CN107167497A - A kind of equipment fault detection method and system - Google Patents
A kind of equipment fault detection method and system Download PDFInfo
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- CN107167497A CN107167497A CN201710502240.5A CN201710502240A CN107167497A CN 107167497 A CN107167497 A CN 107167497A CN 201710502240 A CN201710502240 A CN 201710502240A CN 107167497 A CN107167497 A CN 107167497A
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Abstract
The present invention discloses a kind of equipment fault detection method and system.This method includes：Obtain model parameter and rule base；Model parameter includes：Device type, device structure, measuring point quantity and distribution, measuring point attribute；Rule base includes：Fault type, corresponding Fault characteristic parameters and given threshold；The fault model of equipment is built according to model parameter binding rule storehouse, the multiple fault models of each of which device type correspondence, each fault model includes Fault characteristic parameters, the given threshold of Fault characteristic parameters, corresponding fault type；Obtain the Infrared Thermogram of each measuring point of equipment；Equipment characteristic parameter in model parameter extraction Infrared Thermogram；According to fault model and equipment characteristic parameter, the failure detection result of equipment is obtained；Failure detection result includes trouble location, failure severity level.Using the method and system of the present invention, running status that can be in real time to equipment is analyzed, and realizes on-line checking, improves the accuracy of detection of equipment fault.
Description
Technical field
The present invention relates to Intelligent Measurement field, more particularly to a kind of equipment fault detection method and system.
Background technology
In Condition Detection field, infrared detection technology is one kind of equipment fault detection technique, particularly with electric
Device type, generally carries out fault detect using infrared detection technology.The parts of operational outfit occur abrasion, fatigue, make an exception,
During the failures such as deformation, burn into loosening, melting, material degradation and abnormal vibrations, temperature change can directly or indirectly occur, if
Standby overall or local thermal balance is affected by destruction or influenceed, and by the transmission of heat, the heat of device interior is inevitable progressively
The appearance of equipment is transferred to, the change of external skin temperatures is caused, these may be by infrared hot thermal imaging system detection.
The operation principle of thermal infrared imager is to be detected and measured radiation using optoelectronic device, and in radiation and surface temperature
Between set up and connect each other.All objects higher than absolute zero (- 273 DEG C) can all send infra-red radiation.Thermal infrared imager profit
The infrared energy of measured target is received with infrared detector and optical imagery object lens, infrared acquisition is arrived in graphically reflection
On the light-sensitive element of device, so as to obtain Infrared Thermogram, this thermography is corresponding with the heat distribution of body surface.Generically
Say, thermal infrared imager is exactly that the invisible infrared energy for sending object is changed into visible thermal image.Different face on thermal image
Color represents the different temperatures of testee.By checking thermal image, it is observed that the bulk temperature distribution situation of measured target,
The heat condition of goal in research, so as to carry out the judgement of further work.Therefore, existing equipment fault detection method is typically
By post analysis thermography, the failure situation of equipment is then judged, have one with certain time delay, and to analysis personnel
Fixed skill requirement, the precision for causing equipment fault to detect is not high.
The content of the invention
It is an object of the invention to provide a kind of equipment fault detection method and system, to realize that real-time online is detected, improve
The accuracy of detection of equipment fault.
To achieve the above object, the invention provides following scheme：
A kind of equipment fault detection method, methods described includes：
Obtain model parameter and rule base；The model parameter includes：Device type, device structure, measuring point quantity and point
Cloth, measuring point attribute；Different device type correspondences different device structure, measuring point quantity and distribution and measuring point attribute；The rule
Then storehouse includes：The given threshold and failure of the corresponding Fault characteristic parameters of fault type, fault type, the Fault characteristic parameters
The logic rules of diagnosis；
The fault model of equipment is built according to the model parameter binding rule storehouse, each of which device type correspondence is multiple
Fault model, each fault model includes Fault characteristic parameters, the given threshold of the Fault characteristic parameters, corresponding failure
Type；
Obtain the Infrared Thermogram of each measuring point of equipment；
Equipment characteristic parameter in the Infrared Thermogram according to the model parameter extraction；
According to the fault model and the equipment characteristic parameter, according to being obtained the logic rules of the fault diagnosis
The failure detection result of equipment；The failure detection result includes trouble location, failure severity level.
Optionally, the rule base also includes：The corresponding treatment advice of the fault type and failure cause.
