CN109524139A - A kind of real-time device performance monitoring method based on equipment working condition variation - Google Patents
A kind of real-time device performance monitoring method based on equipment working condition variation Download PDFInfo
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- CN109524139A CN109524139A CN201811236384.1A CN201811236384A CN109524139A CN 109524139 A CN109524139 A CN 109524139A CN 201811236384 A CN201811236384 A CN 201811236384A CN 109524139 A CN109524139 A CN 109524139A
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- G—PHYSICS
- G21—NUCLEAR PHYSICS; NUCLEAR ENGINEERING
- G21D—NUCLEAR POWER PLANT
- G21D3/00—Control of nuclear power plant
- G21D3/04—Safety arrangements
- G21D3/06—Safety arrangements responsive to faults within the plant
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- G—PHYSICS
- G21—NUCLEAR PHYSICS; NUCLEAR ENGINEERING
- G21D—NUCLEAR POWER PLANT
- G21D3/00—Control of nuclear power plant
- G21D3/001—Computer implemented control
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- 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
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract
The invention belongs to nuclear power plant equipment belief system device performance monitoring technical fields, and in particular to a kind of real-time device performance monitoring method based on equipment working condition variation.Object of the present invention is to by big data analysis method, obtain the dynamic threshold for being able to reflect equipment status parameter to large number of equipment history data, the deficiency of existing equipment parameter fixed threshold setting is made up.The invention mainly comprises step 1: data acquisition；Step 2: data prediction；Step 3: feature extraction；Step 4: module training；Step 5: iteration optimization model；Step 6: model application.The present invention establishes the prediction to measuring point variation tendency and future time instance numerical value by mathematical modeling, establishes the judgement for whether meeting measuring point data variation moving law in the past, the judgement that meets situation of the founding mathematical models to measuring point current data.
Description
Technical field
The invention belongs to nuclear power plant equipment belief system device performance monitoring technical fields, and in particular to one kind is based on setting
The real-time device performance monitoring method of standby operating condition variation.
Background technique
One of the important content of nuclear power plant equipment reliability management is exactly to carry out equipment to the important key equipment of nuclear power station
Performance monitoring, and real-time device performance monitoring is a kind of most common mode of device performance monitoring, real-time device performance monitoring
It is exactly the fault mode and phenomenon of bonding apparatus, chooses the parameter for being actually able to reflect equipment state, such as: temperature, pressure, stream
Amount, voltage, vibration, electric current, oil level, oil etc. are set by it being arranged corresponding alarm, early warning value (i.e. threshold value) to realize
Standby real-time monitoring, monitoring parameters meet or exceed threshold value, that is, alarm are generated, to remind engineer to need to pay close attention to, check that this sets
Standby virtual condition, confirmation equipment whether there is failure, and take necessary measure according to actual needs, to prevent the equipment straight
The normal operation for influencing power station is connect, in order to avoid cause power station non-programmed halt shutdown or other operation consequences.
The threshold value of real-time device performance monitoring at present is arranged, usually by engineer according to operation alarming value, the relevant technologies
Standard, specification etc. are formulated, or are determined according to engineer experience, using fixed threshold set-up mode.With power station unit
Operating status variation, monitoring parameters are larger in the difference of the various operating statuses of unit, are unable to the practical shape of real reaction equipment
State, the pre-/alarm sometimes, pre-/alarm cannot be generated in time, be not necessarily to generate pre-/alarm sometimes, produced pre-/alarm instead,
Interference is caused to the work of engineer.For this purpose, the threshold value of dynamic change is to equipment real-time energy according to the operating status of unit
Monitoring just becomes especially important.
Summary of the invention
The equipment real-time energy monitoring method based on dynamic threshold that it is an object of that present invention to provide a kind of, to large number of equipment
History data obtains the dynamic threshold for being able to reflect equipment status parameter by big data analysis method, makes up existing set
The deficiency of standby parameter fixed threshold setting enables Facilities Engineer to carry out accurately real-time monitoring to each parameter of equipment
Accurately confirm equipment state, and then accurately carries out equipment fault diagnosis.
The technical solution of the invention is as follows:
1. step 1: data acquisition includes the following steps:
Step 1: user is by the measuring point in measuring point coding, measuring point title, instrument keyword query real-time data base,
Export measuring point data；
Step 2: the historic defects information of collecting device, the Historical Monitoring report of equipment；
Step 3: being saved, being deleted to the measuring point found, the routine operations such as transform sequence.
