CN108520093A - A kind of Trouble Diagnostic Method of Machinery Equipment and device in knowledge based library - Google Patents
A kind of Trouble Diagnostic Method of Machinery Equipment and device in knowledge based library Download PDFInfo
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- CN108520093A CN108520093A CN201810175974.1A CN201810175974A CN108520093A CN 108520093 A CN108520093 A CN 108520093A CN 201810175974 A CN201810175974 A CN 201810175974A CN 108520093 A CN108520093 A CN 108520093A
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- G—PHYSICS
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- G06F30/20—Design optimisation, verification or simulation
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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
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- 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
- G01M13/04—Bearings
- G01M13/045—Acoustic or vibration analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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Abstract
The invention discloses a kind of Trouble Diagnostic Method of Machinery Equipment and device in knowledge based library, the method includes：It receives equipment to be monitored and carries out vibration measurement；Feature based frequency knowledge base, calculates the characteristic frequency spectrum of the equipment；Based on State Knowledge library, knowledge reasoning is carried out to the characteristic frequency spectrum, obtains the operating status of the equipment；Wherein, the computational methods of the characteristic frequency knowledge library storage characteristic frequency；Relationship between the State Knowledge storage apparatus difference operating status and characteristic frequency.The present invention establishes diagnostic knowledge base, has more diagnosis basis, has the characteristics that diagnostic result is accurate.
Description
Technical field
The present invention relates to rotation class mechanical fault diagnosis technical fields, more particularly to a kind of machine in knowledge based library
Tool equipment fault diagnosis method and device.
Background technology
Fault diagnosis technology refers to differentiating its work by various monitoring means under system running state or working condition
It is whether normal.Usual fault diagnosis technology uses various state-detections, measurement, analysis and method of discrimination, in conjunction with monitored equipment
Historical situation and running environment factor, equipment running status is assessed, judges that equipment is in normal or improper shape
State, and state is shown and recorded.If equipment is in abnormality, alarm need to be made, so that operations staff locates in time
Reason, and provide information and basic data for accident analysis, Performance Evaluation, reasonable employment and trouble free service.
With the development of modern science and technology, people are under the rotation class mechanical equipment such as generator, air blower long period, high load capacity
Reliability propose requirements at the higher level.Realize that the safe operation of mechanical equipment is a kind of having for row using fault diagnosis technology means
The approach of effect.System running state is understood in time, and quick diagnosis simultaneously distinguishes failure, can not only significantly improve maintenance efficiency, drop
Low maintenance cost can also extend the service life of system.Therefore, there is important reality to anticipate the research of fault diagnosis technology
Justice.
Fault diagnosis technology due to modern times produce greatly there is an urgent need to and grow rapidly, fault diagnosis technology of today it is interior
Appearance has become increasingly abundant.But the relevant technologies are more to the research of a certain particular elements, less to versatile equipment, system research, and
More with vibration signals collecting, analytic function, fault diagnosis functions are less.
It is that facts have proved maximally efficient common method based on vibration signal processing and analysis in method for diagnosing faults.
In the existing frequency domain character analysis to vibration signal, Primary Reference be mechanized equipment rotating speed frequency multiplication, such as 1 frequency multiplication, 2 times
Frequently, 3 frequencys multiplication etc..Although it is more commonly used technological means at present to carry out fault diagnosis according to characteristic frequency, there is no at present compared with
For the comprehensive method for generating characteristic frequency according to the concrete composition parameter for being diagnosed mechanized equipment.
Therefore, how design parameter based on equipment carries out accurate fault diagnosis, is that current those skilled in the art compel
Be essential technical problems to be solved.
Invention content
To overcome above-mentioned the deficiencies in the prior art, the present invention provides a kind of method for diagnosing faults, are built by device parameter
Vertical correlation model, calculates characteristic frequency set, further according to the amplitude and artificial state's mark at historical vibration data, characteristic frequency
Note generates equipment state knowledge base, and treating diagnosis vibration signal using the knowledge base carries out analysis acquisition equipment state.If set
Standby abnormal state, provides fault severity level and processing scheme.