Optionally, the rule base is Open architecture, the logic rules of new fault diagnosis and/or new for adding
Fault type, the new fault type new Fault characteristic parameters of correspondence and new given threshold.
Optionally, the model parameter also includes：Temperature/temperature rise canonical parameter and alarm parameters, for judging that each is surveyed
The temperature of point/temperature rise surveys whether actual value is more than the canonical parameter, and surveying actual value when temperature/temperature rise of the measuring point is more than institute
When stating canonical parameter, the alarm parameters are exported.
Optionally, it is described according to the fault model and the equipment characteristic parameter, obtain the fault detect of the equipment
As a result, specifically include：
According to the equipment characteristic parameter, the corresponding fault model of the equipment is extracted, the corresponding failure mould of the equipment
Type is the corresponding fault model of device type belonging to the equipment；
Equipment characteristic parameter fault model corresponding with the equipment is contrasted one by one, according to the fault diagnosis
Logic rules obtain the corresponding fault type of the equipment；
Each fault type corresponding for the equipment, according to fault type and the model parameter, determines failure classes
The corresponding trouble location of type；
It is compared according to equipment characteristic parameter Fault characteristic parameters corresponding with fault type, obtains both differences
Value；
According to the logic rules in the rule base, logic weighted calculation is carried out to the difference, fault type is determined
Failure menace level；The corresponding trouble location of the corresponding all fault types of the equipment and failure menace level are obtained successively.
Optionally, after the failure detection result for obtaining the equipment, in addition to：
Corresponding treatment advice and failure cause are obtained according to the fault model.
Optionally, it is described according to the fault model and the equipment characteristic parameter, obtain the fault detect of the equipment
As a result after, in addition to：
The comparative analysis of longitudinal historical data：Temperature is generated according to the historical data of same observation station and/or temperature rise trend is bent
Line, for being analyzed historical trend and being predicted to Future Data；
The comparative analysis of horizontal same category of device data：To the infrared figure of the different measuring points of the identical measurement position of same category of device
As carrying out contrast displaying, the temperature/temperature rise parameter and Temperature Distribution of same category of device are obtained.
A kind of equipment fault detecting system, the system includes：Image collecting device, pattern recognition device, infrared analysis
Device, image display device；
The input of described image identifying device connects the output end of described image harvester, for receiving described image
The Infrared Thermogram of each measuring point of the equipment of harvester collection, extracts corresponding with model parameter in the Infrared Thermogram set
Standby characteristic parameter；The model parameter includes：Device type, device structure, measuring point quantity and distribution, measuring point attribute；
The input of the infrared analysis device connects the output end of described image identifying device, for according to described image
The equipment characteristic parameter that identifying device is extracted, exports the failure detection result of the equipment；The failure detection result includes event
Hinder position, failure severity level；
The input of described image display device connects the output end of the infrared analysis device, for showing the failure
Testing result.
Optionally, the system also includes：
First warning device, the input of first warning device connects the output end of described image identifying device, when
When the temperature of the measuring point is beyond the temperature threshold of setting, the first alarm activation alarm, and it is different to export fault detect
Normal information is to described image display device；
Second warning device, the input of second warning device connects the output end of described image harvester, root
Start collection prompting alarm according to the collection period of described image harvester.
Optionally, the system passes through the Quick Response Code or bar code of the measuring point of the equipment, control described image collection dress
Put and be automatically positioned the measuring point progress IMAQ.
The specific embodiment provided according to the present invention, the invention discloses following technique effect：
Corresponding evaluation can be provided to equipment state automatically after data acquisition, quickly provide the running status of equipment,
In a large amount of test datas there is the equipment of major defect or major hidden danger in rapid screening, to mitigate user working strength,
Improve operating efficiency, the actual effect of lifting diagnostic result to be significant, meet the development trend of intelligence instrument.It is more convenient for surveying
The management of data is tried, Trend tracing, historical comparison and across comparison is conveniently realized, data and graphical analysis is more facilitated, it is right
Equipment fault diagnosis and equipment state assessment are more convenient；Simultaneously according to temperature trend curve, temperature rise speed can be quickly calculated, is predicted
Temperature development, schedule ahead Maintenance and Repair preparation, it is to avoid emergency occurs, can will shut down influence and be preferably minimized.It is logical
Remote control and communication function are crossed, is realized with the mode of remote control is convenient to there is the test of particular/special requirement occasion equipment.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to institute in embodiment
The accompanying drawing needed to use is briefly described, it should be apparent that, drawings in the following description are only some implementations of the present invention
Example, for those of ordinary skill in the art, without having to pay creative labor, can also be according to these accompanying drawings
Obtain other accompanying drawings.
