CN110426569B - Noise reduction processing method for acoustic signals of transformer - Google Patents
Noise reduction processing method for acoustic signals of transformer Download PDFInfo
- Publication number
- CN110426569B CN110426569B CN201910631576.0A CN201910631576A CN110426569B CN 110426569 B CN110426569 B CN 110426569B CN 201910631576 A CN201910631576 A CN 201910631576A CN 110426569 B CN110426569 B CN 110426569B
- Authority
- CN
- China
- Prior art keywords
- signal
- transformer
- frequency component
- signals
- acoustic
- 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.)
- Active
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 22
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 48
- 238000000034 method Methods 0.000 claims abstract description 23
- 238000001914 filtration Methods 0.000 claims abstract description 21
- 238000004458 analytical method Methods 0.000 claims abstract description 20
- 230000005236 sound signal Effects 0.000 claims abstract description 20
- 230000007613 environmental effect Effects 0.000 claims abstract description 8
- 230000000694 effects Effects 0.000 claims abstract description 7
- 238000005259 measurement Methods 0.000 claims abstract description 6
- 238000000926 separation method Methods 0.000 claims description 18
- 238000009432 framing Methods 0.000 claims description 9
- 238000012544 monitoring process Methods 0.000 abstract description 7
- 238000001514 detection method Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 4
- 238000007689 inspection Methods 0.000 description 2
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical group [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 1
- 238000003331 infrared imaging Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000013024 troubleshooting Methods 0.000 description 1
- 238000011179 visual inspection Methods 0.000 description 1
- 238000004804 winding Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H1/00—Measuring characteristics of vibrations in solids by using direct conduction to the detector
- G01H1/12—Measuring characteristics of vibrations in solids by using direct conduction to the detector of longitudinal or not specified vibrations
- G01H1/14—Frequency
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1209—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing using acoustic measurements
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0208—Noise filtering
-
- 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
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Acoustics & Sound (AREA)
- Signal Processing (AREA)
- Quality & Reliability (AREA)
- Computational Linguistics (AREA)
- Health & Medical Sciences (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Human Computer Interaction (AREA)
- Multimedia (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
- Soundproofing, Sound Blocking, And Sound Damping (AREA)
Abstract
A noise reduction processing method for a transformer acoustic signal belongs to the monitoring field. Based on multiple paths of measurement signals, separating out a sound signal of the running state of the transformer by a sparse component analysis algorithm; decomposing the signal into four layers of intrinsic mode functions by using variational mode decomposition; filtering the intrinsic mode function component to obtain a new intrinsic mode function component; reconstructing it to obtain the final de-noised signal. The method has the advantages that the sparse component analysis algorithm and the variational modal decomposition based on the potential function are used for denoising the collected signals of the acoustic sensor, the operating acoustic signals and the environmental noise of the transformer substation can be effectively separated, and the denoising effect is good. The method can be widely applied to the field of operation monitoring of the unattended transformer substation.
Description
Technical Field
The invention belongs to the field of monitoring, and particularly relates to a noise reduction processing method for a transformer acoustic signal.
Background
As one of the most important electrical devices in an electrical power system, a power transformer needs to be ensured in a reliable operation state.
At present, the troubleshooting of the transformer mainly depends on regular polling and visual inspection by special polling personnel.
The inspection mode consumes a large amount of manpower, has strong subjectivity, and the transformer fault often cannot be fed back and solved in real time due to periodic inspection, thereby finally causing huge property loss to the national power system.
When the transformer is in operation, the iron core and the winding generate vibration, and vibration sound signals which are radiated and mixed around are transmitted through the oil and the box body. In addition, when partial discharge occurs in the transformer, an acoustic signal is often emitted. Therefore, the vibration sound signal of the transformer contains abundant information of the running state of the transformer, and can be used as a basis for judging the running state of the transformer.
The traditional transformer operation state detection method comprises vibration signal detection, ultrasonic detection, infrared imaging monitoring and the like. Compared with the traditional method, the sound signal detection method has no direct contact with a power system and is easy to collect, and meanwhile compared with the method of locally mounting a sensor on the transformer to collect signals, the sound signal detection method can collect audio generated by the integral vibration of the transformer, and is generally due to the vibration signal detection method. Therefore, the transformer fault detection by the acoustic signal is feasible and reliable.
However, the acoustic sensor collects the acoustic signal of the operating state of the transformer, and also has corona acoustic signals, environmental noise and the like, so that effective operating state information is submerged in various interferences and is difficult to perform subsequent processing. Therefore, how to effectively extract the acoustic signal of the transformer becomes the key for accurately judging the fault of the transformer in the follow-up process.
Disclosure of Invention
The invention aims to solve the technical problem of providing a transformer acoustic signal noise reduction processing method. The method carries out denoising processing on the signals acquired by the acoustic sensor through a sparse component analysis algorithm and variational modal decomposition based on a potential function. Firstly, separating out the sound signals of the running state of the transformer by a sparse component analysis algorithm, decomposing the signals into four layers of intrinsic mode functions by using variational mode decomposition, filtering the intrinsic mode function components to obtain new intrinsic mode function components, and reconstructing the new intrinsic mode function components to obtain the final noise-removed signals. The method can effectively separate the operation sound signal and the environmental noise of the transformer substation, and has good denoising effect.
The technical scheme of the invention is as follows: the method for noise reduction processing of the acoustic signal of the transformer comprises the field acquisition of the acoustic signal of the running state of the transformer, and is characterized in that:
1) separating out the sound signals of the running state of the transformer by a sparse component analysis algorithm;
2) decomposing the signal into four layers of intrinsic mode functions by using variational mode decomposition;
3) filtering the intrinsic mode function component to obtain a new intrinsic mode function component;
4) reconstructing it to obtain the final de-noised signal.
Specifically, the method for noise reduction processing of the acoustic signal of the transformer is based on a plurality of paths of measurement signals, and the processing process mainly comprises the following steps:
1) after windowing and framing through a Hamming window, carrying out short-time Fourier transform on the sound signal to enable the sound signal to have frequency domain sparse characteristics;
2) obtaining a preliminary de-noising signal through underdetermined blind source separation;
3) further carrying out four-layer eigenmode function decomposition on the signals by a variation mode decomposition method;
4) carrying out 6-layer wavelet decomposition on the high-frequency component and the low-frequency component of the intrinsic mode function, and carrying out filtering processing on the high-frequency component and the low-frequency component of the intrinsic mode function by using a 3 sigma principle;
5) and performing signal reconstruction according to the high-frequency component, the low-frequency component and the middle-frequency component of the new eigenmode function.
Furthermore, the transformer acoustic signal noise reduction processing method is characterized in that after windowing and framing through a Hamming window, short-time Fourier transform is carried out on the acoustic signal to enable the acoustic signal to have frequency domain sparse characteristics; then, obtaining a preliminary de-noising signal through underdetermined blind source separation; then, further carrying out four-layer eigenmode function decomposition on the signals by a variation mode decomposition method; carrying out 6-layer wavelet decomposition on the high-frequency component and the low-frequency component of the intrinsic mode function, and carrying out filtering processing on the high-frequency component and the low-frequency component of the intrinsic mode function by using a 3 sigma principle; and finally, performing signal reconstruction according to the high-frequency component, the low-frequency component and the middle-frequency component of the new eigenmode function.
According to the transformer acoustic signal noise reduction processing method, the noise reduction processing is performed on the acoustic sensor acquisition signals through the sparse component analysis algorithm and the variational modal decomposition based on the potential function, the transformer substation operation acoustic signals and the environmental noise can be effectively separated, and the noise reduction effect is good.
According to the transformer acoustic signal noise reduction processing method, noise reduction processing is carried out on the acquired signals of the acoustic sensor through a sparse component analysis algorithm and variational modal decomposition based on a potential function.
Specifically, the transformer acoustic signal noise reduction processing method includes the steps of firstly separating out an acoustic signal in a transformer running state through a sparse component analysis algorithm, decomposing the signal into four layers of intrinsic mode functions through variational mode decomposition, obtaining new intrinsic mode function components through filtering the intrinsic mode function components, and reconstructing the new intrinsic mode function components to obtain a final noise removal signal.
Furthermore, the transformer acoustic signal noise reduction processing method comprises the steps of firstly obtaining multiple paths of acoustic signals, windowing and framing the acoustic signals through a Hamming window, and then carrying out short-time Fourier transform to enable the acoustic signals to have frequency domain sparse characteristics; then obtaining a multi-channel output signal through an SCA separation unit based on an underdetermined blind source separation strategy: split signal 1, split signal 2, split signal 3, split signal 4; then, performing variation modal decomposition on the separation signal 1 through a VMD decomposition unit to obtain a high-frequency component IMF1, a low-frequency component IMF4, a medium-frequency component IMF2 and an IMF 3; the high-frequency component IMF1 and the low-frequency component IMF4 are processed by a wavelet decomposition unit and a 3 sigma threshold filtering unit respectively, and finally the filtered signals, the intermediate-frequency components IMF2 and IMF3 are subjected to signal reconstruction to reconstruct the transformer acoustic signal through a signal reconstruction unit.
Compared with the prior art, the invention has the advantages that:
and denoising the acquired signals of the acoustic sensor by a sparse component analysis algorithm and variational modal decomposition based on a potential function. Firstly, separating out the sound signals of the running state of the transformer by a sparse component analysis algorithm, decomposing the signals into four layers of intrinsic mode functions by using variational mode decomposition, filtering the intrinsic mode function components to obtain new intrinsic mode function components, and reconstructing the new intrinsic mode function components to obtain the final noise-removed signals. The method can effectively separate the operation sound signal and the environmental noise of the transformer substation, and has good denoising effect.
Drawings
FIG. 1 is a flow chart of a transformer acoustic signal noise reduction processing method of the present invention;
FIG. 2 is a schematic diagram of a monitoring input signal of a multi-path noisy transformer according to the present invention;
FIG. 3 is a schematic diagram of blind source separation based on sparse component analysis algorithm of the present invention;
FIG. 4 is a waveform diagram of an isolated source signal reconstructed after eigenmode function component filtering according to the present invention;
fig. 5 is a waveform diagram of a noise reduction output signal obtained by reconstructing low, medium and high frequency signals according to the present invention.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
The technical scheme of the invention provides a transformer acoustic signal noise reduction processing method, which comprises the steps of carrying out field acquisition on an acoustic signal in a transformer running state and based on obtained multi-channel measurement signals, and is characterized in that:
1) separating out the sound signals of the running state of the transformer by a sparse component analysis algorithm;
2) decomposing the signal into four layers of intrinsic mode functions by using variational mode decomposition;
3) filtering the intrinsic mode function component to obtain a new intrinsic mode function component;
4) reconstructing it to obtain the final noise-removed signal.
Specifically, the method for noise reduction processing of the acoustic signal of the transformer is based on a plurality of paths of measurement signals, and the processing process mainly comprises the following steps:
1) after windowing and framing through a Hamming window, carrying out short-time Fourier transform on the sound signal to enable the sound signal to have frequency domain sparse characteristics;
2) obtaining a preliminary de-noising signal through underdetermined blind source separation;
3) further carrying out four-layer eigenmode function decomposition on the signals by a variation mode decomposition method;
4) carrying out 6-layer wavelet decomposition on the high-frequency component and the low-frequency component of the intrinsic mode function, and carrying out filtering processing on the high-frequency component and the low-frequency component of the intrinsic mode function by using a 3 sigma principle;
5) and performing signal reconstruction according to the high-frequency component, the low-frequency component and the middle-frequency component of the new eigenmode function.
Furthermore, the transformer acoustic signal noise reduction processing method is characterized in that after windowing and framing through a Hamming window, short-time Fourier transform is carried out on the acoustic signal to enable the acoustic signal to have frequency domain sparse characteristics; then, obtaining a preliminary de-noising signal through underdetermined blind source separation; then, further carrying out four-layer eigenmode function decomposition on the signals by a variation mode decomposition method; carrying out 6-layer wavelet decomposition on the high-frequency component and the low-frequency component of the intrinsic mode function, and carrying out filtering processing on the high-frequency component and the low-frequency component of the intrinsic mode function by using a 3 sigma principle; and finally, performing signal reconstruction according to the high-frequency component, the low-frequency component and the middle-frequency component of the new eigenmode function.
According to the transformer acoustic signal noise reduction processing method, the noise reduction processing is performed on the acoustic sensor acquisition signals through the sparse component analysis algorithm and the variational modal decomposition based on the potential function, the transformer substation operation acoustic signals and the environmental noise can be effectively separated, and the noise reduction effect is good.
According to the transformer acoustic signal noise reduction processing method, noise reduction processing is carried out on the acquired signals of the acoustic sensor through a sparse component analysis algorithm and variational modal decomposition based on a potential function.
Specifically, the transformer acoustic signal noise reduction processing method includes the steps of firstly separating out an acoustic signal in a transformer running state through a sparse component analysis algorithm, decomposing the signal into four layers of intrinsic mode functions through variational mode decomposition, obtaining new intrinsic mode function components through filtering the intrinsic mode function components, and reconstructing the new intrinsic mode function components to obtain a final noise removal signal.
Furthermore, the transformer acoustic signal noise reduction processing method comprises the steps of firstly obtaining multiple paths of acoustic signals, windowing and framing the acoustic signals through a Hamming window, and then carrying out short-time Fourier transform to enable the acoustic signals to have frequency domain sparse characteristics; then obtaining a multi-channel output signal through an SCA separation unit based on an underdetermined blind source separation strategy: split signal 1, split signal 2, split signal 3, split signal 4; then, performing variation modal decomposition on the separation signal 1 through a VMD decomposition unit to obtain a high-frequency component IMF1, a low-frequency component IMF4, a medium-frequency component IMF2 and an IMF 3; the high-frequency component IMF1 and the low-frequency component IMF4 are processed by a wavelet decomposition unit and a 3 sigma threshold filtering unit respectively, and finally the filtered signals, the intermediate-frequency components IMF2 and IMF3 are subjected to signal reconstruction to reconstruct the transformer acoustic signal through a signal reconstruction unit.
Specifically, fig. 1 is a flow chart of a transformer acoustic signal noise reduction processing method according to the present invention.
According to the technical scheme, firstly, a plurality of paths of acoustic signals are obtained, and after windowing and framing through a Hamming window, short-time Fourier transform is carried out, so that the acoustic signals have frequency domain sparse characteristics; then obtaining a multi-channel output signal through an SCA separation unit 1 based on an underdetermined blind source separation strategy: split signal 1, split signal 2, split signal 3, split signal 4; the separation signal 1 is then subjected to a variational modal decomposition by the VMD decomposition unit 2 to obtain a high-frequency component IMF1, a low-frequency component IMF4 and medium-frequency components IMF2, IMF 3. The high-frequency component IMF1 and the low-frequency component IMF4 are processed by a wavelet decomposition unit 3 and a 3 sigma threshold filtering unit 4 respectively, and finally the filtered signals and the intermediate-frequency components IMF2 and IMF3 pass through a signal reconstruction unit 5 to reconstruct the transformer acoustic signal.
The waveform of the multi-path transformer monitoring input signal with noise is shown in fig. 2, wherein measurement signals of the transformer noise are respectively obtained from two sensors.
Fig. 3 is a frequency domain scattergram example obtained by performing blind source separation on two paths of observation signals by a sparse component analysis algorithm.
Fig. 4 shows an example of output signals obtained by performing a metamorphic mode decomposition on input signals after blind source separation.
Fig. 5 is a waveform of a noise reduction output signal obtained by filtering an output signal after the variable mode decomposition through a wavelet decomposition unit and a 3 σ threshold filtering unit and finally reconstructing low, medium and high frequency signals.
According to the technical scheme, the signals acquired by the acoustic sensor are denoised through a sparse component analysis algorithm and variational modal decomposition based on a potential function. Firstly, separating out the sound signals of the running state of the transformer by a sparse component analysis algorithm, decomposing the signals into four layers of intrinsic mode functions by using variational mode decomposition, filtering the intrinsic mode function components to obtain new intrinsic mode function components, and reconstructing the new intrinsic mode function components to obtain the final noise-removed signals. The method can effectively separate the operation sound signal and the environmental noise of the transformer substation, and has good denoising effect.
The invention can be widely applied to the field of operation monitoring of the unattended transformer substation.
Claims (3)
1. A transformer acoustic signal noise reduction processing method comprises the field acquisition of an acoustic signal of a transformer running state, and is characterized in that:
1) separating out the sound signals of the running state of the transformer by a sparse component analysis algorithm;
2) decomposing the signal into four layers of intrinsic mode functions by using variational mode decomposition;
3) filtering the intrinsic mode function component to obtain a new intrinsic mode function component;
4) reconstructing the data to obtain a final de-noised signal;
the transformer acoustic signal noise reduction processing method is based on multi-path measurement signals, and the processing process mainly comprises the following steps:
1) after windowing and framing through a Hamming window, carrying out short-time Fourier transform on the sound signal to enable the sound signal to have frequency domain sparse characteristics;
2) obtaining a preliminary de-noising signal through underdetermined blind source separation;
3) further carrying out four-layer eigenmode function decomposition on the signals by a variation mode decomposition method;
4) carrying out 6-layer wavelet decomposition on the high-frequency component and the low-frequency component of the intrinsic mode function, and carrying out filtering processing on the high-frequency component and the low-frequency component of the intrinsic mode function by using a 3 sigma principle;
5) and performing signal reconstruction according to the high-frequency component, the low-frequency component and the middle-frequency component of the new eigenmode function.
2. The method for denoising transformer acoustic signals according to claim 1, wherein the method for denoising transformer acoustic signals comprises performing short-time fourier transform on the acoustic signals after windowing and framing through a hamming window to make the acoustic signals have frequency domain sparsity; then, obtaining a preliminary de-noising signal through underdetermined blind source separation; then, further carrying out four-layer eigenmode function decomposition on the signals by a variation mode decomposition method; carrying out 6-layer wavelet decomposition on the high-frequency component and the low-frequency component of the intrinsic mode function, and carrying out filtering processing on the high-frequency component and the low-frequency component of the intrinsic mode function by using a 3 sigma principle; and finally, performing signal reconstruction according to the high-frequency component, the low-frequency component and the middle-frequency component of the new eigenmode function.
3. The transformer acoustic signal noise reduction processing method according to claim 1, wherein the transformer acoustic signal noise reduction processing method is used for denoising the acquired signal of the acoustic sensor through a sparse component analysis algorithm and variational modal decomposition based on a potential function, so that the transformer substation operation acoustic signal and the environmental noise can be effectively separated, and the denoising effect is good.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910631576.0A CN110426569B (en) | 2019-07-12 | 2019-07-12 | Noise reduction processing method for acoustic signals of transformer |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910631576.0A CN110426569B (en) | 2019-07-12 | 2019-07-12 | Noise reduction processing method for acoustic signals of transformer |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110426569A CN110426569A (en) | 2019-11-08 |
| CN110426569B true CN110426569B (en) | 2021-09-21 |
Family
ID=68409349
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910631576.0A Active CN110426569B (en) | 2019-07-12 | 2019-07-12 | Noise reduction processing method for acoustic signals of transformer |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110426569B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111107478B (en) * | 2019-12-11 | 2021-04-09 | 江苏爱谛科技研究院有限公司 | Sound enhancement method and sound enhancement system |
| CN111751134B (en) * | 2020-06-22 | 2021-12-14 | 西安科技大学 | VMD and RLS-based coal mining machine vibration signal noise reduction method |
| CN112446329B (en) * | 2020-11-30 | 2023-08-08 | 广州大学 | Time-varying structure instantaneous frequency determining method, system, device and storage medium |
| CN112484844A (en) * | 2020-11-30 | 2021-03-12 | 国网湖南省电力有限公司 | Method and system for detecting noise and environmental interference resistance of transformer |
| CN112927710B (en) * | 2021-01-21 | 2021-10-26 | 安徽南瑞继远电网技术有限公司 | Power transformer working condition noise separation method based on unsupervised mode |
| CN114611329B (en) * | 2022-04-01 | 2023-09-26 | 长江大学 | Time domain electromagnetic method near field noise suppression method based on variation modal decomposition |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0541031A1 (en) * | 1991-11-08 | 1993-05-12 | Sony Corporation | Signal transmitting and receiving apparatus |
| US5907822A (en) * | 1997-04-04 | 1999-05-25 | Lincom Corporation | Loss tolerant speech decoder for telecommunications |
| CN101853242A (en) * | 2010-06-23 | 2010-10-06 | 哈尔滨工业大学 | Equipment or system built-in test signal false-alarm filtering method based on empirical mode decomposition |
| CN103106903A (en) * | 2013-01-11 | 2013-05-15 | 太原科技大学 | Single channel blind source separation method |
| CN106842112A (en) * | 2016-12-30 | 2017-06-13 | 西北工业大学 | Sound localization method based on parametrization Bayes's dictionary learning under strong reverberant ambiance |
| CN107516065A (en) * | 2017-07-13 | 2017-12-26 | 天津大学 | The sophisticated signal denoising method of empirical mode decomposition combination dictionary learning |
| CN107702908A (en) * | 2017-10-12 | 2018-02-16 | 国网山东省电力公司莱芜供电公司 | GIS mechanical oscillation signal Time-Frequency Analysis Methods based on VMD self adapting morphologies |
| CN108631786A (en) * | 2018-04-26 | 2018-10-09 | 青岛理工大学 | Random sparse compression sampling method and device for acoustic signals |
| CN109087631A (en) * | 2018-08-08 | 2018-12-25 | 北京航空航天大学 | A kind of Vehicular intelligent speech control system and its construction method suitable for complex environment |
| CN109374997A (en) * | 2018-09-03 | 2019-02-22 | 三峡大学 | Hybrid power system duration power quality disturbances and appraisal procedure based on VMD initialization S-transformation |
| CN109446928A (en) * | 2018-10-10 | 2019-03-08 | 南京航空航天大学 | A kind of signal de-noising method based on variation mode decomposition and least mean-square error sef-adapting filter |
| CN109580146A (en) * | 2018-11-29 | 2019-04-05 | 东南大学 | A kind of Vibration Parameters recognition methods based on improvement Sparse Component Analysis |
| CN109632310A (en) * | 2019-01-18 | 2019-04-16 | 北京化工大学 | A kind of Method for Bearing Fault Diagnosis based on feature enhancing |
| CN109633368A (en) * | 2018-12-03 | 2019-04-16 | 三峡大学 | The method of duration power quality disturbances containing distributed power distribution network based on VMD and DFA |
| CN109632312A (en) * | 2019-01-22 | 2019-04-16 | 北京化工大学 | Bearing combined failure diagnostic method based on multiple constraint Algorithms of Non-Negative Matrix Factorization |
| CN109828318A (en) * | 2019-01-25 | 2019-05-31 | 吉林大学 | A kind of magnetic resonance depth measurement signal noise filtering method based on variation mode decomposition |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7272566B2 (en) * | 2003-01-02 | 2007-09-18 | Dolby Laboratories Licensing Corporation | Reducing scale factor transmission cost for MPEG-2 advanced audio coding (AAC) using a lattice based post processing technique |
| US8161441B2 (en) * | 2009-07-24 | 2012-04-17 | StarDFX Technologies, Inc. | Robust scan synthesis for protecting soft errors |
| US8903498B2 (en) * | 2012-03-27 | 2014-12-02 | Physio-Control, Inc. | System and method for electrocardiogram analysis and optimization of cardiopulmonary resuscitation and therapy delivery |
| CN106610918A (en) * | 2015-10-22 | 2017-05-03 | 中央大学 | Empirical mode decomposition method and system for adaptive binary and conjugate shielding network |
-
2019
- 2019-07-12 CN CN201910631576.0A patent/CN110426569B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0541031A1 (en) * | 1991-11-08 | 1993-05-12 | Sony Corporation | Signal transmitting and receiving apparatus |
| US5907822A (en) * | 1997-04-04 | 1999-05-25 | Lincom Corporation | Loss tolerant speech decoder for telecommunications |
| CN101853242A (en) * | 2010-06-23 | 2010-10-06 | 哈尔滨工业大学 | Equipment or system built-in test signal false-alarm filtering method based on empirical mode decomposition |
| CN103106903A (en) * | 2013-01-11 | 2013-05-15 | 太原科技大学 | Single channel blind source separation method |
| CN106842112A (en) * | 2016-12-30 | 2017-06-13 | 西北工业大学 | Sound localization method based on parametrization Bayes's dictionary learning under strong reverberant ambiance |
| CN107516065A (en) * | 2017-07-13 | 2017-12-26 | 天津大学 | The sophisticated signal denoising method of empirical mode decomposition combination dictionary learning |
| CN107702908A (en) * | 2017-10-12 | 2018-02-16 | 国网山东省电力公司莱芜供电公司 | GIS mechanical oscillation signal Time-Frequency Analysis Methods based on VMD self adapting morphologies |
| CN108631786A (en) * | 2018-04-26 | 2018-10-09 | 青岛理工大学 | Random sparse compression sampling method and device for acoustic signals |
| CN109087631A (en) * | 2018-08-08 | 2018-12-25 | 北京航空航天大学 | A kind of Vehicular intelligent speech control system and its construction method suitable for complex environment |
| CN109374997A (en) * | 2018-09-03 | 2019-02-22 | 三峡大学 | Hybrid power system duration power quality disturbances and appraisal procedure based on VMD initialization S-transformation |
| CN109446928A (en) * | 2018-10-10 | 2019-03-08 | 南京航空航天大学 | A kind of signal de-noising method based on variation mode decomposition and least mean-square error sef-adapting filter |
| CN109580146A (en) * | 2018-11-29 | 2019-04-05 | 东南大学 | A kind of Vibration Parameters recognition methods based on improvement Sparse Component Analysis |
| CN109633368A (en) * | 2018-12-03 | 2019-04-16 | 三峡大学 | The method of duration power quality disturbances containing distributed power distribution network based on VMD and DFA |
| CN109632310A (en) * | 2019-01-18 | 2019-04-16 | 北京化工大学 | A kind of Method for Bearing Fault Diagnosis based on feature enhancing |
| CN109632312A (en) * | 2019-01-22 | 2019-04-16 | 北京化工大学 | Bearing combined failure diagnostic method based on multiple constraint Algorithms of Non-Negative Matrix Factorization |
| CN109828318A (en) * | 2019-01-25 | 2019-05-31 | 吉林大学 | A kind of magnetic resonance depth measurement signal noise filtering method based on variation mode decomposition |
Non-Patent Citations (5)
| Title |
|---|
| Fault Diagnosis for Rolling Bearings Based on Composite Multiscale Fine-Sorted Dispersion Entropy and SVM With Hybrid Mutation SCA-HHO Algorithm Optimization;Wenlong Fu等;《IEEE Access》;20200114;第8卷;全文 * |
| Variational mode decomposition based denoising in side channel attacks;Juan Ai等;《2016 2nd IEEE International Conference on Computer and Communications (ICCC)》;20170511;全文 * |
| 变分模态分解方法及其在滚动轴承早期故障诊断中的应用;唐贵基等;《振动工程学报》;20160815;第29卷(第4期);全文 * |
| 基于改进势函数稀疏分量分析算法的变压器振动自适应提取方法;邹亮等;《高电压技术》;20180228;第44卷(第2期);第508页右栏第2段至第516页左栏第3段 * |
| 基于终止准则改进K-SVD字典学习的稀疏表示特征增强方法;王华庆等;《机械工程学报》;20190430;第55卷(第7期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110426569A (en) | 2019-11-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110426569B (en) | Noise reduction processing method for acoustic signals of transformer | |
| Guo et al. | Fault feature extraction for rolling element bearing diagnosis based on a multi-stage noise reduction method | |
| CN102721545B (en) | Rolling bearing failure diagnostic method based on multi-characteristic parameter | |
| Yu et al. | Sparse coding shrinkage in intrinsic time-scale decomposition for weak fault feature extraction of bearings | |
| Hao et al. | A novel rail defect detection method based on undecimated lifting wavelet packet transform and Shannon entropy-improved adaptive line enhancer | |
| Guo et al. | An enhanced modulation signal bispectrum analysis for bearing fault detection based on non-Gaussian noise suppression | |
| CN109883706A (en) | A kind of rolling bearing local damage Weak fault feature extracting method | |
| Zheng et al. | Faults diagnosis of rolling bearings based on shift invariant K-singular value decomposition with sensitive atom nonlocal means enhancement | |
| CN112945546B (en) | Precise diagnosis method for complex faults of gearbox | |
| Duan et al. | Minimum entropy morphological deconvolution and its application in bearing fault diagnosis | |
| Zhang et al. | An adaptive graph morlet wavelet transform for railway wayside acoustic detection | |
| Zhou et al. | Denoising the hob vibration signal using improved complete ensemble empirical mode decomposition with adaptive noise and noise quantization strategies | |
| Zhou et al. | Improving purity of blasting vibration signals using advanced Empirical Mode Decomposition and Wavelet packet technique | |
| CN114330445A (en) | Wavelet threshold denoising method based on transformer vibration signal sensitive IMF | |
| CN117316172A (en) | Noise reduction method and system for robot sound emission signals | |
| CN116189711B (en) | Transformer fault identification method and device based on acoustic wave signal monitoring | |
| CN110459197B (en) | Signal enhancer and method for denoising and extracting weak blind signals | |
| CN115859054B (en) | MIC-CEEMDAN-based hydroelectric generating set draft tube pressure pulsation signal denoising method | |
| LU502454B1 (en) | A Bearing Fault Identification Method and System Based on EEMD Sparse Decomposition | |
| Zou et al. | Toward accurate extraction of bearing fault modulation characteristics with novel time–frequency modulation bispectrum and modulation Gini index analysis | |
| CN111521396B (en) | Bearing fault diagnosis method based on translation invariant high-density wavelet packet transformation | |
| CN113343887A (en) | Multi-sensor mixed fault signal blind separation method based on edge calculation and machine learning | |
| CN113702821A (en) | Method and system for extracting GIS partial discharge signal | |
| Lu et al. | Bearing fault diagnosis based on redundant second generation wavelet denoising and EEMD | |
| Wang et al. | Research on transformer fault voice print information processing based on wavelet packet optimization transform |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |