CN108776855A - A kind of smart machine health status evaluation method and system - Google Patents
A kind of smart machine health status evaluation method and system Download PDFInfo
- Publication number
- CN108776855A CN108776855A CN201810342018.8A CN201810342018A CN108776855A CN 108776855 A CN108776855 A CN 108776855A CN 201810342018 A CN201810342018 A CN 201810342018A CN 108776855 A CN108776855 A CN 108776855A
- Authority
- CN
- China
- Prior art keywords
- health status
- level
- index
- smart machine
- equipment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The present invention provide a kind of smart machine health status evaluation method and system the method includes：Obtain standby running state information；Equipment health status is obtained according to the relationship between the equipment running status information and equipment health status influence factor of the acquisition in the smart machine health status assessment indicator system model built in advance；The smart machine health status assessment indicator system model includes：Destination layer and the layering rule layer built based on relationship between equipment health status influence factor；Technical solution provided by the invention obtains equipment health status, and task is overhauled according to the health status arrangement of equipment by maintenance department, and priority processing state of necessity equipment deficiency can be substantially improved O&M efficiency, while ensure the safe operation of power grid.
Description
Technical field
The present invention relates to a kind of smart machine health status evaluation methods, and in particular to a kind of smart machine health status is commented
Valence method and system.
Background technology
Smart machine is the main composition part of transformer station secondary system and its electric loop.For a long time, as to one
The analysis of the electronic equipment that subsystem is monitored, controlled, adjusted and protected, smart machine running state data gets the brush-off,
Only output par, c equipment self-inspection information.The main planned maintenance of maintenance system, correction maintenance and precognition maintenance various modes.
Planned maintenance causes the waste of a large amount of resource and equipment, and correction maintenance is that electric system brings unpredictable safety hidden
Suffer from, has not adapted to the requirement of intelligent production.And since collected smart machine operation information is relatively limited, do not carry out
The Predictive Maintenance of smart machine.
Currently, smart machine is assessed frequently with non-linear, Fuzzy Processing method, such methods Finite Samples, algorithm is multiple
Miscellaneous, Appreciation gist is not comprehensive, commonly used in there are the giant mechanical and electrical equipments of more uncertain judging quota factor to carry out health
Status assessment.
Invention content
In order to solve the above-mentioned deficiency in the presence of the prior art, the present invention provides a kind of smart machine health status to comment
Valence method.
Technical solution provided by the invention is：
A kind of smart machine health status evaluation method, the method includes：
Obtain standby running state information；
It is run according to the equipment of the acquisition in the smart machine health status assessment indicator system model built in advance
Relationship between status information and equipment health status influence factor obtains equipment health status；
The smart machine health status assessment indicator system model includes：Destination layer and based on equipment health status influence
The layering rule layer that relationship is built between factor.
Preferably, the structure of the equipment health status assessment indicator system model includes：
Smart machine health status is set as destination layer；
Level-one rule layer is built based on each factor for influencing destination layer, wherein each factor corresponds to the level-one rule layer
A first class index；
Based on each first class index, the two-level index of the first class index is built according to the influence factor to first class index；
All two-level index build two level rule layer；
Preferably, the level-one rule layer and each index of two level rule layer include weight；
Level-one rule layer weight matrix is established based on the relationship between first class index, wherein each first class index is one one
Grade weight vectors；
Pass under the range of weight vectors based on each first class index, and each first class index between two-level index
System, builds the weight matrix of two-level index under the first class index；Wherein, each two-level index is a two level weight vectors.
Preferably, it is described in the smart machine health status assessment indicator system model built in advance according to the acquisition
Equipment running status information and equipment health status influence factor between relationship obtain equipment health status and include：
It is the two-level index marking within the scope of weight vectors based on equipment running status information and two-level index；
The score value of two-level index is calculated to the score value of each first class index using method for normalizing；
The score value of first class index is calculated to the healthy score of smart machine using method for normalizing；
Preset health status table is combined according to healthy score, determines equipment health status.
Preferably, the healthy score of the smart machine is calculated using method for normalizing：
In formula, HF is the healthy score of tested smart machine；M is the element number after being normalized by comparison element；For
Two-level index score.
Preferably, the two level weight vectors are calculated as follows：
ωm=ωA[i]*ωm
Wherein, ωm：Two level weight vectors；i：0,1,2,3 or 4；ωA：The relative weighting that element sorts under destination layer.
Preferably, the level-one weight vectors are calculated as follows：
AωA=λmaxAωA
Wherein, ωA：The relative weighting that element sorts under destination layer；A：Judgment matrix, judgment matrix A is by smart machine
Health status Index Influence grade combination Satty 1-9 value methods obtain；λmaxA：Matrix A maximum eigenvalue.