Fig. 1 is the flow chart of present device fault detection method embodiment 1；
Fig. 2 is present device fault detection system structure chart；
Fig. 3 is the schematic flow sheet of present device fault detection method embodiment 2；
Fig. 4 is the modeling procedure figure corresponding with embodiment 2 of present device fault detection method embodiment 1.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
In order to facilitate the understanding of the purposes, features and advantages of the present invention, it is below in conjunction with the accompanying drawings and specific real
Applying mode, the present invention is further detailed explanation.
Fig. 1 is the flow chart of present device fault detection method embodiment 1.As shown in figure 1, methods described includes：
Step 101：Obtain model parameter and rule base；The different Fault Model of different types of equipment correspondence is (same
Kind equipment uses identical Fault Model), Fault Model is according to model parameter and rule base foundation, model ginseng
Number includes：Device type, device structure, measuring point quantity and distribution, measuring point attribute, temperature/temperature rise canonical parameter, alarm parameters
Deng；Different device type correspondences different device structure, measuring point quantity and distribution and measuring point attribute.Temperature is set in model parameter
Degree/temperature rise canonical parameter and the purpose of alarm parameters are：Whether alarm parameters surpass as simple warning function to measuring point temperature
The alarm temperature absolute standard for going out setting carries out simple automatic Evaluation, on the one hand reminds staff to carry out emphasis row to this measuring point
Look into, on the other hand this evaluation result can be contributed in the diagnosis of equipment, last diagnostic is influenceed by corresponding computational algorithm
Conclusion.Measuring point temperature, which is not alarmed, does not mean that equipment is normal, and measuring point temperature alarming is certain to have exception along with equipment.
Rule base is interior to be directed to built in different types of equipment abundant Failure Diagnostic Code, can be achieved to the event of 90% equipment
The efficient diagnosis analysis of barrier, rule base includes：The corresponding Fault characteristic parameters of fault type, fault type and Fault characteristic parameters
Given threshold and fault diagnosis logic rules；The corresponding failure cause of fault type and processing method can also be included.Rule
Then Open architecture is taken in storehouse, can be supplemented perfect according to the new rule found in research and practical application, it is one
Individual sustained improvement, the database of continuous updating.
Step 102：Build fault model.The fault model of equipment is built according to model parameter binding rule storehouse, wherein often
The multiple fault models of one device type correspondence, each fault model includes the setting of Fault characteristic parameters, Fault characteristic parameters
Threshold value, corresponding fault type.Different events are namely set up according to device parameter binding rule storehouse different in model parameter
Hinder model, each equipment can correspond to multiple fault models, each fault model includes fault type, Fault characteristic parameters
Given threshold corresponding with Fault characteristic parameters.
For example, rule base includes：Fault type and characteristic parameter, the given threshold of Diagnostic parameters, menace level are calculated
The reason for logic, all kinds of fault signatures correspondence and processing method, corresponding failure mould is formed according to device type and structure respectively
Type.Such as：For 220kV current transformers (CT) this equipment, corresponding fault type includes：Pigtail splice temperature is high, supports
Porcelain vase hot-spot, measuring transformer temperature anomaly etc., corresponding characteristic parameter is respectively：The temperature of pigtail splice position, branch
Hold Distribution of temperature rise, external temperature of measuring transformer position of porcelain vase etc..When building fault model, infrared analysis software can
Set up " factory-region-equipment-measuring point " level Four device structure tree, by the tree nodes of different levels correspondence be devices under with
Measurement position, sets the attribute of the node of different levels.Fault model can be set up in device node attribute, in measurement position
Alarm parameters can be set in nodal community.
The model set up in the infrared analysis software of PC ends is loaded into thermal infrared imager by " uploading " operation, on-the-spot test
Automatic diagnosis can be achieved.
Step 103：Obtain the Infrared Thermogram of each measuring point of equipment.To the different parts correspondence of equipment when being detected to equipment
Different measuring points are set, therefore the different Infrared Thermograms of different measuring points can be obtained, the purpose of fixed point detection is realized.
Step 104：Extract the equipment characteristic parameter in Infrared Thermogram.According to model parameter, each infrared heat of measuring point is extracted
As corresponding equipment characteristic parameter in figure, for example, extract temperature, the temperature rise parameter of electrical equipment.