2. step 2: data prediction includes the following steps:
Step 1: carrying out exceptional value lookup, the scope of period, including ocean temperature, environment temperature, humidity；It adopts
With the correlation analysis of linear single order, the correlation analysis of measuring point and the establishment of weight are realized, reject the ginseng of completely no correlation
Number；Rules Filtering, the error that eliminating system is generated by uncertain factor；Screened using trend, reject mutation, it is irregular fluctuation,
The period of substantial deviation floor data；Fixed threshold range is defined, it is made to be bonded operating condition completely；Start and stop rule is set；
Step 2: model pre-warning effect detection screens defect information according to (2) step in the first step；Retain
The information of early warning judgement can be carried out by Parameters variation；History parameters are extracted, the analog detection of model is carried out；According to testing result
To optimize and revise the algorithm of model；
Step 3: being determined whether to ignore the outlier according to field conditions and historical record by operator, simultaneously
Operator carries out the adjustment of data time interval according to the situation of abnormal point；If judging, abnormal point is caused by measuring instrumentss failure,
Then the data are deleted, a stepping row data missing values of going forward side by side are automatically replenished；If judging, abnormal point for equipment fault, is mentioned
The outlier is taken, further analysis processing is remained；
Step 4: shortage of data value caused by ignoring to abnormal data in above-mentioned third step is automatically replenished, before missing
Data measured value afterwards, setting take most dirty data automatically to fill or be averaged and fill function.
3. step 3: feature extraction includes the following steps:
Step 1: according to the design data of equipment, supervision document, drawing delimit parameter area, herein within the scope of carry out
Parameter is chosen, including equipment design parameter, equipment safety characterisitic parameter, equipment running status parameter；
Step 2: handled by the step (1) in data prediction by parameter normalization, take deviation percent or
Unified two kinds of methods of percentage are completed, the determination of Lai Shixian weight；Operator can suitably be adjusted parameters weighting
To verify validity, the correctness of model；
4. step 4: module training includes the following steps:
Step 1: the equipment for there is historical failure data, using machine learning and data mining based on data-driven
Algorithm is modeled, once actual monitoring data fit model alarming value, system make device failure alert automatically, and according to building
When mould determine fault mode automatic prompt equipment fault type, while can also according to model automatic Prediction failure occur when
Between, and make prompt；
Step 2: the equipment of not historical failure data is operated normally data using history and is built to carry out machine learning
Mould, the model are the sections of a normal operation, and beyond operating normally, then system makes unit exception warning automatically.
5. step 5: iteration optimization model includes the following steps:
Step 1: according to the defect information reported by mistake and failed to report, repeated data pretreatment is generated in model actual moving process
In step (1), step (3), execute optimize and revise model calculating dynamic threshold range, if model generate wrong report, sieve again
The time segment limit of the historical data under similar operating condition is selected, or adjusts the bound of dynamic threshold range according to the actual situation；
Step 2: adjusting the range, quantity, weight of modeling parameters according to the early warning effect in model actual moving process
Realize the effect of simplified model；
6. step 6: model application includes the following steps:
Step 1: the dynamic threshold model that big data analysis is obtained puts into application；
Step 2: system becomes automatically according to the time model of inside configuration in conjunction with the variation of historical failure or abnormal data
Gesture, the time that prediction failure occurs form anticipation trend curve, device failure alert or abnormal warning, including yellow or more
Rank automatically forms event log and issues mail and prompting message automatically.
The present invention has the beneficial effect that compared with prior art
1. equipment early warning: finding that the recessive abnormal and unit performance of unit that traditional monitoring means can not be found is bad early
The slight change of change.It can decompose design parameter for unit exception, indicate the difference between abnormal parameters value and history optimal value
Away from.-- trend group
2. device performance monitoring is analyzed: by the dynamic threshold of real-time monitoring mechanical seal correlation measuring point, and to history
The visualization of the comparison of optimum operating condition is presented, and makes equipment performance operation trend and Long-term change trend very clear, while providing equipment
The development trend of following a period of time.-- dynamic threshold envelope
3. equipment trend prediction: pointing out equipment performance cracking inflection point, find that the state of measuring point trend deviates early, pass through and be
Statistics calculates the time point that pre- measurement equipment concrete position will fail.-- alarm is estimated
4. device exception information tracks: abnormal development trend is tracked, by counting the association measuring point of unit exception and different
The frequency often occurred forms systemic anomalous event.-- event analysis
Detailed description of the invention
Fig. 1 is a kind of real-time device performance monitoring method flow chart based on equipment working condition variation
Specific embodiment
In the following with reference to the drawings and specific embodiments to a kind of real-time device performance monitoring method based on equipment working condition variation
It is described in detail:
The present invention calculates to illustrate that the technical solution, target are by big with the dynamic threshold of three factory owner's pump mechanical sealing of the Qin
Data mode excavates the personalized threshold value under equipment difference operating status, while carrying out intensity grade setting to dynamic threshold,
For the subsequent requirement for meeting device performance monitoring dynamic management under different operating statuses.By mathematical modeling, establish to measuring point
The judgement for whether meeting measuring point data variation moving law in the past established, is built for the prediction of variation tendency and future time instance numerical value
The vertical judgement that meets situation of the mathematical model to measuring point current data.