Preferably, the equipment running status information includes:Communications status, external environment, device resource, self-test information and
Clock synchronization state；
The communications status includes：SV communications status, GOOSE communications status and station level communications status；
The external environment：Including cabinet inside temperature and power of alterating and direct current voltage；
The device resource：Including cpu temperature and load, CPU operating voltages, communicate optical port power, memory usage and
Disk storage space；
The self-test information：Including device hardware check, fixed value checking, ac input circuit monitoring and secondary circuit prison
Depending on；
The clock synchronization state：Including clock synchronization signal condition, clock synchronization service state and time saltus step.
Preferably, described that preset health status table is combined according to healthy score, determine that equipment health status includes：
If the healthy score of equipment is 100, equipment is in health status；
If the healthy score of 85≤equipment<When 100, equipment is in the hole；
If the health status score of equipment<When 85, equipment is in state of necessity.
Preferably, the equipment health status assessment indicator system model further includes：Solution layer；
Solution layer includes：When the destination layer smart machine health status is not health status, to make the destination layer
Smart machine state is reached health status and is built based on the FAQs of first class index and two-level index alternative various
Measure, decision scheme.
A kind of smart machine health status evaluation system, the system comprises：
Apparatus information acquiring module, for obtaining standby running state information；
Health status determining module, the smart machine for building the equipment running status information input of acquisition in advance are good for
According to the equipment running status information of the acquisition and equipment health status influence factor in health state evaluation Index System Model
Between relationship obtain equipment health status.
Preferably, further include：Model construction module：For building the smart machine health status assessment indicator system mould
Type；
Smart machine health status assessment indicator system model includes：Destination layer and be based on equipment health status influence factor
Between relationship build layering rule layer.
Preferably, the model construction module, including：
Destination layer determination sub-module：For determining destination layer based on smart machine health status；
Rule layer determination sub-module：For according to influencing each other between each factor and each factor for influencing destination layer
Determine rule layer.
Preferably, the determining rule layer submodule, including：
First determination unit, for building level-one rule layer based on each factor for influencing destination layer, wherein each factor
One first class index of the corresponding level-one rule layer；
Second determination unit builds described one for being based on each first class index according to the influence factor to first class index
The two-level index of grade index；
Preferably, the health status determining module, including：
Marking submodule, for being described two within the scope of the weight vectors based on equipment running status information and two-level index
Grade index marking；
Computational submodule, the healthy score of score value and smart machine for calculating each first class index；
The score value of the first class index includes：The score value of two-level index is calculated each level-one using method for normalizing and refers to
Target score value；
The score value of the first class index includes：Smart machine is calculated using method for normalizing in the score value of first class index
Healthy score；
Determination sub-module determines equipment health status for combining preset health status table according to healthy score.
Compared with prior art, beneficial effects of the present invention are：
1, the present invention proposes a kind of equipment health status method for quantitatively evaluating, obtains standby running state information；Advance
According to the equipment running status information and equipment of the acquisition in the smart machine health status assessment indicator system model of structure
Relationship between health status influence factor obtains smart machine health status assessment indicator system model described in equipment health status
Including：Destination layer and the layering rule layer built based on relationship between equipment health status influence factor；It solves to large-scale electromechanical
Equipment carries out the incomplete problem of health state evaluation Appreciation gist, to there are the mainframes of more uncertain judging quota factor
Electric equipment can carry out health state evaluation.
2, technical solution provided by the invention, can be with quantitatively evaluating smart machine health status, and is examined in this, as state
The foundation repaiied tells state of necessity, precarious position and the health status of smart machine, solves planned maintenance and causes largely
The waste of resource and equipment, correction maintenance are the problem of electric system brings unpredictable security risk.
3, technical solution provided by the invention so that task is overhauled according to the health status arrangement of equipment by maintenance department, excellent
First handle state of necessity equipment deficiency, reprocessing wouldn't critical system safety precarious position equipment deficiency, can be substantially improved
O&M efficiency, while ensureing the safe operation of power grid.
Description of the drawings
Fig. 1 is the smart machine health status evaluation method flow chart of the present invention；
Fig. 2 is the smart machine health status evaluation system structural schematic diagram of the present invention；
Fig. 3 is the smart machine health status evaluation system model schematic diagram of the present invention.
Specific implementation mode
For a better understanding of the present invention, following will be combined with the drawings in the embodiments of the present invention, in the embodiment of the present invention
Technical solution be clearly and completely described, it is clear that described embodiments are some of the embodiments of the present invention, rather than
Whole embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not making creative work premise
Lower obtained all other embodiment, shall fall within the protection scope of the present invention.
