 2017-02-22 | 三峡大学 | Hydroelectric generating set vibration signal noise reduction method based on mode autocorrelation analysis of SVD and VMD |
| CN107192553A (en) * | 2017-06-28 | 2017-09-22 | 石家庄铁道大学 | Gear-box combined failure diagnostic method based on blind source separating |
| JP2017201462A (en) * | 2016-05-02 | 2017-11-09 | キヤノン株式会社 | Image processing apparatus and image processing method |
| CN109187023A (en) * | 2018-09-04 | 2019-01-11 | 温州大学激光与光电智能制造研究院 | A kind of automobile current generator bearing method for diagnosing faults |
| CN109472288A (en) * | 2018-10-08 | 2019-03-15 | 华中科技大学 | A kind of pump-storage generator vibration hybrid feature extraction and classification method |
| CN109541455A (en) * | 2018-12-03 | 2019-03-29 | 国网江苏省电力有限公司南京供电分公司 | A kind of OLTC impact characteristics extracting method based on S-transformation time-frequency spectrum SVD noise reduction |
| CN110057317A (en) * | 2018-11-16 | 2019-07-26 | 中山大学 | A kind of projection noise removing method constructed based on singular value decomposition and look-up table |
| CN110503060A (en) * | 2019-08-28 | 2019-11-26 | 中南大学 | A kind of spectral signal denoising method and its system |
| CN111947045A (en) * | 2020-08-24 | 2020-11-17 | 重庆邮电大学 | GVMD parameter optimization and singular value decomposition-based fluid pipeline leakage positioning method |
-
2020
- 2020-11-26 CN CN202011353478.4A patent/CN112464811A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020126834A1 (en) * | 2001-01-11 | 2002-09-12 | Michael Seibert | Double-talk and path change detection using a matrix of correlation coefficients |
| US20110077886A1 (en) * | 2009-09-30 | 2011-03-31 | Electronics And Telecommunications Research Institute | System and method of selecting white gaussian noise sub-band using singular value decomposition |
| CN101876546A (en) * | 2009-11-27 | 2010-11-03 | 北京航空航天大学 | MEMS (Micro Electronic Mechanical System) gyro data processing method based on wavelet threshold de-noising and FAR (Finite Automaton Recognizable) model |
| JP2017201462A (en) * | 2016-05-02 | 2017-11-09 | キヤノン株式会社 | Image processing apparatus and image processing method |
| CN106446829A (en) * | 2016-09-22 | 2017-02-22 | 三峡大学 | Hydroelectric generating set vibration signal noise reduction method based on mode autocorrelation analysis of SVD and VMD |
| CN107192553A (en) * | 2017-06-28 | 2017-09-22 | 石家庄铁道大学 | Gear-box combined failure diagnostic method based on blind source separating |
| CN109187023A (en) * | 2018-09-04 | 2019-01-11 | 温州大学激光与光电智能制造研究院 | A kind of automobile current generator bearing method for diagnosing faults |
| CN109472288A (en) * | 2018-10-08 | 2019-03-15 | 华中科技大学 | A kind of pump-storage generator vibration hybrid feature extraction and classification method |
| CN110057317A (en) * | 2018-11-16 | 2019-07-26 | 中山大学 | A kind of projection noise removing method constructed based on singular value decomposition and look-up table |
| CN109541455A (en) * | 2018-12-03 | 2019-03-29 | 国网江苏省电力有限公司南京供电分公司 | A kind of OLTC impact characteristics extracting method based on S-transformation time-frequency spectrum SVD noise reduction |
| CN110503060A (en) * | 2019-08-28 | 2019-11-26 | 中南大学 | A kind of spectral signal denoising method and its system |
| CN111947045A (en) * | 2020-08-24 | 2020-11-17 | 重庆邮电大学 | GVMD parameter optimization and singular value decomposition-based fluid pipeline leakage positioning method |
Non-Patent Citations (6)
| Title |
|---|
| TIANXU ZHU等: "Noise Reduction for Modal Parameter Identification of the Measured FRFs Using the Modal Peak-Based Hankel-SVD Method", 《SHOCK AND VIBRATION》, pages 1 - 21 * |
| ZIJIAN QIAO等: "SVD principle analysis and fault diagnosis for bearings based on the correlation coefficient", 《MEASUREMENT SCIENCE AND TECHNOLOGY》, vol. 26, no. 8, pages 1 - 15 * |
