CN108414219A - A kind of gear failure diagnosing method and system of gear motor - Google Patents
A kind of gear failure diagnosing method and system of gear motor Download PDFInfo
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- CN108414219A CN108414219A CN201810097150.7A CN201810097150A CN108414219A CN 108414219 A CN108414219 A CN 108414219A CN 201810097150 A CN201810097150 A CN 201810097150A CN 108414219 A CN108414219 A CN 108414219A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/02—Gearings; Transmission mechanisms
- G01M13/021—Gearings
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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- G01M13/028—Acoustic or vibration analysis
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Abstract
A kind of gear failure diagnosing method of gear motor, acquires the vibration signal of the gear；WAVELET PACKET DECOMPOSITION and wavelet package reconstruction successively are carried out to the vibration signal, obtain the reconstruction signal under different frequency；The feature parameter vectors are built to the reconstruction signal；Support vector machines is built, and parameter optimization is carried out with genetic algorithm to the support vector machines；Fault diagnosis will be carried out in the support vector machines after the feature parameter vectors input optimization, detects the work condition state of the gear and generate corresponding prompt message and processing strategy.
Description
Technical field
The application belongs to gear motor technical field, more particularly to a kind of Gear Fault Diagnosis side of gear motor
Method and system.
Background technology
Gear motor is generally used for the drive apparatus of low rotation speed large torque, is by motor or other running at high speed
Drive apparatus the gear of output shaft is transmitted to achieve the purpose that deceleration by speed reducer.
In the prior art, lack effective signal processing method effectively to diagnose gear distress.
Invention content
In view of this, it is that the prior art lacks and diagnosed to gear motor gear distress that the application is to be solved
Problem.The present invention provides a kind of gear failure diagnosing method of gear motor and systems, can improve the effect of fault diagnosis
Rate and accuracy.
In order to solve the above-mentioned technical problem, the present invention is achieved by the following technical programs：
A kind of gear failure diagnosing method of gear motor, including：
S1. the vibration signal of the gear is acquired；
S2. WAVELET PACKET DECOMPOSITION and wavelet package reconstruction successively are carried out to the vibration signal, obtains the reconstruct under different frequency
Signal；
S3. the feature parameter vectors are built to the reconstruction signal；
S4. support vector machines is built, and parameter optimization is carried out with genetic algorithm to the support vector machines；
S5. fault diagnosis will be carried out in the support vector machines after the feature parameter vectors input optimization, detected
The work condition state of the gear simultaneously generates corresponding prompt message and processing strategy.
In one embodiment, in the step S2, the process of the WAVELET PACKET DECOMPOSITION is carried out to the vibration signal
For：The vibration signal in the step S1 is first subjected to first time decomposition, a pair of of low frequency signal and high-frequency signal is obtained, then divides
It is other the low frequency signal and high-frequency signal decompose for second ..., n-th obtain n+1 layers of sub-band tree after decomposing,
The different sub-bands of double WAVELET PACKET DECOMPOSITION number are obtained after decomposing every time, last layer of sub-band tree there are 2 (n+1) a
Sub-band；
The formula that the WAVELET PACKET DECOMPOSITION and wavelet package reconstruction are carried out to the vibration signal is respectively：
With:
Wherein, ak-2lAnd bk-2lIt is wavelet decomposition conjugate filter coefficient, hl-2kAnd gl-2kIt is wavelet reconstruction conjugation filter
Wave device coefficient.
In one embodiment, in the step S3, if each sub-band of the sub-band tree is provided as a section
Point, the realization process that the feature parameter vectors are built to the reconstruction signal are：
Each sub-band of the sub-band tree is converted into corresponding frequency band energy：
Wherein xi,j(k) indicate that the energy of the sub-band of i-th layer, j-th node, N indicate the sub-band of the sub-band tree
Number；
Frequency band energy summation to each sub-band, obtains the gross energy of the vibration signal：
The feature parameter vectors are built according to the frequency band energy of each sub-band：
According to the energy accounting T/E of the feature parameter vectors of each sub-band and the gross energy of vibration signal, obtain
Normalized the feature parameter vectors：
In one embodiment, in the step S4, parameter optimization is carried out with genetic algorithm to the support vector machines
Realization process be：Optimizing is carried out to the kernel functional parameter and penalty factor of support vector machines with genetic algorithm, is obtained best
Parameter combination, wherein the kernel functional parameter is the complexity quantization for influencing sample data and being distributed in high-dimensional feature space
Value, the penalty factor are the fiducial range and empiric risk that the support vector machines is adjusted in determining feature space
Ratio.
In one embodiment, in step S4, S5, the work condition state is curved for gear normal work, input shaft
Bent failure or gear tooth breakage failure；
The support vector machines is, will be under the gear normal work of input, input shaft flexural failure and gear tooth breakage failure
Given data value, be accordingly divided into normal data set, input shaft flexural failure data set and gear tooth breakage fault data collection, then
Form the reference model of fixed operation fixed pattern；
The fixed operation fixed pattern is, when real-time unknown operation data, into the support vector machines, described normal
Data set, input shaft flexural failure data set or gear tooth breakage fault data are focused to find out with the unknown operation data difference most
Small data element, and it is curved that the unknown operation data is classified as normal data set belonging to the data element, input shaft
Bent fault data collection or gear tooth breakage fault data collection.
A kind of Gear Fault Diagnosis system of gear motor, is equipped with lower computer unit, and the lower computer unit is equipped with phase
The signal picker and signal processor to connect；It is additionally provided with the host computer unit for connecting the signal processor；
The signal picker, the vibration signal for acquiring the gear；
The signal processor is obtained for successively carrying out WAVELET PACKET DECOMPOSITION and wavelet package reconstruction to the vibration signal
Reconstruction signal under different frequency, and the feature parameter vectors are built to the reconstruction signal and are sent to the host computer list
Member；
The host computer unit carries out parameter to the support vector machines for building support vector machines with genetic algorithm
Optimizing is additionally operable to that fault diagnosis will be carried out in the support vector machines after the feature parameter vectors input optimization, detects
The work condition state of the gear simultaneously generates corresponding prompt message and processing strategy.
In one embodiment, the work condition state is gear normal work, input shaft flexural failure or gear tooth breakage event
Barrier.
In one embodiment, the signal picker is shaking on the gear shaft holder or generator bearing base
Dynamic sensor, the signal processor are data collecting instrument；The host computer unit is equipped with connects the number by communication link
According to the database server of Acquisition Instrument, and the control terminal of the connection database server, it is equipped in the control terminal
Monitoring and controlling software；
The control terminal is used for the data memory action by the database server, soft by the monitoring and controlling
Part builds the support vector machines, and carries out parameter optimization with genetic algorithm to the support vector machines, is controlled by the monitoring
Software processed will carry out fault diagnosis in the support vector machines after the feature parameter vectors input optimization, detect the tooth
The work condition state of wheel simultaneously generates corresponding prompt message and processing strategy.
In one embodiment, the support vector machines is the normal work of gear that will be inputted from the monitoring and controlling software
Make, the given data value under input shaft flexural failure and gear tooth breakage failure, is accordingly divided into normal data set, input bending shaft
Fault data collection and gear tooth breakage fault data collection, re-form the reference model of operation fixed pattern；
The operation fixed pattern is, when real-time unknown operation data, by the signal picker enter it is described support to
Amount machine, the normal data set, input shaft flexural failure data set or gear tooth breakage fault data be focused to find out with it is described not
Know the data element of operation data difference minimum, and the unknown operation data is classified as normal belonging to the data element
Data set, input shaft flexural failure data set or gear tooth breakage fault data collection.
Compared with prior art, the application can be obtained including following technique effect：
The gear failure diagnosing method and system of a kind of gear motor provided by the invention,
(1) it utilizes WAVELET PACKET DECOMPOSITION and energy feature to analyze wind gear distress, can effectively extract the characteristic value of failure；
(2) the parallel random searching ability of the overall situation that genetic algorithm is powerful is utilized, realizes support vector machines kernel functional parameter
Automatic quick optimal selection, overcomes the blindness of artificial selection parameter；
(3) gear distress is identified using support vector machines, extraction feature is used as using wavelet packet-energy feature,
Recognition accuracy is high, realizes the accurate differentiation of gear distress.
Certainly, implementing any product of the application must be not necessarily required to reach all the above technique effect simultaneously.
Description of the drawings
Attached drawing described herein is used for providing further understanding of the present application, constitutes part of this application, this Shen
Illustrative embodiments and their description please do not constitute the improper restriction to the application for explaining the application.In the accompanying drawings：
Fig. 1 is the flow diagram of the fuzzy control method of the brshless DC motor in one embodiment.