Embodiment 1：
A kind of equipment health status method for quantitatively evaluating provided by the invention and system, as shown in Figure 1：
The smart machine health status assessment indicator system mould that the equipment running status information input of acquisition is built in advance
Type obtains equipment health status；
Smart machine health status assessment indicator system model includes：Destination layer, rule layer and solution layer.
Intelligent substation is adopted using 61850 standards of IEC, network samples and control technology and embedded intelligent equipment, information
Collection digitlization and communication network, IED can be obtained from network and transmission data；Secondly, communication protocol is unified in intelligent station, different
Equipment between producer can freely interoperate, and there is no problem for on-line monitoring information exchange.Therefore, smart machine status monitoring system
System can unify the most information of acquisition reflection equipment running status, including quantity of state and measurement.
According to equipment running status information, it is the mutual of each index in analysis system to establish tree-like hierarchy structural model
Relationship, logic ownership and importance, carry out hierarchal arrangement, constitute a top-down ladder hierarchical structure.Model evaluation
The weight of each element determines that tree-like hierarchy structural model is exactly smart machine health status assessment indicator system mould using AHP methods
Type.
One, smart machine health status assessment indicator system model, as shown in Figure 3：
Smart machine health status assessment indicator system model is Recurison order hierarchy assessment models, can generally be divided into three
Level：
(1) destination layer：Only there are one elements, i.e. smart machine health status indicator evaluation system.
(2) rule layer：Contain the intermediate link to realize involved by smart machine state health.
The running state information of smart machine includes communications status, external environment, device resource, self-test information and clock synchronization shape
Five aspects of state choose level-one rule layer evaluation index of this five aspect indexs as weighing device health status.Each one
Grade rule layer is also divided into several two level rule layers.
Communications status：Including three SV communications status, GOOSE communications status and station level communications status two level rule layers refer to
Mark.
External environment：Including cabinet inside temperature and power of alterating and direct current voltage binomial two level rule layer index.
Device resource：Including cpu temperature and load, CPU operating voltages, communicate optical port power, memory usage and disk
Five two level rule layer indexs of memory space.
Self-test information：Four are monitored including device hardware check, fixed value checking, ac input circuit monitoring and secondary circuit
Two level rule layer index.
Clock synchronization state：Including three clock synchronization signal condition, clock synchronization service state and time saltus step rule layer indexs.
(3) solution layer：It contains to realize the alternative various measures of target, decision scheme etc., i.e., smart machine is transported
Whether row state meets the requirement of influence index involved by rule layer smart machine state health.
Two, each rank hierarchical decision matrix
Hierarchical structure reflects the relationship between index, but the proportion shared in target measurement of each criterion in criterion is simultaneously
It is not necessarily identical.Analytic hierarchy process (AHP) provides 1-9 kind scaling laws, to indicate the significance level between different indexs.Assuming that compare n
A element Y={ y1,y2,y3,,ynInfluence to target, two element y are taken every timeiAnd yj, then the element a of its judgment matrix Aij
Indicate yiAnd yjTo the ratio between the influence degree of target, wherein aijValue determined by Satty 1-9 value methods.Work as aij>When 1, for
Index i ratios j is more important for target, and numerical values recited represents important degree.There must be a simultaneouslyji=(1/aij)<1, to target
For index j it is more inessential than index i, numerical values recited indicates unessential degree.Therefore judgment matrix is positive inverse matrix.
Table 1 is smart machine health status Index Influence grade.
| Grade | Specific manifestation |
| 5 | It influences the safe operation of intelligent device and then influences electric power netting safe running |
| 4 | Influence automated system safe operation |
| 3 | Influence functions of the equipments |
| 2 | Influence equipment life |
| 1 | It has little influence on |
Rule layer A judgment matrixs are established under smart machine health status assessment indicator system simulated target layer, are considered
The influence of communications status, external environment, device resource, self-test information, five aspect index of clock synchronization state, wherein equipment communications status
Exception can influence the safe operation of power grid, the abnormal obvious relation between persistence equipment of device resource and self-test message context stablizes fortune
Row, the exception of clock synchronization state are possible to influence automated system safe operation, and the defect of external environment, which will not then directly result in, to be set
The exception of standby function, therefore communications status and clock synchronization state index importance are preferential, device resource and two aspect of self-test information refer to
Mark is slightly secondary, and the influence of external environment is smaller.
The judgment matrix of two level rule layer B under level-one rule layer B indexs.GOOSE communications status and SV communications status are different
Electric power netting safe running will be often influenced, although station level communication network can influence the normal operation of electric substation automation system extremely
Equipment will not be caused the catastrophe failures such as tripping/malfunction occur, therefore station level communications status index is slightly weak.
The judgment matrix of two level rule layer C under level-one rule layer C indexs.The priority of power of alterating and direct current voltage indexes compared with
Cabinet inside temperature index is slightly higher.
The judgment matrix of two level rule layer D under level-one rule layer D indexs.The importance of five indexs is essentially identical.
The judgment matrix of two level rule layer E under level-one rule layer E indexs.The importance of four indexs is essentially identical.
The judgment matrix of two level rule layer F under level-one rule layer F indexs.Clock synchronization signal condition and clock synchronization service state two
A Indexes Abnormality will influence automation system functions, therefore importance is slightly higher compared with time saltus step index.
Three, Mode of Level Simple Sequence and consistency desired result
Establish level-one rule layer and two level rule layer corresponding judgment matrix A, B, C, D, E, F respectively from the above analysis, by
This calculates the relative weighting of single criterion element.Under destination layer, elements A1, A2, A3, A4, A5Constitute judgment matrix A.A is corresponding
Solve latent root equation A ωA=λmaxAωA, wherein λmaxAFor matrix A maximum eigenvalue, ωAFor λmaxACorresponding feature vector, table
Show the relative weighting that five elements sort under destination layer.
Take A's to correspond to λmaxANormalization characteristic vector ωA=(ω1,ω2,,ωn), (∑ ωi=1) it is index Y=
{y1,y2,ynTo the weight vector of target.By ωA=(ω1,ω2,,ωn) component ωiSize the importance of index can be arranged
Sequence.
For the error for reducing artificially quantitative, it is also necessary to the consistency of judgment matrix A.Consistency rationWhereinFor the coincident indicator of judgment matrix A, RI is random index, and RI is related with matrix exponent number.
2 Aver-age Random Consistency Index of table
| n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| RI | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |
WhenWhen, A has satisfied consistency.
Similarly, the normalization characteristic weight vector for obtaining judgment matrix B, C, D, E, F, that is, correspond to the relative weighting under rule layer
Sequence, and verify the consistency of each matrix.If the consistency that judgment matrix is not satisfied with, matrix is adjusted until it has satisfaction
Consistency.
Each two level rule layer further segments under level-one rule layer, therefore two level rule layer is to the importance of index
Sequence is also contemplated that importance ranking of the level-one rule layer to the two level rule layer factor.
ωB_final=ωA[0]*ωB, wherein ωB_finalFor the weight sequencing of each indexs of two level rule layer B, ωBFor B normalizings
The feature weight vector of change.
ωC_final=ωA[1]*ωC, wherein ωC_finalFor the weight sequencing of each indexs of two level rule layer C, ωCFor C normalizings
The feature weight vector of change.
ωD_final=ωA[2]*ωD, wherein ωD_finalFor the weight sequencing of each indexs of two level rule layer D, ωDFor D normalizings
The feature weight vector of change.
ωE_final=ωA[3]*ωE, wherein ωE_finalFor the weight sequencing of each indexs of two level rule layer E, ωEFor E normalizings
The feature weight vector of change.
ωF_final=ωA[4]*ωF, wherein ωF_finalFor the weight sequencing of each indexs of two level rule layer F, ωFFor F normalizings
The feature weight vector of change.
The weight sequencing ω of all matrixes in summarymIt can be calculated as follows：
ωm=ωA[i]*ωm
Wherein, i：0,1,2,3 or 4；ωA：Element is in the normalized feature weight vector of destination layer.
Four, repair based on condition of component analysis decision
Set device health status full marks are 100 points, the deduction if there are certain aspect defect, the quantization point of equipment health status
Number isEquipment health status is divided into health, dangerous and critical three kinds of states.If equipment health status
When score 100, equipment is in health status；If 85<Equipment health status score<When 100, equipment is in the hole, needs to pacify
Drain fast maintenance；If equipment health status score<When 85, equipment is in state of necessity, need to send someone to overhaul at once.
Embodiment 2：
Based on same inventive concept, the present invention also provides a kind of smart machine health status evaluation systems, such as Fig. 2 institutes
Show, the system comprises：
A kind of smart machine health status evaluation system, the system comprises：
Apparatus information acquiring module, for obtaining standby running state information；
Health status determining module, the smart machine for building the equipment running status information input of acquisition in advance are good for
According to the equipment running status information of the acquisition and equipment health status influence factor in health state evaluation Index System Model
Between relationship obtain equipment health status.
Preferably, further include：Model construction module：For building the smart machine health status assessment indicator system mould
Type；
Smart machine health status assessment indicator system model includes：Destination layer and be based on equipment health status influence factor
Between relationship build layering rule layer.
Preferably, the model construction module, including：
Destination layer determination sub-module：For determining destination layer based on smart machine health status；
Rule layer determination sub-module：For according to influencing each other between each factor and each factor for influencing destination layer
Determine rule layer.
Preferably, the determining rule layer submodule, including：
First determination unit, for building level-one rule layer based on each factor for influencing destination layer, wherein each factor
One first class index of the corresponding level-one rule layer；
Second determination unit builds described one for being based on each first class index according to the influence factor to first class index
The two-level index of grade index；
Preferably, the health status determining module, including：
Marking submodule, for being described two within the scope of the weight vectors based on equipment running status information and two-level index
Grade index marking；
Computational submodule, the healthy score of score value and smart machine for calculating each first class index；
The score value of the first class index includes：The score value of two-level index is calculated each level-one using method for normalizing and refers to
Target score value；
The score value of the first class index includes：Smart machine is calculated using method for normalizing in the score value of first class index
Healthy score；
Determination sub-module determines equipment health status for combining preset health status table according to healthy score.
Preferably, the computational submodule includes：First computing unit, for healthy score HF to be calculated as follows：
In formula, m：Element number after being normalized by comparison element；The score value of each index of rule layer；
Second computing unit, for two-level index weight vectors to be calculated as follows：
ωm=ωA[i]*ωm
Wherein, ωm：Weight vectors；i：0,1,2,3 or 4；ωA：The relative weighting that element sorts under destination layer；
Third computing unit, for first class index weight vectors to be calculated as follows：
AωA=λmaxAωA
Wherein, ωA：The relative weighting that element sorts under destination layer；A：Matrix；λmaxA：Matrix A maximum eigenvalue.
Preferably, the index determination sub-module includes：
Communications status unit：For obtaining SV communications status, GOOSE communications status and station level communications status；
External environment unit：For obtaining cabinet inside temperature and power of alterating and direct current voltage；
Device resource unit：For obtain cpu temperature and load, CPU operating voltages, communicate optical port power, memory uses
Rate and disk storage space；
Self-test information unit：For acquisition device hardware check, fixed value checking, ac input circuit monitoring and secondary circuit
Monitoring；
Clock synchronization state cell：For obtaining clock synchronization signal condition, clock synchronization service state and time saltus step.
Embodiment 3：
Measure and control device health status appraisement system judgment matrix is respectively：
Indicate that the weight vector of two level rule layer index importance sequence is：
ωA_final=(0.403048,0.0790856,0.136731,0.136731,0.244403),
ωB_final=(0.172735,0.172735,0.0575783),
ωC_final=(0.0263619,0.0527238),
ωD_final=(0.0273463,0.0273463,0.0273463,0.0273463,0.0273463),
ωE_final=(0.0341829,0.0341829,0.0341829,0.0341829),
ωF_final=(0.104744,0.104744,0.0349148).
Certain one board optical port of intelligent substation measure and control device generates light intensity abnormality alarming, but communicates normally, then measure and control device
Health status score value be 97.3 points, it is in the hole, personnel should be arranged to overhaul in the recent period；If optical port is damaged, there is SV/
GOOSE chain ruptures alert, and the health status score value of measure and control device is less than 83 points, is in state of necessity, should send someone to overhaul at once.
It should be understood by those skilled in the art that, embodiments herein can be provided as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, the application can be used in one or more wherein include computer usable program code computer
The computer program production implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.)
The form of product.
The application is with reference to method, the flow of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that can be realized by computer program instructions every first-class in flowchart and/or the block diagram
The combination of flow and/or box in journey and/or box and flowchart and/or the block diagram.These computer programs can be provided
Instruct the processor of all-purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine so that the instruction executed by computer or the processor of other programmable data processing devices is generated for real
The device for the function of being specified in present one flow of flow chart or one box of multiple flows and/or block diagram or multiple boxes.
These computer program instructions, which may also be stored in, can guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works so that instruction generation stored in the computer readable memory includes referring to
Enable the manufacture of device, the command device realize in one flow of flow chart or multiple flows and/or one box of block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device so that count
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, in computer or
The instruction executed on other programmable devices is provided for realizing in one flow of flow chart or multiple flows and/or block diagram one
The step of function of being specified in a box or multiple boxes.
It these are only the embodiment of the present invention, be not intended to restrict the invention, it is all in the spirit and principles in the present invention
Within, any modification, equivalent substitution, improvement and etc. done, be all contained in apply pending scope of the presently claimed invention it
It is interior.
Claims (12)
1. a kind of smart machine health status evaluation method, which is characterized in that the method includes：
Obtain equipment running status information；
According to the equipment running status of the acquisition in the smart machine health status assessment indicator system model built in advance
Relationship between information and equipment health status influence factor obtains equipment health status；
The smart machine health status assessment indicator system model includes：Destination layer and be based on equipment health status influence factor
Between relationship build layering rule layer.
2. the method as described in claim 1, which is characterized in that the structure of the equipment health status assessment indicator system model
Including：
Smart machine health status is set as destination layer；
Level-one rule layer is built based on each factor for influencing destination layer, wherein each factor corresponds to the one of the level-one rule layer
A first class index；
Based on each first class index, the two-level index of the first class index is built according to the influence factor to first class index；
All two-level index build two level rule layer.
3. method as claimed in claim 2, which is characterized in that the level-one rule layer and each index of two level rule layer are wrapped
Include weight；
Level-one rule layer weight matrix is established based on the relationship between first class index, wherein each first class index is that a level-one is weighed
Weight vector；
Relationship under the range of weight vectors based on each first class index, and each first class index between two-level index, structure
Build the weight matrix of two-level index under the first class index；Wherein, each two-level index is a two level weight vectors.
4. method as claimed in claim 3, which is characterized in that described to refer in the smart machine health status built in advance evaluation
It is obtained according to the relationship between the equipment running status information of the acquisition and equipment health status influence factor in mark system model
Equipment health status includes：
It is the two-level index marking within the scope of weight vectors based on equipment running status information and two-level index；
The score value of two-level index is calculated to the score value of each first class index using method for normalizing；
The score value of first class index is calculated to the healthy score of smart machine using method for normalizing；
Preset health status table is combined according to healthy score, determines equipment health status.
5. method as claimed in claim 4, which is characterized in that calculate the health point of the smart machine using method for normalizing
Number：
In formula, HF is the healthy score of tested smart machine；m：For the element number after being normalized by comparison element；For two level
Index obatained score.
6. method as claimed in claim 3, which is characterized in that the two level weight vectors are calculated as follows：
ωm=ωA[i]*ωm
Wherein, ωm：Two level weight vectors；i：0,1,2,3 or 4；ωA：The corresponding level-one weight vectors of the two level weight vectors
Relative weighting.
7. method as claimed in claim 3, which is characterized in that the level-one weight vectors are calculated as follows：
AωA=λmaxAωA
Wherein, ωA：The relative weighting that element sorts under destination layer；A：Judgment matrix, judgment matrix A is by smart machine health shape
State Index Influence grade combination Satty 1-9 value methods obtain；λmaxA：Matrix A maximum eigenvalue.
8. a kind of smart machine health status evaluation system, which is characterized in that the system comprises：
Apparatus information acquiring module, for obtaining standby running state information；
Health status determining module, the smart machine health for building the equipment running status information input of acquisition in advance
According between the equipment running status information of the acquisition and equipment health status influence factor in state evaluation Index System Model
Relationship obtain equipment health status.
9. a kind of smart machine health status evaluation system as claimed in claim 8, which is characterized in that further include：Model construction
Module：For building the smart machine health status assessment indicator system model；
Smart machine health status assessment indicator system model includes：Destination layer and based on being closed between equipment health status influence factor
It is the layering rule layer of structure.
10. system as claimed in claim 9, which is characterized in that the model construction module, including：
Destination layer determination sub-module：For determining destination layer based on smart machine health status；
Rule layer determination sub-module：For according to the determination that influences each other between each factor and each factor for influencing destination layer
Rule layer.
11. system as claimed in claim 10, which is characterized in that the determining rule layer submodule, including：
First determination unit, for building level-one rule layer based on each factor for influencing destination layer, wherein each factor corresponds to
One first class index of the level-one rule layer；
Second determination unit builds the level-one according to the influence factor to first class index and refers to for being based on each first class index
Target two-level index.
12. system as claimed in claim 8, which is characterized in that the health status determining module, including：
Marking submodule, for being that the two level refers within the scope of the weight vectors based on equipment running status information and two-level index
Mark marking；
Computational submodule, the healthy score of score value and smart machine for calculating each first class index；
The score value of the first class index includes：Each first class index is calculated using method for normalizing in the score value of two-level index
Score value；
The score value of the first class index includes：The health of smart machine is calculated using method for normalizing for the score value of first class index
Score；
Determination sub-module determines equipment health status for combining preset health status table according to healthy score.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810342018.8A CN108776855A (en) | 2018-04-17 | 2018-04-17 | A kind of smart machine health status evaluation method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810342018.8A CN108776855A (en) | 2018-04-17 | 2018-04-17 | A kind of smart machine health status evaluation method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108776855A true CN108776855A (en) | 2018-11-09 |
Family
ID=64033948
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810342018.8A Pending CN108776855A (en) | 2018-04-17 | 2018-04-17 | A kind of smart machine health status evaluation method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108776855A (en) |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110008235A (en) * | 2019-04-15 | 2019-07-12 | 优必爱信息技术（北京）有限公司 | Power battery health degree evaluation method, apparatus and system |
| CN111239516A (en) * | 2020-01-19 | 2020-06-05 | 广东电网有限责任公司计量中心 | Method and device for predicting service life of mutual inductor |
| CN111563692A (en) * | 2020-05-20 | 2020-08-21 | 深圳达实智能股份有限公司 | Intelligent operation and maintenance system for rail transit |
| CN111784534A (en) * | 2020-06-22 | 2020-10-16 | 国网湖南省电力有限公司 | Method and system for predicting running state of comprehensive energy metering system with multiple subsystems |
| CN112396196A (en) * | 2020-12-07 | 2021-02-23 | 上海宝康电子控制工程有限公司 | System for realizing intelligent operation and maintenance management aiming at intelligent traffic system |
| CN112418603A (en) * | 2020-10-15 | 2021-02-26 | 招商华软信息有限公司 | ETC portal system state evaluation method based on equipment health index, electronic equipment and storage medium |
| CN112948163A (en) * | 2021-03-26 | 2021-06-11 | 中国航空无线电电子研究所 | Method for evaluating influence of equipment on functional fault based on BP neural network |
| CN113159638A (en) * | 2021-05-17 | 2021-07-23 | 国网山东省电力公司电力科学研究院 | Intelligent substation layered health degree index evaluation method and device |
| CN113326863A (en) * | 2021-03-24 | 2021-08-31 | 广州大学 | Building structure health condition detection method and system and repairing scheme determination method |
| CN113723732A (en) * | 2020-05-25 | 2021-11-30 | 中国石油化工股份有限公司 | State determination method and system for centrifugal pump |
| CN113780788A (en) * | 2021-09-02 | 2021-12-10 | 青岛联众芯云科技有限公司 | Modeling method for health evaluation of embedded equipment |
| CN114091618A (en) * | 2021-11-30 | 2022-02-25 | 重庆允成互联网科技有限公司 | Industrial equipment health state diagnosis management method and device and server |
| WO2023024131A1 (en) * | 2021-08-23 | 2023-03-02 | 株洲瑞德尔冶金设备制造有限公司 | Method and apparatus for evaluating health state of sintering device |
| CN117592383A (en) * | 2024-01-19 | 2024-02-23 | 四川晟蔚智能科技有限公司 | Method, system, equipment and medium for predicting equipment health life |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106843100A (en) * | 2016-12-13 | 2017-06-13 | 国网北京市电力公司 | Substation secondary device running status level determination method and device |
-
2018
- 2018-04-17 CN CN201810342018.8A patent/CN108776855A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106843100A (en) * | 2016-12-13 | 2017-06-13 | 国网北京市电力公司 | Substation secondary device running status level determination method and device |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110008235A (en) * | 2019-04-15 | 2019-07-12 | 优必爱信息技术（北京）有限公司 | Power battery health degree evaluation method, apparatus and system |
| CN111239516A (en) * | 2020-01-19 | 2020-06-05 | 广东电网有限责任公司计量中心 | Method and device for predicting service life of mutual inductor |
| CN111563692A (en) * | 2020-05-20 | 2020-08-21 | 深圳达实智能股份有限公司 | Intelligent operation and maintenance system for rail transit |
| CN111563692B (en) * | 2020-05-20 | 2024-02-09 | 深圳达实智能股份有限公司 | Intelligent rail transit operation and maintenance system |
| CN113723732A (en) * | 2020-05-25 | 2021-11-30 | 中国石油化工股份有限公司 | State determination method and system for centrifugal pump |
| CN111784534A (en) * | 2020-06-22 | 2020-10-16 | 国网湖南省电力有限公司 | Method and system for predicting running state of comprehensive energy metering system with multiple subsystems |
| CN111784534B (en) * | 2020-06-22 | 2023-12-12 | 国网湖南省电力有限公司 | Method and system for predicting running state of comprehensive energy metering system with multiple subsystems |
| CN112418603A (en) * | 2020-10-15 | 2021-02-26 | 招商华软信息有限公司 | ETC portal system state evaluation method based on equipment health index, electronic equipment and storage medium |
| CN112396196A (en) * | 2020-12-07 | 2021-02-23 | 上海宝康电子控制工程有限公司 | System for realizing intelligent operation and maintenance management aiming at intelligent traffic system |
| CN113326863B (en) * | 2021-03-24 | 2023-08-11 | 广州大学 | Building structure health condition detection method, system and repair scheme determination method |
| CN113326863A (en) * | 2021-03-24 | 2021-08-31 | 广州大学 | Building structure health condition detection method and system and repairing scheme determination method |
| CN112948163B (en) * | 2021-03-26 | 2023-09-19 | 中国航空无线电电子研究所 | Method for evaluating influence of equipment on functional failure based on BP neural network |
| CN112948163A (en) * | 2021-03-26 | 2021-06-11 | 中国航空无线电电子研究所 | Method for evaluating influence of equipment on functional fault based on BP neural network |
| WO2022242181A1 (en) * | 2021-05-17 | 2022-11-24 | 国网山东省电力公司电力科学研究院 | Method and apparatus for evaluating health degree indexes of layers of smart substation |
| CN113159638A (en) * | 2021-05-17 | 2021-07-23 | 国网山东省电力公司电力科学研究院 | Intelligent substation layered health degree index evaluation method and device |
| WO2023024131A1 (en) * | 2021-08-23 | 2023-03-02 | 株洲瑞德尔冶金设备制造有限公司 | Method and apparatus for evaluating health state of sintering device |
| CN113780788A (en) * | 2021-09-02 | 2021-12-10 | 青岛联众芯云科技有限公司 | Modeling method for health evaluation of embedded equipment |
| CN113780788B (en) * | 2021-09-02 | 2024-04-12 | 青岛联众芯云科技有限公司 | Modeling method for health evaluation of embedded equipment |
| CN114091618A (en) * | 2021-11-30 | 2022-02-25 | 重庆允成互联网科技有限公司 | Industrial equipment health state diagnosis management method and device and server |
| CN117592383A (en) * | 2024-01-19 | 2024-02-23 | 四川晟蔚智能科技有限公司 | Method, system, equipment and medium for predicting equipment health life |
| CN117592383B (en) * | 2024-01-19 | 2024-03-26 | 四川晟蔚智能科技有限公司 | Method, system, equipment and medium for predicting equipment health life |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108776855A (en) | A kind of smart machine health status evaluation method and system | |
| CN111537939B (en) | Voltage transformer state evaluation method and device based on multi-index fusion | |
| CN103617561A (en) | System and method for evaluating state of secondary equipment of power grid intelligent substation | |
| CN106786560A (en) | A kind of power system stability characteristic automatic extraction method and device | |
| CN109359894A (en) | A kind of Application of Power Metering Instruments risk evaluating method and device based on RPN | |
| CN110826228B (en) | Regional power grid operation quality limit evaluation method | |
| CN116663747B (en) | Intelligent early warning method and system based on data center infrastructure | |
| CN111047169A (en) | Fault analysis and detection system for power grid dispatching | |
| Xu et al. | Risk‐averse multi‐objective generation dispatch considering transient stability under load model uncertainty | |
| CN106779443B (en) | Operation risk determination method and device in power dispatching | |
| Eltyshev et al. | Diagnostics of the power oil-filled transformer equipment of thermal power plants | |
| Ying et al. | Research on state evaluation and risk assessment for relay protection system based on machine learning algorithm | |
| Airoboman et al. | Contingency analysis on the nigerian power systems network | |
| Ibe et al. | Adequacy analysis and security reliability evaluation of bulk power system | |
| CN114784793A (en) | New energy power grid cascading failure prediction method based on machine learning and storage medium | |
| Wang et al. | Human Operating Risk Assessment for Outdoor Terminal Box of Electric Power | |
| CN116541748A (en) | Power grid fault diagnosis method and system based on artificial intelligence technology | |
| CN116702084A (en) | Secondary fusion on-column breaker state evaluation method based on fuzzy theory | |
| CN115951292A (en) | Electronic voltage transformer error state online evaluation method and device | |
| CN115907526A (en) | Power grid voltage sag evaluation method and system, storage medium and computing device | |
| Zhang et al. | CNN‐LSTM based power grid voltage stability emergency control coordination strategy | |
| Sanz et al. | Experimental design for a large power system vulnerability estimation | |
| Kerin et al. | Dynamic security assessment in system operation and planning—First experiences | |
| Ye et al. | The maintenance strategy for optimizing distribution transformer life cycle cost | |
| Chen et al. | A state assessment method of relay protection device based on pso optimizes svm parameters and cloud model |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181109 |
|
| RJ01 | Rejection of invention patent application after publication |