Step 105：Obtain equipment testing result.According to the fault model and the equipment characteristic parameter, obtain described
The failure detection result of equipment, failure detection result includes trouble location, failure severity level etc., for example, failure detection result
It can also include：The corresponding treatment advice of fault type and failure cause etc..
Specifically obtaining equipment testing result process is：
According to equipment characteristic parameter, the corresponding fault model of the equipment is extracted, the corresponding fault model of the equipment is
The corresponding fault model of device type belonging to the equipment；
Equipment characteristic parameter is contrasted one by one with fault model, logically Rule fault type；
For each fault type, according to fault type and model parameter, the corresponding trouble location of fault type is determined；Root
It is compared according to equipment characteristic parameter Fault characteristic parameters corresponding with fault type, obtains both differences；According to rule base
Interior logic rules, carry out logic weighted calculation to difference, determine the failure menace level of fault type；
The corresponding trouble location of all fault types and failure menace level are obtained successively.
Because fault model is the fault type that the parameter rule of correspondence storehouse in model parameter is determined, model parameter bag
Measuring point quantity and distribution are included, therefore, fault type includes each measuring point information, fault type that can be in fault model is obtained
Trouble location, then can obtain corresponding treatment advice and failure cause according to the fault model.
For the failure menace level of equipment, such as the fault model that the rule base of example is built in step 102, for drawing
The high fault model of wire terminal temperature is corresponding to determine that rule includes：The temperature of single-phase CT correspondence positions is set beyond Diagnostic parameters 1
The difference of threshold value or three-phase CT correspondence positions is determined beyond the threshold value or single-phase CT correspondence positions of the setting of Diagnostic parameters 2 and other two-phases
Relative temperature difference exceed Diagnostic parameters 3 set threshold value.According to the actual value of correspondence parameter and the difference of threshold value, it is weighted
Go out order of severity score value, order of severity score value order of severity index contrast corresponding with rule base draws CT pigtail splice temperature
High corresponding fault level is spent, gained failure severity level is arranged according to order from high to low；According to failure severity level
Export from high to low, and color, each failure severity level are marked according to corresponding failure severity level on Infrared Thermogram
Different colors are corresponded to respectively, and different color evaluation machine states is pressed according to the order of severity.
In addition to carrying out fixed point fault detect, longitudinal historical data comparative analysis and horizontal same category of device can also be realized
The function of comparative analysis：
The comparative analysis of longitudinal historical data：Temperature is generated according to the historical data of same observation station and/or temperature rise trend is bent
Line, for being analyzed historical trend and being predicted to Future Data；
The comparative analysis of horizontal same category of device data：To the infrared figure of the different measuring points of the identical measurement position of same category of device
As carrying out contrast displaying, the temperature/temperature rise parameter and Temperature Distribution of same category of device are obtained, aids in examining for further failure
It is disconnected.
In addition, this method of the present invention also has the characteristics that：
(1) infrared analysis software can set up device structure tree, be devices under by the tree node correspondence of different levels
And measurement position, the attribute of the node of different levels is set.Diagnostic model can be set up in device node attribute, in measurement position
Alarm parameters can be set by putting in nodal community.
The model set up in the infrared analysis software of PC ends is loaded into thermal infrared imager by " uploading " operation, on-the-spot test
Automatic diagnosis can be achieved.Image in thermal imaging system can imported into PC ends infrared analysis by " download " operation with automatic batch
In software.
(2) data management.Infrared analysis software can realize data migration, by device structure tree and/or infrared heat
As backup, the restoring operation of figure, can mutually it be transplanted between different PC ends.Equipment measuring point that can also be by selected scope and day
Phase scope, exports as required picture format by infrared image batch and names automatically.
(3) test is reminded.By setting device node " collection period " attribute, realize that test is reminded and the alarm that expires.Root
According to the collection period of structure tree device node attribute definition, reported compared with the nearest testing time after the collection period of setting
It is alert.
(4) automatic Acquisition Environment temperature, calculates temperature rise.Temperature rise be the mathematic interpolation according to measuring point temperature and environment temperature and
Come.Built-in environment temperature induction installation, obtains the environment temperature of the equipment in test process, and remember automatically on thermal infrared imager
Record in Infrared Image Information data, corresponding temperature rise data can be automatically generated.
(5) high in the clouds is stored.Device structure tree is set up in LAN high in the clouds or internet high in the clouds by infrared analysis software,
Thermal infrared imager connects cloud device structure tree by Wi-Fi, 3G/4G network etc., realizes cloud storage.In LAN or internet
Cloud server sets up device structure tree, and thermal infrared imager sets network storage mode, correct configuration high in the clouds address, user and Mi
After code, thermal infrared imager is set up with cloud server and is connected, and is formed and cloud server device structure tree on thermal infrared imager
Corresponding network mapping address, the infrared picture of shooting can be directly stored in the corresponding position of cloud server.Infrared heat
As instrument and provided with buffer area, for the environment that temporary network signal is poor, buffer area can be temporarily stored into, treats that thermal infrared imager is automatic
The network signal of search, which reaches, is automatically sent to high in the clouds after requirement.
PC ends infrared analysis software can also be directly connected to cloud device structure tree by network settings, check, analyze red
Outer image, completes infrared intelligent diagnosis.Cloud server can support many station terminals using same device structure tree, so to pacify simultaneously
Infrared analysis software mounted in different PC ends can realize data sharing by network connection to identical device structure tree.High in the clouds
Server can also be connected by mobile terminal, when can also be checked using mobile phone A PP infrared analysis software by cloud server, point
Analysis, realizes mobile interchange.
(6) remote control and communication.It is fixedly mounted temporarily with controlled in wireless such as Wi-Fi, 3G/4G, bluetooths by mobile phone A PP
Thermal infrared imager wake up, focusing, shoot, storage, realize and be not easy to approach at some, do not allow access into or machine operation middle ring
The monitoring of the dangerous higher special occasions in border needs.The whole function of thermal infrared imager can be realized by mobile phone A PP remote controls.
Fig. 2 is present device fault detection system structure chart.As shown in Fig. 2 the system includes：Image collecting device
201st, pattern recognition device 202, infrared analysis device 203, image display device 204；The input of described image identifying device 202
The output end of end connection described image harvester 201, for receiving the equipment of the collection of described image harvester 201 each survey
The Infrared Thermogram of point, extracts equipment characteristic parameter corresponding with model parameter in the Infrared Thermogram；The model parameter
Including：Device type, device structure, measuring point quantity and distribution, measuring point attribute；The input of the infrared analysis device 203 connects
The output end of described image identifying device 202 is connect, for the equipment characteristic parameter extracted according to described image identifying device 202,
Export the failure detection result of the equipment；The failure detection result includes trouble location, menace level；Described image is shown
The input of device 204 connects the output end of the infrared analysis device 203, for showing the failure detection result.
Image collecting device 201 is made up of traditional optical imaging system, detector and signal processor, for obtaining inspection
Survey the thermal-induced imagery of target.The infra-red radiation for being detected target is focused on detector by optical imaging system, detector
Electric signal is produced, electric signal passes through the signal processor for amplifying and digitizing thermal imaging system, forms Infrared Thermogram.IMAQ
Device 201 can be that portable infrared thermal imaging system can also be in line style infrared monitoring device.Pattern recognition device 202 and infrared
Analytical equipment 203 can be computer, or integrated chip, can be integrated by integrated chip when using integrated chip
In on image collecting device 201.Image display device 204 can be the display or computer of thermal infrared imager, shifting
The display screen of dynamic terminal or Infrared Monitor System.
Described device can also include：(1) first warning device, the input connection described image of the first warning device is known
The output end of other device 202, when the temperature of the measuring point is beyond the temperature threshold of setting, the first alarm activation report
It is alert, and the abnormal information of fault detect is exported to described image display device 204.(2) storage device, the storage device it is defeated
Enter end to be connected with the output end of described image harvester 201, the output end of the storage device and the infrared analysis device
203 input connection.(3) second warning devices, the input connection described image harvester of second warning device
201 output end, collection prompting alarm is started according to the collection period of described image harvester 201.
Described device can realize the functions such as IMAQ, accident analysis detection, data management, test prompting.
(1) IMAQ：By the Quick Response Code or bar code of equipment measuring point, thermal infrared imager can be automatically positioned measuring point road
On footpath.For example, by inputting the numbering of measuring point 5 in computer end or scanning the Quick Response Code of measuring point 5, thermal infrared imager can be determined automatically
Position carries out IMAQ to measuring point 5, realizes that the image in automatic data collection, quick storage, and thermal infrared imager can be certainly with this
That moves batch imported into PC ends.