To achieve the above object, the present invention adopts the following technical scheme that：
A kind of Trouble Diagnostic Method of Machinery Equipment in knowledge based library, includes the following steps：
It receives equipment to be monitored and carries out vibration measurement；
Feature based frequency knowledge base, calculates the characteristic frequency spectrum of the equipment；
Based on State Knowledge library, knowledge reasoning is carried out to the characteristic frequency spectrum, obtains the operating status of the equipment；
Wherein, the computational methods of the characteristic frequency knowledge library storage characteristic frequency；
Relationship between the State Knowledge storage apparatus difference operating status and characteristic frequency.
Further, the calculating of the characteristic frequency is structural parameters based on equipment.
Further, the structural parameters include the parameter of electric machine, bearing parameter and specific mechanical parameter.
Further, the operating status includes normal and failure；Further include fault level when operating status is failure.
Further, State Knowledge library method for building up is：
Operating status label is carried out to each equipment surface historical vibration data, characteristic spectral line is generated and is run with each equipment difference
Relationship between state；
Amplitude range at the corresponding characteristic frequency of each equipment difference operating status is calculated, State Knowledge library is formed.
Further, the status indication includes：
Normal operation or fault type are marked first；
Record is measured according to history and obtains the maximum value and minimum value of the corresponding each characteristic frequency of each equipment, so that it is determined that respectively
The normal value range of characteristic frequency；
According to the normal value range, the fault level of each equipment is further marked.
Second purpose according to the present invention, the present invention also provides a kind of mechanical fault diagnosis systems in knowledge based library
System, including memory and processor, the memory storage device library, characteristic frequency knowledge base, State Knowledge library, and can be
The computer program run on processor, the processor realize following steps when executing described program, including：
The device name for receiving equipment to be monitored transfers corresponding construction parameter from equipment library；
Receive the vibration measurement result of equipment to be monitored；
Feature based frequency knowledge base, calculates the characteristic frequency spectrum of the equipment；
Based on State Knowledge library, knowledge reasoning is carried out to the characteristic frequency spectrum, obtains the operating status of the equipment；
Wherein, the computational methods of the characteristic frequency knowledge library storage characteristic frequency；
Relationship between the State Knowledge storage apparatus difference operating status and characteristic frequency.
Beneficial effects of the present invention
1, emphasis of the present invention is directed to the bearing fault and electrical fault of general machinery equipment generally existing, carries out fault diagnosis
Identification and the fault level analysis of multiple types mechanical breakdown are realized in modeling and algorithm design.It can be according to the specific of mechanized equipment
Composition parameter, such as the internal diameter of bearing, outer diameter determine 20-30 different characteristic frequencies, considerably increase diagnosis when institute foundation
The information content, therefore have the characteristics that diagnostic result is accurate.
2, the present invention uses the automatic diagnostics in knowledge based library, and knowledge base includes characteristic frequency knowledge base and equipment shape
State knowledge base.Characteristic frequency knowledge base recording equipment model and characteristic frequency set, recording equipment shape in equipment state knowledge base
The contents such as state, equipment characteristic frequency spectrum range.It, can be according to its concrete composition parameter rather than simple basis to different mechanized equipments
Equipment rotating speed determines characteristic frequency, therefore stronger feature adaptable simultaneously, reliability and maintainability are stronger.
Description of the drawings
The accompanying drawings which form a part of this application are used for providing further understanding of the present application, and the application's shows
Meaning property embodiment and its explanation do not constitute the improper restriction to the application for explaining the application.
Fig. 1 is present device fault modeling and Troubleshooting Flowchart.
Specific implementation mode
It is noted that described further below be all exemplary, it is intended to provide further instruction to the application.Unless another
It indicates, all technical and scientific terms used herein has usual with the application person of an ordinary skill in the technical field
The identical meanings of understanding.
It should be noted that term used herein above is merely to describe specific implementation mode, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singulative
It is also intended to include plural form, additionally, it should be understood that, when in the present specification using term "comprising" and/or " packet
Include " when, indicate existing characteristics, step, operation, device, component and/or combination thereof.
In the absence of conflict, the features in the embodiments and the embodiments of the present application can be combined with each other.
Embodiment one
Present embodiment discloses a kind of mechanical fault diagnosis modeling methods in knowledge based library, include the following steps：
Step 1:Receive the structural parameters of rotation class equipment.
The structural parameters include：The parameter of electric machine, bearing parameter and specific mechanical parameter.Specifically,
1) electric machinery parameter includes：Motor dominant frequency, motor speed, the motor stator number of teeth, number of teeth of motor rotor, motor are extremely right
Number.
2) bearing parameter includes：Bearing inner race diameter, bearing outer diameter, bearing ball diameter, bearing inner circle radius, axis
Hold ball number, bearing columns, bearing spiral line number.
3) device parameter includes：Equipment rotating speed, equipment bearing number, the equipment number of blade, equipment rotor series, equipment level-one turn
The sub- number of teeth, the equipment secondary rotor number of teeth, equipment gear ratio.
Step 2:Establish characteristic frequency knowledge base, the feature that the characteristic frequency knowledge base needs when including fault diagnosis
Frequency calculation formula, and the corresponding characteristic frequency set of each rotation class equipment sought according to these formula generate feature frequency
Spectrum.
Characteristic frequency T1~T26 calculation formula：
Motor speed/60.0 T1=；
T2=2.0*T1；
T3=(T1* bearing inner circle radius * bearings columns)/(2.0* (Z1+ (bearing ball diameter/2.0)))；
T4=T3* bearing ball number * bearing columns；
T5=T4*Z4* bearing columns；
T6=(T1* bearing inner circle radius * bearings columns)/bearing ball diameter；
T7=T6* bearing ball number * bearing columns；
T8=(T6-T1) * bearing columns；
T9=T8* bearing ball number * bearing columns；
T10=(T1-T3) * bearing columns；
T11=T10* bearing ball number * bearing columns；
The T12=T1* motor stator numbers of teeth；
T13=T1* number of teeth of motor rotor；
T14=T1* (number of teeth of motor rotor+motor stator number of teeth)
T15=T1* equipment number of rotor blades
T16=T1* equipment rotor series
T17=2.0* motor dominant frequency
T18=4.0* motor dominant frequency
T19=2.0* motor dominant frequency * Z3
T20=engine dominant frequency * (1-Z3)
T21=engine dominant frequency * (1-2.0*Z3)
T22=2.0* motor dominant frequency * (1-2.0*Z3)
T23=2300
T24=3150
T25=4500
T26=motor dominant frequency；
The intermediate parameters being directed to include：
Z1=(bearing inner race diameter+bearing outer diameter)/4.0- (bearing ball diameter/2.0)；
Z2=(motor dominant frequency * 60.0)/(motor numbers of pole-pairs)；
Z3=(Z2- motor speeds)/Z2
Z4=(Z1+2* bearing balls diameter)/(bearing ball diameters 2.0)；
Z5=bearing ball diameters
Step 3:State Knowledge library is established, the State Knowledge library includes each equipment difference operating status and characteristic frequency spectrum
Between relationship.The operating status is normal operation or fault type and fault level.
State Knowledge library method for building up is：
Operating status label is carried out to each equipment surface historical vibration data by expert, marks normal operation or failure classes
Type；
Record is measured according to history and obtains the maximum value and minimum value of the corresponding each characteristic frequency of each equipment, so that it is determined that respectively
The normal value range of characteristic frequency；
According to the normal value range, the fault level of each equipment is further marked；
According to above-mentioned label, it is different from each equipment to generate characteristic spectral line (amplitude of the vibration signal at different characteristic frequency)
Relationship between operating status；
Amplitude range at the corresponding characteristic frequency of each equipment difference operating status is calculated, State Knowledge library is formed.
Step 4:It treats monitoring device and carries out vibration measurement, according to the characteristic frequency set generated in step 2, calculate
Then characteristic frequency spectrum carries out knowledge reasoning to characteristic frequency spectrum according to the State Knowledge library in step 3, obtains corresponding operation shape
State.
Emphasis of the present invention is directed to the bearing fault and electrical fault of general machinery equipment generally existing, carries out fault diagnosis and builds
Identification and the fault level analysis of multiple types mechanical breakdown are realized in mould and algorithm design.
Preferably, the characteristic frequency knowledge base recording equipment model and characteristic frequency set, in equipment state knowledge base
The contents such as recording equipment model, the equipment operation time limit, equipment state, equipment characteristic frequency spectrum range.Knowledge base can be according to equipment
The factors such as model, the time limit are updated and are supplemented into Mobile state.
Embodiment two
The purpose of the present embodiment is to provide a kind of computing device.
A kind of mechanical fault diagnosis device in knowledge based library, including memory and processor, the memory are deposited
Equipment library, characteristic frequency knowledge base, State Knowledge library are stored up, and the computer program that can be run on a processor, the processing
Device realizes following steps when executing described program, including：
The device name for receiving equipment to be monitored transfers corresponding construction parameter from equipment library；
Receive the vibration measurement result of equipment to be monitored；
Feature based frequency knowledge base, calculates the characteristic frequency spectrum of the equipment；
Based on State Knowledge library, knowledge reasoning is carried out to the characteristic frequency spectrum, obtains the operating status of the equipment；
Wherein, the computational methods of the characteristic frequency knowledge library storage characteristic frequency；
Relationship between the State Knowledge storage apparatus difference operating status and characteristic frequency.
Embodiment three
The purpose of the present embodiment is to provide a kind of computer storage media.
A kind of computer storage media, including equipment library, characteristic frequency knowledge base, State Knowledge library, and can handle
The computer program run on device, the processor realize following steps when executing described program, including：
The device name for receiving equipment to be monitored transfers corresponding construction parameter from equipment library；
Receive the vibration measurement result of equipment to be monitored；
Feature based frequency knowledge base, calculates the characteristic frequency spectrum of the equipment；
Based on State Knowledge library, knowledge reasoning is carried out to the characteristic frequency spectrum, obtains the operating status of the equipment；
Wherein, the computational methods of the characteristic frequency knowledge library storage characteristic frequency；
Relationship between the State Knowledge storage apparatus difference operating status and characteristic frequency.
Beneficial effects of the present invention：
1, emphasis of the present invention is directed to the bearing fault and electrical fault of general machinery equipment generally existing, carries out fault diagnosis
Identification and the fault level analysis of multiple types mechanical breakdown are realized in modeling and algorithm design.It can be according to the specific of mechanized equipment
Composition parameter, such as the internal diameter of bearing, outer diameter determine 20-30 different characteristic frequencies, considerably increase diagnosis when institute foundation
The information content, therefore have the characteristics that diagnostic result is accurate.
2, the present invention uses the automatic diagnostics in knowledge based library, and knowledge base includes characteristic frequency knowledge base and equipment shape
State knowledge base.Characteristic frequency knowledge base recording equipment model and characteristic frequency set, recording equipment shape in equipment state knowledge base
The contents such as state, equipment characteristic frequency spectrum range.It, can be according to its concrete composition parameter rather than simple basis to different mechanized equipments
Equipment rotating speed determines characteristic frequency, therefore stronger feature adaptable simultaneously, reliability and maintainability are stronger.
It will be understood by those skilled in the art that each module or each step of aforementioned present invention can be filled with general computer
It sets to realize, optionally, they can be realized with the program code that computing device can perform, it is thus possible to which they are stored
Be performed by computing device in the storage device, either they are fabricated to each integrated circuit modules or by they
In multiple modules or step be fabricated to single integrated circuit module to realize.The present invention is not limited to any specific hardware and
The combination of software.
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects model to the present invention
The limitation enclosed, those skilled in the art should understand that, based on the technical solutions of the present invention, those skilled in the art are not
Need to make the creative labor the various modifications or changes that can be made still within protection scope of the present invention.
Claims (9)
1. a kind of Trouble Diagnostic Method of Machinery Equipment in knowledge based library, which is characterized in that include the following steps：
It receives equipment to be monitored and carries out vibration measurement；
Feature based frequency knowledge base, calculates the characteristic frequency spectrum of the equipment；
Based on State Knowledge library, knowledge reasoning is carried out to the characteristic frequency spectrum, obtains the operating status of the equipment；
Wherein, the computational methods of the characteristic frequency knowledge library storage characteristic frequency；
Relationship between the State Knowledge storage apparatus difference operating status and characteristic frequency.
2. a kind of Trouble Diagnostic Method of Machinery Equipment in knowledge based library as described in claim 1, which is characterized in that the spy
The calculating for levying frequency is structural parameters based on equipment.
3. a kind of Trouble Diagnostic Method of Machinery Equipment in knowledge based library as described in claim 1, which is characterized in that described to set
Standby structural parameters acquisition methods are：The device name for receiving equipment to be monitored transfers corresponding construction parameter from equipment library.
4. a kind of Trouble Diagnostic Method of Machinery Equipment in knowledge based library as claimed in claim 2, which is characterized in that the knot
Structure parameter includes the parameter of electric machine, bearing parameter and specific mechanical parameter.
5. a kind of Trouble Diagnostic Method of Machinery Equipment in knowledge based library as described in claim 1, which is characterized in that the fortune
Row state includes normal and failure；Further include fault level when operating status is failure.
6. a kind of Trouble Diagnostic Method of Machinery Equipment in knowledge based library as described in claim 1, which is characterized in that the shape
State Knowledge Base is：
Operating status label is carried out to each equipment surface historical vibration data, generates characteristic spectral line and each equipment difference operating status
Between relationship；
Amplitude range at the corresponding characteristic frequency of each equipment difference operating status is calculated, State Knowledge library is formed.
7. a kind of Trouble Diagnostic Method of Machinery Equipment in knowledge based library as claimed in claim 5, which is characterized in that the shape
State marks：
Normal operation or fault type are marked first；
Record is measured according to history and obtains the maximum value and minimum value of the corresponding each characteristic frequency of each equipment, so that it is determined that each feature
The normal value range of frequency；
According to the normal value range, the fault level of each equipment is further marked.
8. a kind of mechanical fault diagnosis device in knowledge based library, including memory and processor, which is characterized in that described
The computer program that memory storage can be run on a processor, the processor realize such as claim when executing described program
The Trouble Diagnostic Method of Machinery Equipment in 1-7 any one of them knowledge baseds library.
9. a kind of computer storage media, is stored thereon with computer program, such as right is executed when which is executed by processor
It is required that the Trouble Diagnostic Method of Machinery Equipment in 1-7 any one of them knowledge baseds library.
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| CN201810175974.1A CN108520093B (en) | 2018-03-02 | 2018-03-02 | Mechanical equipment fault diagnosis method and device based on knowledge base |
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| CN201810175974.1A CN108520093B (en) | 2018-03-02 | 2018-03-02 | Mechanical equipment fault diagnosis method and device based on knowledge base |
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Cited By (2)
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| CN110057583A (en) * | 2019-03-01 | 2019-07-26 | 西人马(西安)测控科技有限公司 | A kind of bearing fault recognition methods, device and computer equipment |
| CN113988202A (en) * | 2021-11-04 | 2022-01-28 | 季华实验室 | Mechanical arm abnormal vibration detection method based on deep learning |
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| Publication number | Publication date |
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| CN108520093B (en) | 2020-08-18 |
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