| 刘东等: "基于 SVD 的水电机组振动信号去噪方法研究", 《水电与新能源》, vol. 32, no. 7, pages 38 - 43 * |
| 张建伟等: "基于SVD与改进EMD联合的泄流结构工作特性信息提取", 《应用基础与工程科学学报》, vol. 24, no. 4, pages 698 - 711 * |
| 沈微等: "基于同步挤压小波变换的振动信号自适应降噪方法", 《振动与冲击》, vol. 37, no. 14, pages 239 - 247 * |
| 潘峥嵘等: "基于相关系数的SVD旋转机械振动信号消噪方法", 《第十三届全国敏感元件与传感器学术会议》, pages 88 - 92 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115165081A (en) * | 2022-07-29 | 2022-10-11 | 东北大学 | System and method for mining machinery vibration signal acquisition and working condition identification |
| CN115165081B (en) * | 2022-07-29 | 2024-10-11 | 东北大学 | Mining machinery vibration signal acquisition and working condition identification system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR101958674B1 (en) | Actually-measured marine environment data assimilation method based on sequence recursive filtering three-dimensional variation | |
| CN107590317B (en) | Generator dynamic estimation method considering model parameter uncertainty | |
| CN110659693B (en) | K-nearest neighbor classification-based power distribution network rapid topology identification method, system and medium | |
| CN108074015B (en) | Ultra-short-term prediction method and system for wind power | |
| CN107478990A (en) | A kind of generator electromechanical transient process method for dynamic estimation | |
| CN110212592B (en) | Thermal power generating unit load regulation maximum rate estimation method and system based on piecewise linear expression | |
| CN112649196B (en) | Signal variation modal decomposition preset scale parameter selection method based on frequency domain information | |
| CN108334704A (en) | Structure damage monitoring method based on density self-adapting peak value-mixing probabilistic Modeling | |
| CN105043384A (en) | Modeling method of gyroscopic random noise ARMA model based on robust Kalman wave filtering | |
| CN104807534A (en) | Equipment natural vibration mode self-learning recognition method based on online vibration data | |
| CN111046327A (en) | Prony analysis method suitable for low-frequency oscillation and subsynchronous oscillation identification | |
| CN109214318B (en) | Method for searching weak peak of unsteady time sequence | |
| CN109861250A (en) | A kind of oscillation of power type identification method based on electric system multidimensional characteristic | |
| CN106228026A (en) | A kind of predicting residual useful life algorithm based on optimum degenerative character amount | |
| CN114755010A (en) | Rotary machine vibration fault diagnosis method and system | |
| CN112464811A (en) | Method for accurately filtering high-frequency random noise in pumped storage unit runout signal | |
| Korenberg | Fast orthogonal algorithms for nonlinear system identification and time-series analysis | |
| CN111241749B (en) | Permanent magnet synchronous motor chaos prediction method based on reserve pool calculation | |
| CN106226752B (en) | A method of extension sea clutter Pareto estimation of distribution parameters range | |
| CN106683001A (en) | Thermal power plant set identification data selection method based on historical operation data | |
| CN117390508A (en) | Hydroelectric generating set signal state identification method based on time-shifting multi-scale cosine similarity entropy | |
| CN112668183B (en) | Rain flow counting processing method for long-time course vibration signal | |
| CN106153046A (en) | A kind of Gyro Random noise AR modeling method based on adaptable Kalman filter | |
| CN104469795A (en) | FARIMA model Hurst parameter estimation method based on domain searching | |
| CN114117349A (en) | Power system random variable probability analysis method, system, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |