CN111665405A - Vibration and sound detection signal filtering method and system based on sparsity minimization - Google Patents

Vibration and sound detection signal filtering method and system based on sparsity minimization Download PDF

Info

Publication number
CN111665405A
CN111665405A CN202010525225.4A CN202010525225A CN111665405A CN 111665405 A CN111665405 A CN 111665405A CN 202010525225 A CN202010525225 A CN 202010525225A CN 111665405 A CN111665405 A CN 111665405A
Authority
CN
China
Prior art keywords
matrix
normalized correlation
signal sequence
sparse
eigenvalue
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.)
Withdrawn
Application number
CN202010525225.4A
Other languages
Chinese (zh)
Inventor
翟明岳
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong University of Petrochemical Technology
Original Assignee
Guangdong University of Petrochemical Technology
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Guangdong University of Petrochemical Technology filed Critical Guangdong University of Petrochemical Technology
Priority to CN202010525225.4A priority Critical patent/CN111665405A/en
Publication of CN111665405A publication Critical patent/CN111665405A/en
Withdrawn legal-status Critical Current

Links

Images

Classifications

    • 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
    • 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

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)

Abstract

The embodiment of the invention discloses a vibration and sound detection signal filtering method and system utilizing sparsity minimization, wherein the method comprises the following steps: step 101, acquiring a signal sequence S acquired according to a time sequence; step 102, obtaining a sparsity factor P; step 103, obtaining a sparse matrix A; step 104, obtaining the signal sequence S after noise filteringnew。

Description

Vibration and sound detection signal filtering method and system based on sparsity minimization
Technical Field
The invention relates to the field of electric power, in particular to a method and a system for filtering a vibration and sound detection signal of a transformer.
Background
With the high-speed development of the smart grid, the safe and stable operation of the power equipment is particularly important. At present, the detection of the operating state of the power equipment with ultrahigh voltage and above voltage grades, especially the detection of the abnormal state, is increasingly important and urgent. As an important component of an electric power system, a power transformer is one of the most important electrical devices in a substation, and its reliable operation is related to the safety of a power grid. Generally, the abnormal state of the transformer can be divided into core abnormality and winding abnormality. The core abnormality is mainly represented by core saturation, and the winding abnormality generally includes winding deformation, winding looseness and the like.
The basic principle of the transformer abnormal state detection is to extract each characteristic quantity in the operation of the transformer, analyze, identify and track the characteristic quantity so as to monitor the abnormal operation state of the transformer. The detection method can be divided into invasive detection and non-invasive detection according to the contact degree; the detection can be divided into live detection and power failure detection according to whether the shutdown detection is needed or not; the method can be classified into an electrical quantity method, a non-electrical quantity method, and the like according to the type of the detected quantity. In comparison, the non-invasive detection has strong transportability and is more convenient to install; the live detection does not affect the operation of the transformer; the non-electric quantity method is not electrically connected with the power system, so that the method is safer. The current common detection methods for the operation state of the transformer include a pulse current method and an ultrasonic detection method for detecting partial discharge, a frequency response method for detecting winding deformation, a vibration detection method for detecting mechanical and electrical faults, and the like. The detection methods mainly detect the insulation condition and the mechanical structure condition of the transformer, wherein the detection of the vibration signal (vibration sound) of the transformer is the most comprehensive, and the fault and the abnormal state of most transformers can be reflected.
In the running process of the transformer, the magnetostriction of the iron core silicon steel sheets and the vibration caused by the winding electrodynamic force can radiate vibration sound signals with different amplitudes and frequencies to the periphery. When the transformer normally operates, uniform low-frequency noise is emitted outwards; if the sound is not uniform, it is not normal. The transformer can make distinctive sounds in different running states, and the running state of the transformer can be mastered by detecting the sounds made by the transformer. It is worth noting that the detection of the sound emitted by the transformer in different operating states not only can detect a plurality of serious faults causing the change of the electrical quantity, but also can detect a plurality of abnormal states which do not endanger the insulation and do not cause the change of the electrical quantity, such as the loosening of internal and external parts of the transformer, and the like.
Because the vibration sound detection method utilizes the vibration signal sent by the transformer, the vibration sound detection method is easily influenced by environmental noise, and therefore, how to effectively identify the vibration sound and the noise is the key for success of the method.
Disclosure of Invention
Because the vibration sound detection method utilizes the vibration signal sent by the transformer, the vibration sound detection method is easily influenced by environmental noise, and therefore, how to effectively identify the vibration sound and the noise is the key for success of the method.
The invention aims to provide a vibration and sound detection signal filtering method and system based on sparsity minimization, wherein the method utilizes the characteristics that a transformer vibration and sound signal, impulse noise and background noise come from different information sources, and realizes the separation and filtering of the background noise (including abnormal points) and the impulse noise according to sparsity minimization. The method has better robustness and simpler calculation.
In order to achieve the purpose, the invention provides the following scheme:
a vibro-acoustic detection signal filtering method with sparsity minimization, comprising:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, obtaining a sparsity factor P, specifically: arranging N eigenvalues of the normalized correlation matrix B according to ascending order to obtain an eigenvalue set
Figure BDA0002533570640000021
For the feature value set
Figure BDA0002533570640000022
Each of the feature values in (a) determines whether or not σ is greater than or equal tobAll greater than or equal to σbAdding said characteristic value of (2) to a threshold set
Figure BDA0002533570640000023
In (2), the threshold value set is obtained
Figure BDA0002533570640000024
The sequence number of the minimum element in the feature value set C is assigned to the sparsity factor P. Wherein, the calculation formula of the normalized correlation matrix B is
Figure BDA0002533570640000025
N is the length of the signal sequence S; sigmabIs the mean square error of the sparse vector b; the calculation formula of the sparse vector b is that b is equal to U; u is a left characteristic vector matrix of the normalized correlation matrix B; a characteristic value matrix of the normalized correlation matrix B; m is0Is the mean of the signal sequence S;
step 103, obtaining a sparse matrix a, specifically: the calculation formula of the sparse matrix A is that A is equal to U*And V. Wherein V is a right eigenvector matrix of the normalized correlation matrix B;*is a matrix of eigenvalues of the sparse matrix A, the sparsificationEigenvalue matrix of matrix A*The calculation method comprises the following steps: the sum of the values of the diagonal elements of the eigenvalue matrix of the normalized correlation matrix B
Figure BDA0002533570640000026
σ0Is the mean square error of the signal sequence S;
step 104, obtaining the signal sequence S after noise filteringnewThe method specifically comprises the following steps: among all the intermediate parameter vectors x, are selected such that
Figure BDA0002533570640000027
Taking the intermediate parameter vector x with the minimum value as the signal sequence S after noise filteringnew. Wherein λ ismaxIs the largest eigenvalue of the normalized correlation matrix B.
A vibro-acoustic detection signal filtering system with sparsity minimization, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 finds the sparsity factor P, specifically: arranging N eigenvalues of the normalized correlation matrix B according to ascending order to obtain an eigenvalue set
Figure BDA0002533570640000028
For the feature value set
Figure BDA0002533570640000029
Each of the feature values in (a) determines whether or not σ is greater than or equal tobAll greater than or equal to σbAdding said characteristic value of (2) to a threshold set
Figure BDA00025335706400000210
In (2), the threshold value set is obtained
Figure BDA00025335706400000211
Is determined, the minimum element is included in the set of feature values
Figure BDA00025335706400000212
The sequence number in (2) is assigned to the sparsity factor P. Wherein, the calculation formula of the normalized correlation matrix B is
Figure BDA00025335706400000213
N is the length of the signal sequence S; sigmabIs the mean square error of the sparse vector b; the calculation formula of the sparse vector b is that b is equal to U; u is a left characteristic vector matrix of the normalized correlation matrix B; a characteristic value matrix of the normalized correlation matrix B; m is0Is the mean of the signal sequence S;
the module 203 finds a sparse matrix a, specifically: the calculation formula of the sparse matrix A is that A is equal to U*And V. Wherein V is a right eigenvector matrix of the normalized correlation matrix B;*is the eigenvalue matrix of the sparse matrix A, the eigenvalue matrix of the sparse matrix A*The calculation method comprises the following steps: the sum of the values of the diagonal elements of the eigenvalue matrix of the normalized correlation matrix B
Figure BDA00025335706400000214
σ0Is the mean square error of the signal sequence S;
module 204 finds the noise-filtered signal sequence SnewThe method specifically comprises the following steps: among all the intermediate parameter vectors x, are selected such that
Figure BDA00025335706400000215
Taking the intermediate parameter vector x with the minimum value as the signal sequence S after noise filteringnew. Wherein λ ismaxIs the largest eigenvalue of the normalized correlation matrix B.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
because the vibration sound detection method utilizes the vibration signal sent by the transformer, the vibration sound detection method is easily influenced by environmental noise, and therefore, how to effectively identify the vibration sound and the noise is the key for success of the method.
The invention aims to provide a vibration and sound detection signal filtering method and system based on sparsity minimization, wherein the method utilizes the characteristics that a transformer vibration and sound signal, impulse noise and background noise come from different information sources, and realizes the separation and filtering of the background noise (including abnormal points) and the impulse noise according to sparsity minimization. The method has better robustness and simpler calculation.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic flow chart of the system of the present invention;
FIG. 3 is a flow chart illustrating an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
FIG. 1 is a schematic flow chart of a vibro-acoustic detection signal filtering method using sparsity minimization
Fig. 1 is a schematic flow chart of a vibro-acoustic detection signal filtering method using sparsity minimization according to the present invention. As shown in fig. 1, the method for filtering a vibro-acoustic detection signal by minimizing sparsity specifically includes the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, obtaining a sparsity factor P, specifically: arranging N eigenvalues of the normalized correlation matrix B according to ascending order to obtain an eigenvalue set
Figure BDA0002533570640000031
For the feature value set
Figure BDA0002533570640000032
Each of the feature values in (a) determines whether or not σ is greater than or equal tobAll greater than or equal to σbAdding said characteristic value of (2) to a threshold set
Figure BDA0002533570640000033
In (2), the threshold value set is obtained
Figure BDA0002533570640000034
Is determined, the minimum element is included in the set of feature values
Figure BDA0002533570640000035
The sequence number in (2) is assigned to the sparsity factor P. Wherein, the calculation formula of the normalized correlation matrix B is
Figure BDA0002533570640000036
N is the length of the signal sequence S; sigmabIs the mean square error of the sparse vector b; the calculation formula of the sparse vector b is that b is equal to U; u is a left characteristic vector matrix of the normalized correlation matrix B; a characteristic value matrix of the normalized correlation matrix B; m is0Is the mean of the signal sequence S;
step 103, obtaining a sparse matrix a, specifically: the calculation formula of the sparse matrix A is that A is equal to U*And V. Wherein V is a right eigenvector matrix of the normalized correlation matrix B;*is the eigenvalue matrix of the sparse matrix A, the eigenvalue matrix of the sparse matrix A*The calculation method comprises the following steps: what is needed isAdding the values of diagonal elements of the eigenvalue matrix of the normalized correlation matrix B
Figure BDA0002533570640000041
σ0Is the mean square error of the signal sequence S;
step 104, obtaining the signal sequence S after noise filteringnewThe method specifically comprises the following steps: among all the intermediate parameter vectors x, are selected such that
Figure BDA0002533570640000042
Taking the intermediate parameter vector x with the minimum value as the signal sequence S after noise filteringnew. Wherein λ ismaxIs the largest eigenvalue of the normalized correlation matrix B.
FIG. 2 structural intent of a vibro-acoustic detection signal filtering system with sparsity minimization
Fig. 2 is a schematic structural diagram of a vibro-acoustic detection signal filtering system using sparsity minimization according to the present invention. As shown in fig. 2, the vibro-acoustic detection signal filtering system using sparsity minimization includes the following structure:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 finds the sparsity factor P, specifically: arranging N eigenvalues of the normalized correlation matrix B according to ascending order to obtain an eigenvalue set
Figure BDA0002533570640000043
For the feature value set
Figure BDA0002533570640000044
Each of the feature values in (a) determines whether or not σ is greater than or equal tobAll greater than or equal to σbAdding said characteristic value of (2) to a threshold set
Figure BDA0002533570640000045
In (2), the threshold value set is obtained
Figure BDA0002533570640000046
Is determined, the minimum element is included in the set of feature values
Figure BDA0002533570640000047
The sequence number in (2) is assigned to the sparsity factor P. Wherein, the calculation formula of the normalized correlation matrix B is
Figure BDA0002533570640000048
N is the length of the signal sequence S; sigmabIs the mean square error of the sparse vector b; the calculation formula of the sparse vector b is that b is equal to U; u is a left characteristic vector matrix of the normalized correlation matrix B; a characteristic value matrix of the normalized correlation matrix B; m is0Is the mean of the signal sequence S;
the module 203 finds a sparse matrix a, specifically: the calculation formula of the sparse matrix A is that A is equal to U*And V. Wherein V is a right eigenvector matrix of the normalized correlation matrix B;*is the eigenvalue matrix of the sparse matrix A, the eigenvalue matrix of the sparse matrix A*The calculation method comprises the following steps: the sum of the values of the diagonal elements of the eigenvalue matrix of the normalized correlation matrix B
Figure BDA0002533570640000049
σ0Is the mean square error of the signal sequence S;
module 204 finds the noise-filtered signal sequence SnewThe method specifically comprises the following steps: among all the intermediate parameter vectors x, are selected such that
Figure BDA00025335706400000410
Taking the intermediate parameter vector x with the minimum value as the signal sequence S after noise filteringnew. Wherein λ ismaxIs the largest eigenvalue of the normalized correlation matrix B.
The following provides an embodiment for further illustrating the invention
FIG. 3 is a flow chart illustrating an embodiment of the present invention. As shown in fig. 3, the method specifically includes the following steps:
step 301, acquiring a signal sequence S acquired according to a time sequence;
step 302, obtaining a sparsity factor P, specifically: arranging N eigenvalues of the normalized correlation matrix B according to ascending order to obtain an eigenvalue set
Figure BDA0002533570640000051
For the feature value set
Figure BDA0002533570640000052
Each of the feature values in (a) determines whether or not σ is greater than or equal tobAll greater than or equal to σbAdding said characteristic value of (2) to a threshold set
Figure BDA0002533570640000053
In (2), the threshold value set is obtained
Figure BDA0002533570640000054
Is determined, the minimum element is included in the set of feature values
Figure BDA0002533570640000055
The sequence number in (2) is assigned to the sparsity factor P. Wherein, the calculation formula of the normalized correlation matrix B is
Figure BDA0002533570640000056
N is the length of the signal sequence S; sigmabIs the mean square error of the sparse vector b; the calculation formula of the sparse vector b is that b is equal to U; u is a left characteristic vector matrix of the normalized correlation matrix B; a characteristic value matrix of the normalized correlation matrix B; m is0Is the mean of the signal sequence S;
step 303, obtaining a sparse matrix a, specifically: the calculation formula of the sparse matrix A is that A is equal to U*And V. Wherein V is a right eigenvector matrix of the normalized correlation matrix B;*is the eigenvalue matrix of the sparse matrix A, the eigenvalue matrix of the sparse matrix A*The calculation method comprises the following steps: the normalized correlationValue addition of diagonal elements of eigenvalue matrix of matrix B
Figure BDA0002533570640000057
σ0Is the mean square error of the signal sequence S;
step 304 is to obtain the signal sequence S after noise filteringnewThe method specifically comprises the following steps: among all the intermediate parameter vectors x, are selected such that
Figure BDA0002533570640000058
Taking the intermediate parameter vector x with the minimum value as the signal sequence S after noise filteringnew. Wherein λ ismaxIs the largest eigenvalue of the normalized correlation matrix B.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is simple because the system corresponds to the method disclosed by the embodiment, and the relevant part can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (2)

1. The method for filtering the vibration and sound detection signal by utilizing sparsity minimization is characterized by comprising the following steps of:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, obtaining a sparsity factor P, specifically: arranging N eigenvalues of the normalized correlation matrix B according to ascending order to obtain an eigenvalue set
Figure FDA0002533570630000011
For the feature value set
Figure FDA0002533570630000012
Each of the feature values in (a) determines whether or not σ is greater than or equal tobAll greater than or equal to σbAdding said characteristic value of (2) to a threshold set
Figure FDA0002533570630000013
In (2), the threshold value set is obtained
Figure FDA0002533570630000014
Is determined, the minimum element is included in the set of feature values
Figure FDA0002533570630000015
The sequence number in (2) is assigned to the sparsity factor P. Wherein, the calculation formula of the normalized correlation matrix B is
Figure FDA0002533570630000016
N is the length of the signal sequence S; sigmabIs the mean square error of the sparse vector b; the calculation formula of the sparse vector b is that b is equal to U; u is a left characteristic vector matrix of the normalized correlation matrix B; a characteristic value matrix of the normalized correlation matrix B; m is0Is the mean of the signal sequence S;
step 103, obtaining a sparse matrix a, specifically: the calculation formula of the sparse matrix A is that A is equal to U*And V. Wherein V is a right eigenvector matrix of the normalized correlation matrix B;*is the eigenvalue matrix of the sparse matrix A, the eigenvalue matrix of the sparse matrix A*The calculation method comprises the following steps: the sum of the values of the diagonal elements of the eigenvalue matrix of the normalized correlation matrix B
Figure FDA0002533570630000017
σ0Is the mean square error of the signal sequence S;
step 104, obtaining the signal sequence S after noise filteringnewThe method specifically comprises the following steps: among all the intermediate parameter vectors x, are selected such that
Figure FDA0002533570630000018
Taking the intermediate parameter vector x with the minimum value as the signal sequence S after noise filteringnew. Wherein λ ismaxIs the largest eigenvalue of the normalized correlation matrix B.
2. The vibration and sound detection signal filtering system using sparsity minimization is characterized by comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 finds the sparsity factor P, specifically: arranging N eigenvalues of the normalized correlation matrix B according to ascending order to obtain an eigenvalue set
Figure FDA0002533570630000019
For the feature value set
Figure FDA00025335706300000110
Each of the feature values in (a) determines whether or not σ is greater than or equal tobAll greater than or equal to σbAdding said characteristic value of (2) to a threshold set
Figure FDA00025335706300000111
In (2), the threshold value set is obtained
Figure FDA00025335706300000112
Is determined, the minimum element is included in the set of feature values
Figure FDA00025335706300000113
The sequence number in (2) is assigned to the sparsity factor P. Wherein, the calculation formula of the normalized correlation matrix B is
Figure FDA00025335706300000114
N is the length of the signal sequence S; sigmabIs the mean square error of the sparse vector b; the calculation formula of the sparse vector b is that b is equal to U; u is a left characteristic vector matrix of the normalized correlation matrix B; a characteristic value matrix of the normalized correlation matrix B; m is0Is the mean of the signal sequence S;
the module 203 finds a sparse matrix a, specifically: the calculation formula of the sparse matrix A is that A is equal to U*And V. Wherein V is a right eigenvector matrix of the normalized correlation matrix B;*is the eigenvalue matrix of the sparse matrix A, the eigenvalue matrix of the sparse matrix A*The calculation method comprises the following steps: the sum of the values of the diagonal elements of the eigenvalue matrix of the normalized correlation matrix B
Figure FDA0002533570630000021
σ0Is the mean square error of the signal sequence S;
module 204 finds the noise-filtered signal sequence SnewThe method specifically comprises the following steps: among all the intermediate parameter vectors x, are selected such that
Figure FDA0002533570630000022
Taking the intermediate parameter vector x with the minimum value as the signal sequence S after noise filteringnew. Wherein λ ismaxIs the largest eigenvalue of the normalized correlation matrix B.
CN202010525225.4A 2020-06-10 2020-06-10 Vibration and sound detection signal filtering method and system based on sparsity minimization Withdrawn CN111665405A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010525225.4A CN111665405A (en) 2020-06-10 2020-06-10 Vibration and sound detection signal filtering method and system based on sparsity minimization

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010525225.4A CN111665405A (en) 2020-06-10 2020-06-10 Vibration and sound detection signal filtering method and system based on sparsity minimization

Publications (1)

Publication Number Publication Date
CN111665405A true CN111665405A (en) 2020-09-15

Family

ID=72386580

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010525225.4A Withdrawn CN111665405A (en) 2020-06-10 2020-06-10 Vibration and sound detection signal filtering method and system based on sparsity minimization

Country Status (1)

Country Link
CN (1) CN111665405A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112415439A (en) * 2020-11-04 2021-02-26 华北电力大学 Vibration and sound detection signal filtering method and system using sparse projection
CN112434567A (en) * 2020-11-06 2021-03-02 华北电力大学 Power signal filtering method and system by using noise jitter property

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112415439A (en) * 2020-11-04 2021-02-26 华北电力大学 Vibration and sound detection signal filtering method and system using sparse projection
CN112415439B (en) * 2020-11-04 2021-11-19 华北电力大学 Vibration and sound detection signal filtering method and system using sparse projection
CN112434567A (en) * 2020-11-06 2021-03-02 华北电力大学 Power signal filtering method and system by using noise jitter property
CN112434567B (en) * 2020-11-06 2021-11-19 华北电力大学 Power signal filtering method and system by using noise jitter property

Similar Documents

Publication Publication Date Title
CN110031089B (en) Filtering method and device for vibration and sound detection signals of transformer in running state
CN111665405A (en) Vibration and sound detection signal filtering method and system based on sparsity minimization
CN110703149B (en) Method and system for detecting vibration and sound of running state of transformer by utilizing character spacing
CN111664933A (en) Method and system for filtering vibration and sound detection signal by utilizing static vector optimization
CN111664934A (en) Transformer state vibration and sound detection signal filtering method and system using feature selection
CN111780868A (en) Transformer running state vibration and noise detection method and system by utilizing Jeffery difference
CN110545086A (en) Transformer vibration sound signal filtering method and system by utilizing global optimization
CN110703145A (en) Transformer vibration sound signal reconstruction method and system by using multiple optimization theories
CN110286289B (en) Filtering method for vibration and sound detection signal of transformer
CN111649819A (en) Transformer state vibration and sound detection signal filtering method and system using iteration soft threshold
CN110514295B (en) Transformer running state vibration and sound detection signal filtering method and system by utilizing SVD (singular value decomposition)
CN110646691B (en) Transformer vibration sound signal filtering method and system by utilizing stretching transformation
CN111473861A (en) Transformer state vibration and sound detection signal reconstruction method and system by using sparse errors
CN111751098A (en) Vibration and sound detection signal reconstruction method and system by using Gaussian prediction model
CN111879403A (en) Vibration and sound detection signal reconstruction method and system by using weak signal retention
CN112417994B (en) Vibration and sound detection signal filtering method and system using regularization factor
CN112304419A (en) Vibration and sound detection signal reconstruction method and system by using generalized sparse coding
CN112415439B (en) Vibration and sound detection signal filtering method and system using sparse projection
CN111680604A (en) Method and system for filtering vibration and sound detection signals by using Fatemi condition
CN112307993B (en) Method and system for filtering vibration and sound detection signals by using local similarity
CN110837013A (en) Transformer state vibration and sound detection signal reconstruction method and system represented by sparse dictionary
CN111141384A (en) Transformer state vibration and sound detection signal reconstruction method and system by utilizing Frechet regularization
CN112254808B (en) Method and system for detecting vibration and sound of running state of transformer by utilizing gradient change
CN110632478A (en) Transformer state vibration and sound detection signal filtering method and system by utilizing hyperplane projection
CN112284520B (en) Vibration and sound detection signal reconstruction method and system by using optimal rank approximation

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
WW01 Invention patent application withdrawn after publication
WW01 Invention patent application withdrawn after publication

Application publication date: 20200915