Specific implementation mode
Presently filed embodiment is described in detail below in conjunction with accompanying drawings and embodiments, how the application is applied whereby
Technological means solves technical problem and reaches the realization process of technical effect to fully understand and implement.
Referring to Fig. 1, in one embodiment,
A kind of gear failure diagnosing method of gear motor, including：
S1. the vibration signal of the gear is acquired；
S2. WAVELET PACKET DECOMPOSITION and wavelet package reconstruction successively are carried out to the vibration signal, obtains the reconstruct under different frequency
Signal；
S3. the feature parameter vectors are built to the reconstruction signal；
S4. support vector machines is built, and parameter optimization is carried out with genetic algorithm to the support vector machines；
S5. fault diagnosis will be carried out in the support vector machines after the feature parameter vectors input optimization, detected
The work condition state of the gear simultaneously generates corresponding prompt message and processing strategy.
In one embodiment, in the step S2, the process of the WAVELET PACKET DECOMPOSITION is carried out to the vibration signal
For：The vibration signal in the step S1 is first subjected to first time decomposition, a pair of of low frequency signal and high-frequency signal is obtained, then divides
It is other the low frequency signal and high-frequency signal decompose for second ..., n-th obtain n+1 layers of sub-band tree after decomposing,
The different sub-bands of double WAVELET PACKET DECOMPOSITION number are obtained after decomposing every time, last layer of sub-band tree there are 2 (n+1) a
Sub-band；
The formula that the WAVELET PACKET DECOMPOSITION and wavelet package reconstruction are carried out to the vibration signal is respectively：
With:
Wherein, ak-2lAnd bk-2lIt is wavelet decomposition conjugate filter coefficient, hl-2kAnd gl-2kIt is wavelet reconstruction conjugation filter
Wave device coefficient.
In one embodiment, in the step S3, if each sub-band of the sub-band tree is provided as a section
Point, the realization process that the feature parameter vectors are built to the reconstruction signal are：
Each sub-band of the sub-band tree is converted into corresponding frequency band energy：
Wherein xi,j(k) indicate that the energy of the sub-band of i-th layer, j-th node, N indicate the sub-band of the sub-band tree
Number；
Frequency band energy summation to each sub-band, obtains the gross energy of the vibration signal：
The feature parameter vectors are built according to the frequency band energy of each sub-band：
According to the energy accounting T/E of the feature parameter vectors of each sub-band and the gross energy of vibration signal, obtain
Normalized the feature parameter vectors：
In one embodiment, in the step S4, parameter optimization is carried out with genetic algorithm to the support vector machines
Realization process be：Optimizing is carried out to the kernel functional parameter and penalty factor of support vector machines with genetic algorithm, is obtained best
Parameter combination, wherein the kernel functional parameter is the complexity quantization for influencing sample data and being distributed in high-dimensional feature space
Value, the penalty factor are the fiducial range and empiric risk that the support vector machines is adjusted in determining feature space
Ratio.
In one embodiment, in step S4, S5, the work condition state is curved for gear normal work, input shaft
Bent failure or gear tooth breakage failure；
The support vector machines is, will be under the gear normal work of input, input shaft flexural failure and gear tooth breakage failure
Given data value, be accordingly divided into normal data set, input shaft flexural failure data set and gear tooth breakage fault data collection, then
Form the reference model of fixed operation fixed pattern；
The fixed operation fixed pattern is, when real-time unknown operation data, into the support vector machines, described normal
Data set, input shaft flexural failure data set or gear tooth breakage fault data are focused to find out with the unknown operation data difference most
Small data element, and it is curved that the unknown operation data is classified as normal data set belonging to the data element, input shaft
Bent fault data collection or gear tooth breakage fault data collection.
A kind of Gear Fault Diagnosis system of gear motor, is equipped with lower computer unit, and the lower computer unit is equipped with phase
The signal picker and signal processor to connect；It is additionally provided with the host computer unit for connecting the signal processor；
The signal picker, the vibration signal for acquiring the gear；
The signal processor is obtained for successively carrying out WAVELET PACKET DECOMPOSITION and wavelet package reconstruction to the vibration signal
Reconstruction signal under different frequency, and the feature parameter vectors are built to the reconstruction signal and are sent to the host computer list
Member；
The host computer unit carries out parameter to the support vector machines for building support vector machines with genetic algorithm
Optimizing is additionally operable to that fault diagnosis will be carried out in the support vector machines after the feature parameter vectors input optimization, detects
The work condition state of the gear simultaneously generates corresponding prompt message and processing strategy.
In one embodiment, the work condition state is gear normal work, input shaft flexural failure or gear tooth breakage event
Barrier.
In one embodiment, the signal picker is shaking on the gear shaft holder or generator bearing base
Dynamic sensor, the signal processor are data collecting instrument；The host computer unit is equipped with connects the number by communication link
According to the database server of Acquisition Instrument, and the control terminal of the connection database server, it is equipped in the control terminal
Monitoring and controlling software；
The control terminal is used for the data memory action by the database server, soft by the monitoring and controlling
Part builds the support vector machines, and carries out parameter optimization with genetic algorithm to the support vector machines, is controlled by the monitoring
Software processed will carry out fault diagnosis in the support vector machines after the feature parameter vectors input optimization, detect the tooth
The work condition state of wheel simultaneously generates corresponding prompt message and processing strategy.
In one embodiment, the support vector machines is the normal work of gear that will be inputted from the monitoring and controlling software
Make, the given data value under input shaft flexural failure and gear tooth breakage failure, is accordingly divided into normal data set, input bending shaft
Fault data collection and gear tooth breakage fault data collection, re-form the reference model of operation fixed pattern；
The operation fixed pattern is, when real-time unknown operation data, by the signal picker enter it is described support to
Amount machine, the normal data set, input shaft flexural failure data set or gear tooth breakage fault data be focused to find out with it is described not
Know the data element of operation data difference minimum, and the unknown operation data is classified as normal belonging to the data element
Data set, input shaft flexural failure data set or gear tooth breakage fault data collection.
Compared with prior art, the application can be obtained including following technique effect：
The gear failure diagnosing method and system of a kind of gear motor provided by the invention,
(1) it utilizes WAVELET PACKET DECOMPOSITION and energy feature to analyze wind gear distress, can effectively extract the characteristic value of failure；
(2) the parallel random searching ability of the overall situation that genetic algorithm is powerful is utilized, realizes support vector machines kernel functional parameter
Automatic quick optimal selection, overcomes the blindness of artificial selection parameter；
(3) gear distress is identified using support vector machines, extraction feature is used as using wavelet packet-energy feature,
Recognition accuracy is high, realizes the accurate differentiation of gear distress.
In a typical configuration, computing device includes one or more processors (CPU), input/output interface, net
Network interface and memory.
Memory may include computer-readable medium in volatile memory, random access memory (RAM) and/or
The forms such as Nonvolatile memory, such as read-only memory (ROM) or flash memory (flashRAM).Memory is showing for computer-readable medium
Example.
Computer-readable medium includes permanent and non-permanent, removable and non-removable media can be by any method
Or technology realizes information storage.Information can be computer-readable instruction, data structure, the module of program or other data.
The example of the storage medium of computer includes, but are not limited to phase transition internal memory (PRAM), static RAM (SRAM), moves
State random access memory (DRAM), other kinds of random access memory (RAM), read-only memory (ROM), electric erasable
Programmable read only memory (EEPROM), fast flash memory bank or other memory techniques, read-only disc read only memory (CD-ROM) (CD-ROM),
Digital versatile disc (DVD) or other optical storages, magnetic tape cassette, tape magnetic disk storage or other magnetic storage apparatus
Or any other non-transmission medium, it can be used for storage and can be accessed by a computing device information.As defined in this article, it calculates
Machine readable medium does not include non-temporary computer readable media (transitory media), such as data-signal and carrier wave of modulation.
Some vocabulary has such as been used to censure specific components in specification and claim.Those skilled in the art answer
It is understood that hardware manufacturer may call the same component with different nouns.This specification and claims are not with name
The difference of title is used as the mode for distinguishing component, but is used as the criterion of differentiation with the difference of component functionally.Such as logical
The "comprising" of piece specification and claim mentioned in is an open language, therefore should be construed to " include but do not limit
In "." substantially " refer in receivable error range, those skilled in the art can be described within a certain error range solution
Technical problem basically reaches the technique effect.In addition, " coupling " word includes any direct and indirect electric property coupling herein
Means.Therefore, if it is described herein that a first device is coupled to a second device, then representing the first device can directly electrical coupling
It is connected to the second device, or the second device indirectly electrically coupled through other devices or coupling means.Specification
Subsequent descriptions be implement the application better embodiment, so it is described description be for the purpose of the rule for illustrating the application,
It is not limited to scope of the present application.The protection domain of the application is when subject to appended claims institute defender.
It should also be noted that, the terms "include", "comprise" or its any other variant are intended to nonexcludability
Including so that commodity or system including a series of elements include not only those elements, but also include not clear
The other element listed, or further include for this commodity or the intrinsic element of system.In the feelings not limited more
Under condition, the element that is limited by sentence "including a ...", it is not excluded that including the element commodity or system in also
There are other identical elements.
Several preferred embodiments of the present invention have shown and described in above description, but as previously described, it should be understood that the present invention
Be not limited to form disclosed herein, be not to be taken as excluding other embodiments, and can be used for various other combinations,
Modification and environment, and the above teachings or related fields of technology or knowledge can be passed through in the scope of the invention is set forth herein
It is modified.And changes and modifications made by those skilled in the art do not depart from the spirit and scope of the present invention, then it all should be in this hair
In the protection domain of bright appended claims.
Claims (9)
1. a kind of gear failure diagnosing method of gear motor, which is characterized in that include the following steps：
S1. the vibration signal of the gear is acquired；
S2. WAVELET PACKET DECOMPOSITION and wavelet package reconstruction successively are carried out to the vibration signal, obtains the reconstruction signal under different frequency；
S3. the feature parameter vectors are built to the reconstruction signal；
S4. support vector machines is built, and parameter optimization is carried out with genetic algorithm to the support vector machines；
S5. fault diagnosis will be carried out in the support vector machines after the feature parameter vectors input optimization, detected described
The work condition state of gear simultaneously generates corresponding prompt message and processing strategy.
2. the gear failure diagnosing method of gear motor as described in claim 1, which is characterized in that in the step S2
In, the process that the WAVELET PACKET DECOMPOSITION is carried out to the vibration signal is：The vibration signal in the step S1 is first carried out the
It is primary to decompose, a pair of of low frequency signal and high-frequency signal are obtained, second then is carried out to the low frequency signal and high-frequency signal respectively
It is secondary decompose ..., n-th obtain n+1 layer of sub-band tree after decomposing, obtain double WAVELET PACKET DECOMPOSITION number after each decomposition
Different sub-bands, last layer of sub-band tree have 2 (n+1) a sub-bands；
The formula that the WAVELET PACKET DECOMPOSITION and wavelet package reconstruction are carried out to the vibration signal is respectively：
With:
Wherein, ak-2lAnd bk-2lIt is wavelet decomposition conjugate filter coefficient, hl-2kAnd gl-2kIt is wavelet reconstruction conjugate filter
Coefficient.
3. the gear failure diagnosing method of gear motor as claimed in claim 2, which is characterized in that in the step S3
In, if each sub-band of the sub-band tree is provided as a node, the feature parameter vectors are built to the reconstruction signal
Realization process is：
Each sub-band of the sub-band tree is converted into corresponding frequency band energy：
Wherein xi,j(k) indicate that the energy of the sub-band of i-th layer, j-th node, N indicate the sub-band number of the sub-band tree；
Frequency band energy summation to each sub-band, obtains the gross energy of the vibration signal：
The feature parameter vectors are built according to the frequency band energy of each sub-band：
According to the energy accounting T/E of the feature parameter vectors of each sub-band and the gross energy of vibration signal, normalizing is obtained
The feature parameter vectors of change：
4. the gear failure diagnosing method of gear motor as claimed in claim 2, which is characterized in that in the step S4
In, the realization process for carrying out parameter optimization with genetic algorithm to the support vector machines is：With genetic algorithm to support vector machines
Kernel functional parameter and penalty factor carry out optimizing, obtain optimal parameter combination, wherein the kernel functional parameter be influence sample
The complexity quantized value that notebook data is distributed in high-dimensional feature space, the penalty factor be in determining feature space,
Adjust the fiducial range of the support vector machines and the ratio of empiric risk.
5. a kind of gear failure diagnosing method of gear motor as claimed in claim 4, which is characterized in that
In step S4, S5, the work condition state is gear normal work, input shaft flexural failure or gear tooth breakage event
Barrier；
The support vector machines is, by under the gear normal work of input, input shaft flexural failure and gear tooth breakage failure
Primary data value is accordingly divided into normal data set, input shaft flexural failure data set and gear tooth breakage fault data collection, re-forms
The reference model of fixed operation fixed pattern；
The fixed operation fixed pattern is, when real-time unknown operation data, into the support vector machines, in the normal data
Collection, input shaft flexural failure data set or gear tooth breakage fault data are focused to find out and the unknown operation data difference minimum
Data element, and by the unknown operation data be classified as normal data set belonging to the data element, input bending shaft therefore
Hinder data set or gear tooth breakage fault data collection.
6. a kind of Gear Fault Diagnosis system of gear motor, which is characterized in that be equipped with lower computer unit, the slave computer list
Member is equipped with the signal picker and signal processor being connected with each other；It is additionally provided with the host computer unit for connecting the signal processor；
The signal picker, the vibration signal for acquiring the gear；
The signal processor obtains difference for successively carrying out WAVELET PACKET DECOMPOSITION and wavelet package reconstruction to the vibration signal
Reconstruction signal under frequency, and the feature parameter vectors are built to the reconstruction signal and are sent to the host computer unit；
The host computer unit carries out parameter with genetic algorithm to the support vector machines and seeks for building support vector machines
It is excellent, it is additionally operable to that fault diagnosis will be carried out in the support vector machines after the feature parameter vectors input optimization, detects institute
It states the work condition state of gear and generates corresponding prompt message and processing strategy.
7. a kind of Gear Fault Diagnosis system of gear motor as claimed in claim 6, which is characterized in that the operating mode shape
State is gear normal work, input shaft flexural failure or gear tooth breakage failure.
8. a kind of Gear Fault Diagnosis system of gear motor as claimed in claim 7, which is characterized in that the signal is adopted
Storage is the vibrating sensor on the gear shaft holder or generator bearing base, and the signal processor acquires for data
Instrument；The host computer unit is equipped with the database server that the data collecting instrument is connected by communication link, and described in connection
The control terminal of database server is equipped with monitoring and controlling software in the control terminal；
The control terminal is used for the data memory action by the database server, passes through the monitoring and controlling software structure
The support vector machines is built, and parameter optimization is carried out with genetic algorithm to the support vector machines, it is soft by the monitoring and controlling
Part will carry out fault diagnosis in the support vector machines after the feature parameter vectors input optimization, detect the gear
Work condition state simultaneously generates corresponding prompt message and processing strategy.
9. a kind of Gear Fault Diagnosis system of gear motor as claimed in claim 8, which is characterized in that it is described support to
Amount machine is that will be worked normally from the gear that the monitoring and controlling software inputs, under input shaft flexural failure and gear tooth breakage failure
Given data value, be accordingly divided into normal data set, input shaft flexural failure data set and gear tooth breakage fault data collection, then
Form the reference model of operation fixed pattern；
The operation fixed pattern is, when real-time unknown operation data, enters the support vector machines by the signal picker,
It is focused to find out and the unknown operation in the normal data set, input shaft flexural failure data set or gear tooth breakage fault data
The data element of data difference minimum, and the unknown operation data is classified as to the normal data belonging to the data element
Collection, input shaft flexural failure data set or gear tooth breakage fault data collection.
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| CN201810097150.7A CN108414219A (en) | 2018-01-31 | 2018-01-31 | A kind of gear failure diagnosing method and system of gear motor |
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| CN201810097150.7A CN108414219A (en) | 2018-01-31 | 2018-01-31 | A kind of gear failure diagnosing method and system of gear motor |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110207967A (en) * | 2019-06-13 | 2019-09-06 | 大连海事大学 | A kind of state identification method and system based on wavelet pack energy feature and cross-correlation |
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| CN107449603A (en) * | 2016-05-31 | 2017-12-08 | 华北电力大学（保定） | Fault Diagnosis of Fan method based on SVMs |
| CN107560844A (en) * | 2017-07-25 | 2018-01-09 | 广东工业大学 | A kind of fault diagnosis method and system of gearbox of wind turbine |
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| CN107449603A (en) * | 2016-05-31 | 2017-12-08 | 华北电力大学（保定） | Fault Diagnosis of Fan method based on SVMs |
| CN106198000A (en) * | 2016-07-11 | 2016-12-07 | 太原理工大学 | A kind of rocker arm of coal mining machine gear failure diagnosing method |
| CN107560844A (en) * | 2017-07-25 | 2018-01-09 | 广东工业大学 | A kind of fault diagnosis method and system of gearbox of wind turbine |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110207967A (en) * | 2019-06-13 | 2019-09-06 | 大连海事大学 | A kind of state identification method and system based on wavelet pack energy feature and cross-correlation |
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