(2) accident analysis is detected：Pattern recognition device 202 combines the Infrared Thermogram of 203 pairs of collections of infrared analysis device
Accident analysis is carried out, failure judgement type obtains trouble location and failure rank, can also trigger corresponding warning device and carry out
Alarm, when failure be superior to it is a certain setting rank or fault type in fault level be higher than a certain grade when, can be at any time
Triggering alarm.Longitudinal historical data comparative analysis and horizontal same category of device comparative analysis can also be completed.
The implementation method of longitudinal historical data comparative analysis：To historical data generation temperature and/or temperature rise trend under measuring point
Curve is realized trend contrast and tracked.Diagnostic model is analyzed and predicted to historical trend, realizes trend diagnosis.
The implementation method of horizontal same category of device comparative analysis：The infrared image of the identical measurement position of same category of device is carried out
Contrast displaying, diagnostic model contrasts the temperature/temperature rise parameter and Temperature Distribution of same category of device, realizes contrast diagnosis.
(3) data management：Data migration can be realized, by equipment fault analysis result and/or Infrared Thermogram
Backup, restoring operation, can mutually be transplanted between different PC ends., can be by by selecting the equipment measuring point and date range of scope
Infrared image batch exports as required picture format and named automatically.
(4) test is reminded：By setting device node " collection period " attribute, realize that test is reminded and the alarm that expires.Root
According to the collection period of structure tree device node attribute definition, reported compared with the nearest testing time after the collection period of setting
It is alert.
Fig. 3 is the schematic flow sheet of present device fault detection method embodiment 2.As shown in figure 3, methods described includes three
Part：Image recognition section 301, fault analysis model part 302, image analyzing section 303；
One complete machine test includes measuring point data all under this machine, when a complete machine has been tested
Cheng Hou, carries out image recognition, combination failure to machine part 1, machine part 2, the corresponding Infrared Thermogram of machine part 3 respectively
Correlation model parameters in analysis model, extract corresponding equipment characteristic parameter, these characteristic parameters are exactly fault model
Characteristic parameter required for accident analysis is carried out to equipment.
Respective rule in the model parameter and rule base of fault analysis model has been correspondingly formed the failure for this equipment
Model.One equipment may correspond to many different failures, fault signature and fault threshold (the setting threshold of these failures
Value) represented with fault model.Fault signature indicates fault type, corresponding to each parameter in model parameter, threshold value
Determine the order of severity of failure.
Fault analysis model passes through the regular computing of certain diagnostic logic and fault model to the equipment characteristic parameter of extraction
Compare one by one, if characteristic parameter meets fault signature and the threshold value requirement of some fault model, correspond to generation failure classes
Type and menace level.Also be stored with the simple treatment advice for different faults type and the order of severity and can in rule base
The reason for this failure occurs (i.e. the source of trouble) can be caused, then diagnosis can export trouble location, fault type, failure simultaneously
Source, the order of severity and treatment advice etc..
As described above, an equipment may be simultaneously present different failures, by the contrast one by one to 1-n of fault model,
Complete the faulty output of institute, multiple failures simultaneously in the presence of, according to the order arrangement of menace level from high to low.Different events
Hinder the different color alarm of grade correspondence, shown in the relevant position of device structure tree.Finally give the serious level of equipment fault
Not.
Whether measuring point alarm parameters are definitely marked as simple warning function to measuring point temperature beyond the alarm temperature set
Standard carries out simple automatic Evaluation, and this evaluation result can be contributed in the diagnosis of equipment, influenceed by corresponding computational algorithm
Last diagnostic conclusion.Measuring point temperature, which is not alarmed, does not mean that device diagnostic conclusion is normal, and measuring point temperature alarming is certain along with setting
Standby diagnosis is abnormal.
The flow and method that diagnosis is completed on the infrared analysis software of PC ends are similar with thermal infrared imager.First soft
Fault analysis model is set up on part, downloading data after the completion of thermal imaging system, test is uploaded to.Operated by menu, operation is examined
Disconnected system, completes to diagnose one by one to each complete machine test in equipment tree construction.
There is notable difference for diagnosis and physical device situation, except can be carried out by adjusting fault analysis model
Optimization is outer, and the present invention is provided with a kind of open rule base, can utilize rule according to the new problem run into practical application
Edit tool is supplemented rule base and perfect, and it is a sustained improvement, the database of continuous updating.This part is integrated in
In the component of fault analysis model part.
The present invention can be applied in equipment condition monitoring field.Such as：It is power plant high voltage station electrical equipment periodic detection, all kinds of
Factory's rotary machine patrols inspection temperature detection, equipment fault analysis and life prediction etc..
Fig. 4 is the modeling procedure figure corresponding with embodiment 2 of present device fault detection method embodiment 1.Such as Fig. 4 institutes
Show, modeling procedure includes：
Step 401：Investigate machine；
Step 402：Build device structure tree, modeling；
Step 403：Upload；
Step 404：Collection in worksite, the Infrared Thermogram of collecting device；
Step 405：Field diagnostic, fault diagnosis is carried out using the model of foundation；
Step 406：Judge whether plant issue needs staff to confirm, if it is, return to step 404, is resurveyed
The Infrared Thermogram of equipment, is analyzed for staff；If not, performing step 407；
Step 407：Download；
Step 408：Further analysis, diagnosis；
Step 409：Judge whether diagnostic result has deviation, if it is, return to step 402, re-establishes model；If
It is no, perform step 4010；
Step 4010：Conclusion confirms；
Step 4011：Judge whether device model changes；If it is, return to step 402, re-establishes model；If
It is no, resurvey equipment Infrared Thermogram.
The embodiment of each in this specification is described by the way of progressive, and what each embodiment was stressed is and other
Between the difference of embodiment, each embodiment identical similar portion mutually referring to.
Specific case used herein is set forth to the principle and embodiment of the present invention, and above example is said
The bright method and its core concept for being only intended to help to understand the present invention；Simultaneously for those of ordinary skill in the art, foundation
The thought of the present invention, will change in specific embodiments and applications.In summary, this specification content is not
It is interpreted as limitation of the present invention.
Claims (10)
1. a kind of equipment fault detection method, it is characterised in that methods described includes：
Obtain model parameter and rule base；The model parameter includes：Device type, device structure, measuring point quantity and distribution, survey
Point attribute；Different device type correspondences different device structure, measuring point quantity and distribution and measuring point attribute；The rule base bag
Include：The corresponding Fault characteristic parameters of fault type, fault type, the given threshold of the Fault characteristic parameters and fault diagnosis
Logic rules；
The fault model of equipment, the multiple failures of each of which device type correspondence are built according to the model parameter binding rule storehouse
Model, each fault model includes Fault characteristic parameters, the given threshold of the Fault characteristic parameters, corresponding failure classes
Type；
Obtain the Infrared Thermogram of each measuring point of equipment；
Equipment characteristic parameter in the Infrared Thermogram according to the model parameter extraction；
According to the fault model and the equipment characteristic parameter, the equipment is obtained according to the logic rules of the fault diagnosis
Failure detection result；The failure detection result includes trouble location, failure severity level.
2. according to the method described in claim 1, it is characterised in that the rule base also includes：The fault type is corresponding
Treatment advice and failure cause.
3. according to the method described in claim 1, it is characterised in that the rule base is Open architecture, new for adding
The logic rules of fault diagnosis and/or new fault type, new Fault characteristic parameters of the new fault type correspondence and new
Given threshold.
4. according to the method described in claim 1, it is characterised in that the model parameter also includes：Temperature/temperature rise canonical parameter
And alarm parameters, temperature/temperature rise for judging each measuring point surveys whether actual value is more than the canonical parameter, when the measuring point
Temperature/temperature rise when surveying actual value and being more than the canonical parameter, export the alarm parameters.
5. according to the method described in claim 1, it is characterised in that described to be joined according to the fault model and the equipment feature
Number, obtains the failure detection result of the equipment, specifically includes：
According to the equipment characteristic parameter, the corresponding fault model of the equipment is extracted, the corresponding fault model of the equipment is
The corresponding fault model of device type belonging to the equipment；
Equipment characteristic parameter fault model corresponding with the equipment is contrasted one by one, according to the logic of the fault diagnosis
The corresponding fault type of equipment described in Rule；
Each fault type corresponding for the equipment, according to fault type and the model parameter, determines fault type pair
The trouble location answered；
It is compared according to equipment characteristic parameter Fault characteristic parameters corresponding with fault type, obtains both differences；
According to the logic rules in the rule base, logic weighted calculation is carried out to the difference, the failure of fault type is determined
Menace level；The corresponding trouble location of the corresponding all fault types of the equipment and failure menace level are obtained successively.
6. method according to claim 2, it is characterised in that after the failure detection result of the acquisition equipment,
Also include：
Corresponding treatment advice and failure cause are obtained according to the fault model.
7. according to the method described in claim 1, it is characterised in that described to be joined according to the fault model and the equipment feature
After number, the failure detection result for obtaining the equipment, in addition to：
The comparative analysis of longitudinal historical data：Temperature and/or temperature rise trend curve are generated according to the historical data of same observation station, used
In being analyzed historical trend and Future Data be predicted；
The comparative analysis of horizontal same category of device data：The infrared image of the different measuring points of the identical measurement position of same category of device is entered
Row contrast displaying, obtains the temperature/temperature rise parameter and Temperature Distribution of same category of device.
8. a kind of equipment fault detecting system, it is characterised in that the system includes：Image collecting device, pattern recognition device,
Infrared analysis device, image display device；
The input of described image identifying device connects the output end of described image harvester, for receiving described image collection
The Infrared Thermogram of each measuring point of the equipment of device collection, extracts equipment corresponding with model parameter in the Infrared Thermogram special
Levy parameter；The model parameter includes：Device type, device structure, measuring point quantity and distribution, measuring point attribute；
The input of the infrared analysis device connects the output end of described image identifying device, for being recognized according to described image
The equipment characteristic parameter that device is extracted, exports the failure detection result of the equipment；The failure detection result includes failure portion
Position, failure severity level；
The input of described image display device connects the output end of the infrared analysis device, for showing the fault detect
As a result.
9. system according to claim 8, it is characterised in that the system also includes：
First warning device, the input of first warning device connects the output end of described image identifying device, when described
When the temperature of measuring point is beyond the temperature threshold of setting, the first alarm activation alarm, and export fault detect exception
Information is to described image display device；
Second warning device, the input of second warning device connects the output end of described image harvester, according to institute
The collection period for stating image collecting device starts collection prompting alarm.
10. system according to claim 8, it is characterised in that the Quick Response Code that the system passes through the measuring point of the equipment
Or bar code, control described image harvester to be automatically positioned the measuring point and carry out IMAQ.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101819239A (en) * | 2010-04-06 | 2010-09-01 | 嘉兴学院 | Rapidly constructed transformer fault diagnosis system based on three-dimensional temperature field |
| US20120259583A1 (en) * | 2009-06-22 | 2012-10-11 | Johnson Controls Technology Company | Automated fault detection and diagnostics in a building management system |
| US20130176418A1 (en) * | 2012-01-10 | 2013-07-11 | Achalesh Kumar Pandey | Continuous infrared thermography monitoring and life management system for heat recovery steam generators |
| CN103617469A (en) * | 2013-12-18 | 2014-03-05 | 重庆大学 | Equipment failure prediction method and system of electrical power system |
| CN105548747A (en) * | 2015-12-04 | 2016-05-04 | 国网技术学院 | Equipment fault identification system and method based on infrared temperature measurement technology |
| CN106197672A (en) * | 2016-06-21 | 2016-12-07 | 国家电网公司 | A kind of transformator method of real-time based on infrared thermal imaging |
-
2017
- 2017-06-27 CN CN201710502240.5A patent/CN107167497B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120259583A1 (en) * | 2009-06-22 | 2012-10-11 | Johnson Controls Technology Company | Automated fault detection and diagnostics in a building management system |
| CN101819239A (en) * | 2010-04-06 | 2010-09-01 | 嘉兴学院 | Rapidly constructed transformer fault diagnosis system based on three-dimensional temperature field |
| US20130176418A1 (en) * | 2012-01-10 | 2013-07-11 | Achalesh Kumar Pandey | Continuous infrared thermography monitoring and life management system for heat recovery steam generators |
| CN103617469A (en) * | 2013-12-18 | 2014-03-05 | 重庆大学 | Equipment failure prediction method and system of electrical power system |
| CN105548747A (en) * | 2015-12-04 | 2016-05-04 | 国网技术学院 | Equipment fault identification system and method based on infrared temperature measurement technology |
| CN106197672A (en) * | 2016-06-21 | 2016-12-07 | 国家电网公司 | A kind of transformator method of real-time based on infrared thermal imaging |
Non-Patent Citations (1)
| Title |
|---|
| 黄立;: "第四讲 红外诊断工作程序及要求" * |
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