One, data acquire:
1. user is surveyed by the measuring point in measuring point coding, measuring point title, instrument keyword query real-time data base, export
Point data；
2. the historic defects information of collecting device, the Historical Monitoring report of equipment；
3. routine operations such as pair measuring point found is saved, deleted, transform sequences.
Two, data prediction:
1. carrying out exceptional value lookup, the scope of period, including ocean temperature, environment temperature, humidity；Using linear
The correlation analysis of single order realizes the correlation analysis of measuring point and the establishment of weight, rejects the parameter of completely no correlation；Rule
Screening, the error that eliminating system is generated by uncertain factor；It is screened using trend, rejects mutation, irregular fluctuation, substantial deviation
The period of floor data；Fixed threshold range is defined, it is made to be bonded operating condition completely；Start and stop rule is set；
Wherein, the dependency analysis process of measuring point is as follows: pushing away at any time since covariance feature can embody two measuring points
The relative variability degree of shifting and direction, therefore using covariance numerical value as measuring point correlation judgment basis.In view of different surveys
The data dimension of point is different, and the variance product using the two is normalized covariance numerical value.So related coefficient has following spy
Point: 1. related coefficient numerical values reciteds represent the power of linearly related degree between two measuring points between [0,1]；2. related
Coefficient symbols it is positive and negative, represent correlativity between measuring point and belong to positive correlation or negatively correlated.Related coefficient calculation formula are as follows:
2. model pre-warning effect detection screens defect information according to (2) step in the first step；Reservation can lead to
Cross the information that Parameters variation carries out early warning judgement；History parameters are extracted, the analog detection of model is carried out；Come according to testing result excellent
Change the algorithm of adjustment model；
3. being determined whether to ignore the outlier, while operator according to field conditions and historical record by operator
The adjustment of data time interval is carried out according to the situation of abnormal point；If judging, abnormal point is caused by measuring instrumentss failure, to this
Data are deleted, and a stepping row data missing values of going forward side by side are automatically replenished；If judging, for equipment fault, it is different to extract this for abnormal point
Constant value point remains further analysis processing；
4. due in step (2) abnormal data ignore caused by shortage of data value be automatically replenished, according to missing front and back data
Measured value, setting take most dirty data automatically to fill or be averaged and fill function.
Three, feature extraction:
1. according to the design data of equipment, supervision document, drawing delimit parameter area, herein within the scope of carry out parameter choosing
It takes, including equipment design parameter, equipment safety characterisitic parameter, equipment running status parameter；
2. handling by the step (1) in data prediction by parameter normalization, deviation percent or unified hundred are taken
Than two kinds methods are divided to complete, the determination of Lai Shixian weight；Operator can suitably could be adjusted to verify to parameters weighting
Validity, the correctness of model；
Four, module training:
1. for there is the equipment of historical failure data, using based on data-driven machine learning and data mining algorithm into
Row modeling, once actual monitoring data fit model alarming value, system makes device failure alert automatically, and according to true when modeling
Fixed fault mode automatic prompt equipment fault type, while the time that can be also occurred according to model automatic Prediction failure, and make
It prompts out；
2. pair not equipment of historical failure data, data are operated normally using history to carry out machine learning modeling, it should
Model is the section of a normal operation, and beyond operating normally, then system makes unit exception warning automatically.
Five, iteration optimization model:
1. the step according to the defect information reported by mistake and failed to report is generated in model actual moving process, in repeated data pretreatment
Suddenly (1), step (3) execute the dynamic threshold range for optimizing and revising model calculating, if model generates wrong report, screen again similar
The time segment limit of historical data under operating condition, or the bound of dynamic threshold range is adjusted according to the actual situation；
2. the range, quantity, weight for adjusting modeling parameters realize letter according to the early warning effect in model actual moving process
Change the effect of model；
Six, model application:
The dynamic threshold model that big data analysis is obtained, in Chinese Nuclear Power equipment reliability management system (ERDB)
It is monitored in three factory, the Qin, 8 main pump device performance monitorings, the time model that system is configured automatically according to inside, in conjunction with history event
The variation tendency of barrier or abnormal data, the time that prediction failure may occur, and form anticipation trend curve, device failure alert
Or abnormal warning, including yellow and the above rank, it automatically forms event log and issues mail and ERDB prompting message automatically.
Claims (6)
1. a kind of real-time device performance monitoring method based on equipment working condition variation, it is characterised in that: including step 1: data are adopted
Collection；Step 2: data prediction；Step 3: feature extraction；Step 4: module training；Step 5: iteration optimization model；Step
Six: model application；Wherein, the step two: data prediction includes the following steps:
Step 1: carrying out exceptional value lookup, the scope of period, including ocean temperature, environment temperature, humidity；Using line
Property single order correlation analysis, realize the correlation analysis of measuring point and the establishment of weight, reject the parameter of completely no correlation；Rule
It then screens, the error that eliminating system is generated by uncertain factor；Screened using trend, reject mutation, irregular fluctuation, it is serious partially
Period from floor data；Fixed threshold range is defined, it is made to be bonded operating condition completely；Start and stop rule is set；
Step 2: model pre-warning effect detection screens defect information according to (2) step in the first step；Reservation can lead to
Cross the information that Parameters variation carries out early warning judgement；History parameters are extracted, the analog detection of model is carried out；Come according to testing result excellent
Change the algorithm of adjustment model；
Step 3: being determined whether to ignore the outlier according to field conditions and historical record by operator, operate simultaneously
Member carries out the adjustment of data time interval according to the situation of abnormal point；If judging, abnormal point is caused by measuring instrumentss failure, right
The data are deleted, and a stepping row data missing values of going forward side by side are automatically replenished；If judging abnormal point for equipment fault, extracting should
Outlier remains further analysis processing；
Step 4: shortage of data value caused by ignoring to abnormal data in above-mentioned third step is automatically replenished, according to missing front and back number
Factually measured value, setting take most dirty data automatically to fill or be averaged and fill function.
2. a kind of real-time device performance monitoring method based on equipment working condition variation as described in claim 1, it is characterised in that:
The step one: data acquisition includes the following steps:
Step 1: user passes through the measuring point in measuring point coding, measuring point title, instrument keyword query real-time data base, export
Measuring point data；
Step 2: the historic defects information of collecting device, the Historical Monitoring report of equipment；
Step 3: being saved, being deleted to the measuring point found, the routine operations such as transform sequence.
3. a kind of real-time device performance monitoring method based on equipment working condition variation as described in claim 1, it is characterised in that:
The step three: feature extraction includes the following steps:
Step 1: according to the design data of equipment, supervision document, drawing delimit parameter area, herein within the scope of carry out parameter
It chooses, including equipment design parameter, equipment safety characterisitic parameter, equipment running status parameter；
Step 2: handle by the step (1) in data prediction by parameter normalization, deviation percent or uniformly is taken
Percentage two kinds of methods are completed, the determination of Lai Shixian weight；Operator can suitably could be adjusted to test to parameters weighting
Validity, the correctness of model of a syndrome.
4. a kind of real-time device performance monitoring method based on equipment working condition variation as described in claim 1, it is characterised in that:
The step four: module training includes the following steps:
Step 1: for there is the equipment of historical failure data, using based on data-driven machine learning and data mining algorithm
It is modeled, once actual monitoring data fit model alarming value, system make device failure alert automatically, and when according to modeling
Determining fault mode automatic prompt equipment fault type, while the time that can be also occurred according to model automatic Prediction failure, and
Make prompt；
Step 2: the equipment of not historical failure data is operated normally data using history to carry out machine learning modeling, is somebody's turn to do
Model is the section of a normal operation, and beyond operating normally, then system makes unit exception warning automatically.
5. a kind of real-time device performance monitoring method based on equipment working condition variation as described in claim 1, it is characterised in that:
The step five: iteration optimization model includes the following steps: step 1: according to wrong report is generated in model actual moving process
With the defect information failed to report, repeated data pretreatment in step (1), step (3), execute optimize and revise model calculating dynamic
Threshold range screens the time segment limit of the historical data under similar operating condition, or according to practical feelings if model generates wrong report again
The bound of condition adjustment dynamic threshold range；
Step 2: the range, quantity, weight for adjusting modeling parameters are realized according to the early warning effect in model actual moving process
The effect of simplified model.
6. a kind of real-time device performance monitoring method based on equipment working condition variation as described in claim 1, it is characterised in that:
The step six: model application includes the following steps:
Step 1: the dynamic threshold model that big data analysis is obtained puts into application；
Step 2: time model of the system automatically according to inside configuration, in conjunction with historical failure or the variation tendency of abnormal data, in advance
The time that failure occurs is surveyed, anticipation trend curve, device failure alert or abnormal warning, including yellow and the above rank are formed,
It automatically forms event log and issues mail and prompting message automatically.
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| CN201811236384.1A CN109524139B (en) | 2018-10-23 | 2018-10-23 | Real-time equipment performance monitoring method based on equipment working condition change |
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| CN109524139A true CN109524139A (en) | 2019-03-26 |
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