CN106596754B - The appraisal procedure and device of oil chromatography sensor availability - Google Patents

The appraisal procedure and device of oil chromatography sensor availability Download PDF

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CN106596754B
CN106596754B CN201611036247.4A CN201611036247A CN106596754B CN 106596754 B CN106596754 B CN 106596754B CN 201611036247 A CN201611036247 A CN 201611036247A CN 106596754 B CN106596754 B CN 106596754B
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valuation
value
evaluated
characteristic
variation
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CN106596754A (en
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齐波
张鹏
荣智海
王昊月
李成榕
杨祎
李程启
马艳
鞠屹
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Electric Power Research Institute of State Grid Shandong Electric Power Co Ltd
State Grid Shandong Electric Power Co Ltd
North China Electric Power University
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Electric Power Research Institute of State Grid Shandong Electric Power Co Ltd
State Grid Shandong Electric Power Co Ltd
North China Electric Power University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography

Abstract

The invention discloses the appraisal procedures and device of a kind of oil chromatography sensor availability.Wherein, this method comprises: obtaining the oil colours spectrum sensor characteristic that collection in worksite arrives under running state of transformer；Characteristic is handled, the corresponding valuation to be evaluated of characteristic is obtained, wherein valuation to be evaluated includes invalid value ratio, consecutive identical values ratio, the whole coefficient of variation, segmentation at least one of coefficient of variation unnatural proportions and factor of created gase unnatural proportions；According to valuation to be evaluated, corresponding failure discriminant value is obtained using corresponding criterion；Failure discriminant value is handled to obtain state value；State value is compared judge whether sensor is effective with tolerance.The present invention solves the technical issues of prior art can not carry out sensor availability assessment based on collection in worksite data.

Description

The appraisal procedure and device of oil chromatography sensor availability
Technical field
The present invention relates to transformer oil chromatographics to monitor field on-line, effective in particular to a kind of oil colours spectrum sensor The appraisal procedure and device of property.
Background technique
For transformer as important power transmission and transforming equipment, operational reliability is directly related to the safety fortune of entire electric system Row.For the safe and stable operation for ensureing transformer, it is often used oil dissolved gas on-line monitoring system and equipment state is supervised It surveys.It with gradually increasing for application amount, is gradually exposed the problem of sensor availability in on-line monitoring system, invalid sensor shadow The application effect of on-line monitoring system, therefore the discovery invalid sensor by corresponding method promptly and accurately are rung, for mentioning The reliability of high on-line monitoring system is of great significance.
Recent domestic had some researchs for sensor fault analysis.
Article " site assessment of transformer oil chromatographic on-Line Monitor Device is analyzed " (Soviet Union's exhibition, Ningxia electric) is utilized respectively Line and off-line method measure the mark oil of low concentration, middle concentration and high concentration, and calculate error.To realize to oil chromatography The accuracy of on-Line Monitor Device is assessed.Article " the applying As-Is analysis of transformer oil on-line chromatograph monitor device " (Guo It is big, power safety technique) it is based on same oil sample, it is for statistical analysis to online and offline data to the online of Liang Jia manufacturer Oil colours spectrum sensor is assessed, and the method that above-mentioned two articles are all based on test experiment assesses monitoring device, area Not in the method for carrying out online evaluation under equipment normal operating conditions.
(Fu Kechang, Zhejiang are big for article " sensor fault diagnosis method and its application study based on structure optimization PCA " Learn) using a large amount of historical data as training data, corresponding model is constructed, and model is further optimized and changed Into to propose corresponding Transducer fault detection and diagnostic method.It is biased toward herein to method in existing Fault Model Improvement, and be trained using a large amount of historical data as training data, be different from and carried out based on field real-time acquisition data The method of assessment.
Article " research and application of sensor fault diagnosis " (Jianping YANG, North China Electric Power University) passes through construction nonlinear filtering Wave device obtains residual signals to carry out fault diagnosis, is filtered respectively in Extended Kalman filter, recessive Kalman filtering and glug The algorithm of wave realization sensor fault diagnosis.The accident analysis and diagnosis for laying particular emphasis on nonlinear system sensor herein, bias toward The case where theoretical research, difference is analyzed in real time on site.
Article " the sensor fault diagnosis method research based on Multi-source Information Fusion " (Zhang Ji, North China Electric Power University) is with more Based on source information blending theory, using D-S evidence theory, nerual network technique and principle component analysis etc. to the sensor of system Method for diagnosing faults is studied.The research for laying particular emphasis on the fault diagnosis of big system and multisensor herein, is different from base In the method that acquisition data analyze the sensor of a certain type.
In addition, patent " a kind of appraisal procedure of flowing oil metal particle on-line monitoring sensor performance " (Xiong Zhigang, Zhejiang Jiang Zhongxin power observation and control technology Co., Ltd) sensor is assessed respectively to the gold of the consistent metallic particles of partial size and different-grain diameter The recall rate of metal particles, to realize the assessment to sensor.Patent " for capacitive touch sensor appraisal procedure and comment Estimate device " (Shi Difen Burger, German second company of microwafer science and technology) by one measuring signal of input, according to detection criteria Detection signal is formed, the assessment to sensor is realized by detection signal.Patent is " for determining the side of sensor device usability Method and device " (G. Martin, Abbott Point of Care Inc.) by determine associated electrical characteristics whether already exceed with The associated threshold level of device availability assesses sensor to realize.Patent " VALIDITY DIAGNOSIS SYSTEM FOR UREA WATER TEMPERATURESENSOR " (TAKAHASHI HIROTAKA) propose to urea water temperature Whether the diagnostic system for spending sensor, the difference by comparing temperature under different water levels are more than preset threshold to realize to sensing Device is assessed.The method that above-mentioned patent is all based on experiment assesses sensor, that is, needs to release sensor normal Operating status is placed in laboratory environment, carries out analysis assessment by applying the method that experimental signal obtains test signal, then Difference by scene real-time data collection analyze, realize in the case where normal operation of sensor, to sensor into The method of row assessment.
To sum up, the existing sensor availability appraisal procedure based on field real-time acquisition data not yet.For above-mentioned Problem, currently no effective solution has been proposed.
Summary of the invention
The embodiment of the invention provides the appraisal procedures and device of a kind of oil chromatography sensor availability, existing at least to solve There is the technical issues of technology can not carry out sensor availability assessment based on collection in worksite data.
According to an aspect of an embodiment of the present invention, a kind of appraisal procedure of oil chromatography sensor availability is provided, is wrapped It includes: obtaining the oil colours spectrum sensor characteristic that collection in worksite arrives under running state of transformer；Characteristic is handled, Obtain the corresponding valuation to be evaluated of characteristic, wherein valuation to be evaluated includes invalid value ratio, consecutive identical values number, whole change Different coefficient, segmentation at least one of coefficient of variation unnatural proportions and factor of created gase unnatural proportions；According to valuation to be evaluated, using corresponding Criterion obtain corresponding failure discriminant value；Failure discriminant value is handled to obtain state value；By state value and tolerance into Row is relatively to judge whether sensor is effective.
According to another aspect of an embodiment of the present invention, a kind of assessment device of oil chromatography sensor availability is additionally provided, It include: acquiring unit, for obtaining the oil colours spectrum sensor characteristic that collection in worksite arrives under running state of transformer；Processing Unit obtains the corresponding valuation to be evaluated of characteristic for handling characteristic, wherein valuation to be evaluated includes invalid In value ratio, consecutive identical values number, the whole coefficient of variation, segmentation coefficient of variation unnatural proportions and factor of created gase unnatural proportions extremely Few one kind；Judgement unit: for obtaining corresponding failure discriminant value using corresponding criterion according to valuation to be evaluated；Computing unit: For being handled to obtain state value to failure discriminant value；Assessment unit, for state value is compared with tolerance to Judge whether sensor is effective.
In embodiments of the present invention, using the acquisition oil colours spectrum sensor spy that collection in worksite arrives under running state of transformer The mode of sign data obtains the corresponding valuation to be evaluated of characteristic, wherein valuation to be evaluated by handling characteristic Including invalid value ratio, consecutive identical values number, the whole coefficient of variation, segmentation coefficient of variation unnatural proportions and factor of created gase anomaly ratio At least one of example, has reached according to valuation to be evaluated, has judged the whether effective purpose of sensor, to realize based on scene It acquires data mutual transmission sensor and carries out the technical effect of Effective judgement, and then solve the prior art not being based on collection in worksite number The technical issues of according to sensor availability assessment is carried out.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present invention, constitutes part of this application, this hair Bright illustrative embodiments and their description are used to explain the present invention, and are not constituted improper limitations of the present invention.In the accompanying drawings:
Fig. 1 is a kind of flow chart of the appraisal procedure of oil chromatography sensor availability according to an embodiment of the present invention；And
Fig. 2 is a kind of schematic diagram of the assessment device of oil chromatography sensor availability according to an embodiment of the present invention.
Specific embodiment
In order to enable those skilled in the art to better understand the solution of the present invention, below in conjunction in the embodiment of the present invention Attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is only The embodiment of a part of the invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill people The model that the present invention protects all should belong in member's every other embodiment obtained without making creative work It encloses.
It should be noted that description and claims of this specification and term " first " in above-mentioned attached drawing, " Two " etc. be to be used to distinguish similar objects, without being used to describe a particular order or precedence order.It should be understood that using in this way Data be interchangeable under appropriate circumstances, so as to the embodiment of the present invention described herein can in addition to illustrating herein or Sequence other than those of description is implemented.In addition, term " includes " and " having " and their any deformation, it is intended that cover Cover it is non-exclusive include, for example, the process, method, system, product or equipment for containing a series of steps or units are not necessarily limited to Step or unit those of is clearly listed, but may include be not clearly listed or for these process, methods, product Or other step or units that equipment is intrinsic.
Embodiment 1
According to embodiments of the present invention, a kind of embodiment of the method for the assessment of oil chromatography sensor availability is provided, is needed Illustrate, step shown in the flowchart of the accompanying drawings can be in a computer system such as a set of computer executable instructions It executes, although also, logical order is shown in flow charts, and it in some cases, can be to be different from herein suitable Sequence executes shown or described step.
Fig. 1 is according to the method for the embodiment of the present invention, as shown in Figure 1, this method comprises the following steps:
Step S102 obtains the oil colours spectrum sensor characteristic that collection in worksite arrives under running state of transformer.
Optionally, it in above-mentioned steps S102, is assessed compared to placing the sensors under test experiment environment, this reality Applying example, the collected data under equipment normal operating conditions are used for the assessment of sensor itself validity by sensor, so as to It realizes scene analysis in real time, the reliability of monitoring system is improved by accurately finding invalid sensor.
Step S104, handles characteristic, obtains the corresponding valuation to be evaluated of characteristic, wherein valuation to be evaluated Including invalid value ratio, consecutive identical values number, the whole coefficient of variation, segmentation coefficient of variation unnatural proportions and factor of created gase anomaly ratio At least one of example.
Optionally, in above-mentioned steps S104, invalid value ratio be can be according to the calculated this feature number of preset rules According to the ratio of middle invalid data, either the ratio of invalid data amount and global feature data volume, is also possible to invalid data The ratio of amount and valid data amount, can also be change rate etc. of the invalid data amount in different monitoring cycles.It is wherein invalid Data can be the data of apparent error, the data of missing, empty data, outrange data, negative data or abnormal data etc. Deng.The covering scope of invalid data can flexibly be set according to the required precision of voltage transformer system.Such as work as voltage transformer system When high to precise requirements, can suitably expand the covering scope of invalid data, for example, will although in range ability, may The abnormal data generated by noise jamming is included in invalid data scope, is proposed by the stable accuracy degree to sensor higher Requirement come ensure as far as possible determine effective sensor be all authentic and valid；Similarly, when voltage transformer system is to precise requirements When less high, the covering scope of invalid data can be suitably reduced, such as abnormal data will not be included in invalid data, passed through Tolerant condition is come to ensure to determine invalid sensor as far as possible all be true invalid.
Optionally, in above-mentioned steps S104, consecutive identical values can be the maximum of a certain fixed numbers in characteristic Continuous frequency of occurrence or predetermined length, data segment comprising consecutive identical numerical value number.In another stringent situation Under, can also with due regard to residual quantity, small numerical value to a certain extent will be differed also can be regarded as the fixed numbers continuously occurred, can think See, this stringent situation is dedicated to the sensor for excluding the to be likely to occur abnormal conditions blunt to reacting condition, can correspond to become The depressor system application scenarios high to precise requirements.
Optionally, in above-mentioned steps S104, the coefficient of variation (Coefficient of variation) can response data Dispersion degree, be the statistic for measuring characteristic degree of variation.The coefficient of variation can pass through formulaIt is calculated, wherein SD indicates that the standard deviation of sample data, MN indicate the average value of sample data.
When using the coefficient of variation as valuation to be evaluated, the whole coefficient of variation of characteristic both can be directly used, Can only consider characteristic in wherein one piece of data the coefficient of variation, can also by characteristic be divided into several segments it Afterwards, comprehensively consider each section of the coefficient of variation.
Optionally, in above-mentioned steps S104, factor of created gase can pass through formulaIt is calculated, Wherein, γn+1Indicate (n+1)th day factor of created gase, Cn+1Indicate the concentration of certain gas in (n+1)th day oil, CnIndicate certain in n-th day oil The concentration of gas.Above-mentioned formula be calculate factor of created gase signal, factor of created gase can according to two adjacent datas or other set The data of fixed cycle are calculated, such as hour.
Optionally, in above-mentioned steps S104, valuation to be evaluated can only include invalid value ratio, consecutive identical values number, One of which in the whole coefficient of variation, segmentation coefficient of variation unnatural proportions and factor of created gase unnatural proportions, can also include to be evaluated Valuation includes that invalid value ratio, consecutive identical values number, the whole coefficient of variation, segmentation coefficient of variation unnatural proportions and factor of created gase are different Multiple combinations in normal ratio.By experiment it is found that the type to be assessed for including is more, whether sensor is effectively sentenced Result of breaking is with regard to more accurate and comprehensive.
Step S106 obtains the corresponding failure discriminant value of every kind of valuation to be evaluated using preset according to valuation to be evaluated.
Optionally, in above-mentioned steps S106, when valuation to be evaluated only includes above-mentioned one of, so that it may only according to this kind Whether valuation to be evaluated meets preset condition, to obtain corresponding failure discriminant value.Such as may determine that the valuation to be evaluated whether In default valid interval, whether it is greater than preset condition, whether meets whether reasonable etc. the condition of preset function, distribution, with invalid Value ratio be example, it can be determined that whether invalid value ratio is greater than preset threshold, or judge inefficient function (such as square, Evolution, index, logarithm etc.) whether it is greater than preset threshold, the valuation to be evaluated of other classes is also similarly.
Step S108 is handled to obtain state value to the corresponding failure discriminant value of all valuations to be evaluated.
Optionally, in above-mentioned steps S108, according to formulaThe state value F of sensor is calculated, wherein Pi be the corresponding failure discriminant value of valuation to be evaluated, Ai be the corresponding preset weights of valuation to be evaluated, i=1,2 ... 5.
State value is compared judge whether sensor is effective with default tolerance by step S110.
Optionally, in above-mentioned steps S110, default tolerance R is obtained first, later by state value and default tolerance R It is compared, if state valueThen determine sensor failure.
S102-S110 through the above steps, using the valuation to be evaluated of proposition to the data of oil colours spectrum sensor collection in worksite It is analyzed, due to having fully considered the factors such as exceptional value, consecutive identical values, coefficient of variation and factor of created gase, and characteristic is equal It is online data, does not need to carry out test experiment.Therefore this method is more applicable for the online of oil colours spectrum sensor and comments in real time Estimate.
Further, step S102 obtains the oil colours spectrum sensor feature that collection in worksite arrives under running state of transformer Data include:
Step S1022 obtains the data that sensor is arrived in assessment moment collection in worksite, the end value as sliding section.
Specifically, in above-mentioned steps S1022, the assessment moment can be the same day assessed, can also be specific to commenting Estimate the Hour Minute Second of generation.Sliding section, which can be one, has default or regular length section, can correspond to comprising default or The characteristic of fixed amount.
Step S1024, according to the corresponding predetermined time period in sliding section, the front sensor scene for extracting the assessment moment is adopted The historical data collected, the Filling power as sliding section.
Step S1026 determines the data in sliding section, is characterized data.
Such as the H of collected 2013 Nian Yinian of certain sensor is obtained in the present embodiment altogether2Content data (wherein 2013 The loss of data in 1 to 3 January), the length in setting sliding section is 365, i.e., by December 31 1 day to 2013 January in 2013 The data of day acquisition are as characteristic.
S1022-S1026 through the above steps, in a specific embodiment, using adopting on the day of day to be assessed or at that time The oil colours modal data of collection and historical data adjacent thereto constitute the sliding section of regular length, slide the oil chromatography number in section According to being characteristic for assessing sensor availability.These characteristic direct sources and collection in worksite system, difference In the collected experimental data of off-line test institute, it is fixed for furthermore sliding the length in section, and it is a dynamic data set, When carrying out sensor availability assessment every time, a numerical value for sliding the rearmost end in section is the monitor value of day to be assessed.
Further, characteristic can be divided into different data types, such as valid data and invalid data, with Convenient for carrying out statistic of classification, or direct exclusion invalid data to characteristic to improve the precision of judgement.In a kind of embodiment In, " missing values " present in characteristic, " negative value ", " outranging value " and " null value " this partial data are known as invalid data, Remaining data is known as valid data.
The oil chromatography in section is slided by setting the sliding section of regular length in conjunction with above-mentioned steps S1022-S1026 Data are characteristic, and are classified according to data type to characteristic.Such as certain sensing is obtained in the present embodiment altogether The H of collected 2013 Nian Yinian of device2Content data (the wherein loss of data in 1 to 3 January in 2013), data format is such as Shown in table 1, the length in setting sliding section is 365, i.e., the data by acquisition December 31 1 day to 2013 January in 2013 are made It is characterized data.Characteristic should have 365, from real data it can be found that only collecting 265, lose 100, wherein Null value 2, therefore invalid data totally 100, valid data 265.
Table 1
Further, step S104 handles characteristic, obtains when valuation to be evaluated includes invalid value ratio The corresponding valuation to be evaluated of characteristic includes:
Step S104A1 screens the invalid data in characteristic, and wherein invalid data includes missing data, negative value number According to, assigning null data and at least one of outrange data；
It is corresponding invalid that characteristic is calculated according to accounting of the invalid data in characteristic in step S104A2 Value ratio.
Specifically, this method can also include being carried out using preset mark value to invalid data before step S104A2 Label.Inefficiency can be determined based on mark value at this time, such as checks the number of mark value in whole characteristic numbers In accounting.
Further, step S104 is handled characteristic when valuation to be evaluated includes consecutive identical values number, Obtaining the corresponding valuation to be evaluated of characteristic includes:
Step S104B1 screens the invalid data in characteristic, and wherein invalid data includes missing data, negative value number According to, assigning null data and at least one of outrange data；
Step S104B2 is marked invalid data using preset mark value；
Specifically, the mark value can be any numerical value without falling into Limit of J-validity, word in step S104B2 Symbol, other labels etc..In a kind of specific implementation scene, by mark value be set as " 0 " it is simpler convenient, and will not be in table Other data generate unnecessary interference.
Step S104B3 counts consecutive identical values number according to the characteristic after label, when consecutive identical number of days etc. When the second preset threshold, it is denoted as one-time continuous identical value, the corresponding consecutive identical values number of characteristic is calculated.
Specifically, in step S104B3, such as the length of data segment can be 7, when one continuous 7 data of appearance When being worth all the same, the number of the data segment comprising consecutive identical numerical value is added 1.
Further, step S104 handles characteristic, obtains when valuation to be evaluated includes the whole coefficient of variation Include: to the corresponding valuation to be evaluated of characteristic
Step S104C1 screens the invalid data in characteristic, and wherein invalid data includes missing data, negative value number According to, assigning null data and at least one of outrange data；
Step S104C2, the invalid data using the mode of valid data in characteristic, in alternative features data；
Specifically, in step S104C2, the mode of valid data, i.e., the highest number of the frequency of occurrences in valid data.Make With the mode of valid data, the influence of the outlier in valid data has been fully considered, it is possible to be interfered and generate It is much larger than or much smaller than general measure value, but this outlier is again in the range ability of sensor, when invalid data is not contained When lid outlier, the situation of data inaccuracy is then likely to occur using the average value of valid data.Using the mode of virtual value, Then it is possible to prevente effectively from the interference of improper data.
In one embodiment of the invention, the nothing in the average alternative features data of valid data can also be used Imitate data.
Step S104C3 calculates the whole coefficient of variation of replaced characteristic, obtains the corresponding entirety of characteristic The coefficient of variation.Specifically, in step S104C3, the calculation formula of the coefficient of variation are as follows:Wherein, SD is indicated The standard deviation of sample data, MN indicate the average value of sample data.
Further, step S104 carries out characteristic when valuation to be evaluated includes segmentation coefficient of variation unnatural proportions Processing, obtaining the corresponding valuation to be evaluated of characteristic includes:
Step S104D1 screens the invalid data in characteristic, and wherein invalid data includes missing data, negative value number According to, assigning null data and at least one of outrange data；
Step S104D2, the invalid data using the mode of valid data in characteristic, in alternative features data；
Characteristic is segmented by step S104D3 according to default segments, obtains multiple segmentation feature data, Middle segments is determined by the minimum stable operation cycle of sensor；
Specifically, in step S104D3, by with mode instead after characteristic be divided into k sections, the selection principle root of k Depending on the minimum stable operation cycle of sensor, if certain sensor can minimum stable operation cycle be 30 days, need to select K value appropriate is taken, so that the length of every section of characteristic is 30.
At this time, it is also possible to because characteristic is not the integral multiple of minimum stable operation cycle, and generate redundant data Situation, in this case, redundant data can be placed in any one data sectional, such as in first or final stage.
Step S104D4 calculates the segmentation coefficient of variation of each segmentation feature data, each segmentation coefficient of variation is greater than When four preset thresholds, it is denoted as exception.According to total segments, it is abnormal that the corresponding segmentation coefficient of variation of characteristic is calculated Ratio.
Further, step S104 handles characteristic, obtains feature when valuation to be evaluated includes factor of created gase The corresponding valuation to be evaluated of data includes:
Step S104E1 screens the invalid data in characteristic, and wherein invalid data includes missing data, negative value number According to, assigning null data and at least one of outrange data,
Step S104E2, the invalid data using the mode of valid data in characteristic, in alternative features data；
Step S104E3 calculates the every two adjacent acquisition moment according to two adjacent acquisition moment corresponding characteristic Between factor of created gase, by factor of created gase be greater than six preset thresholds when, be denoted as factor of created gase exception, calculate factor of created gase exception number, Obtain the corresponding factor of created gase unnatural proportions of characteristic.
Specifically, in step S104D3, to acquire a data instance daily, two neighboring acquisition moment i.e. twos' day The moment is acquired, calculates factor of created gase, calculation formula based on adjacent two days data are as follows:
Wherein, γn+1Indicate (n+1)th day factor of created gase, Cn+1Indicate the concentration of certain gas in (n+1)th day oil, CnIndicate n-th The concentration of certain gas in its oil.
First a kind of situation is only included for valuation to be evaluated as follows to be illustrated.
Further, valuation to be evaluated includes invalid value ratio, consecutive identical values number, the whole coefficient of variation, segmentation variation One of which in coefficient unnatural proportions and factor of created gase unnatural proportions.Wherein, step S106, according to valuation to be evaluated, using corresponding The criterion discriminant value that obtains failing accordingly include:
Step S106A, if valuation to be evaluated includes invalid value ratio, when determining inefficiency greater than eight preset thresholds, The corresponding failure discriminant value P1 of invalid value ratio is generated, state value is calculated according to F=P1 × A1, wherein A1 is valuation pair to be evaluated The preset weights answered；
Step S106B is equal to the 9th when determining consecutive identical number of days if valuation to be evaluated includes consecutive identical values number When preset threshold, it is denoted as one-time continuous identical value, when the number for determining consecutive identical values is greater than ten preset thresholds, is generated The corresponding failure discriminant value P2 of consecutive identical values number, is calculated state value according to F=P2 × A2, and wherein A2 is valuation to be evaluated Corresponding preset weights；；
For example, in above-mentioned steps S106B, the tenth preset threshold example is 7, i.e., when continuous 7 days data are identical, record For one-time continuous identical value.Under the application scenarios of slightly wider appearance or when with more characteristic, the tenth preset threshold can With biggish value.
Step S106C, it is pre- greater than the 11st when determining the whole coefficient of variation if valuation to be evaluated includes the whole coefficient of variation If when threshold value, generating the corresponding failure discriminant value P3 of the whole coefficient of variation, state value being calculated according to F=P3 × A3, wherein A3 is the corresponding preset weights of valuation to be evaluated；；
Step S106D presets segments to feature according to third if valuation to be evaluated includes segmentation coefficient of variation unnatural proportions Data are segmented, and when the coefficient of variation of each segmentation is greater than four preset thresholds, are denoted as exception.When determining abnormal point The number of the section coefficient of variation presets the accounting in segments in third, when being greater than 12 preset threshold, generates segmentation variation lines The corresponding failure discriminant value P4 of number unnatural proportions, is calculated state value according to F=P4 × A4, and wherein A4 is corresponding for valuation to be evaluated Preset weights；
For example, the segmentation coefficient of variation is abnormal in above-mentioned steps S106D, it can be the segmentation coefficient of variation and be greater than default threshold Value.When the number for being greater than preset threshold in all segmentation coefficient of variation is more than total number certain proportion, satisfaction is default to differentiate item Part, setting failure discriminant value is 1.
Step S106E is calculated using adjacent gas content value if valuation to be evaluated includes factor of created gase unnatural proportions and is produced gas Rate when factor of created gase is greater than six preset thresholds, is denoted as factor of created gase exception, the ratio of factor of created gase exception is counted, when factor of created gase is different When accounting of the normal number in all numbers is greater than 13 preset threshold, the corresponding discriminant value of factor of created gase unnatural proportions is generated State value is calculated according to F=P5 × A5 in P5, and wherein A5 is the corresponding preset weights of valuation to be evaluated.
For example, factor of created gase can be factor of created gase greater than preset threshold extremely in above-mentioned steps S106E.It is default when being greater than When the number of the factor of created gase of threshold value is more than total certain proportion, meet default criterion, setting failure discriminant value is 1.
Further, valuation to be evaluated includes invalid value ratio, consecutive identical values number, the whole coefficient of variation, segmentation variation One of which in coefficient unnatural proportions and factor of created gase unnatural proportions.Wherein, step S108 compares state value and tolerance Compared with to judge sensor whether effectively include:
Step S108A obtains tolerance R if valuation to be evaluated includes invalid value ratio, if state value F >=R × A1, determines Sensor failure；
Step S108B obtains tolerance R if valuation to be evaluated includes consecutive identical values number, if state value F >=R × A2, Determine sensor failure；
Step S108C obtains tolerance R, if state value F >=R × A3, sentences if valuation to be evaluated includes the whole coefficient of variation Determine sensor failure；
Step S108D obtains tolerance R, if state value F >=R if valuation to be evaluated includes segmentation coefficient of variation unnatural proportions × A4 determines sensor failure；
Step S108E obtains tolerance R if valuation to be evaluated includes factor of created gase unnatural proportions, if state value F >=R × A5, Determine sensor failure；
Include the case where that at least two are illustrated for valuation to be evaluated as follows.
Further, valuation to be evaluated includes invalid value ratio, consecutive identical values number, the whole coefficient of variation, segmentation variation At least two in coefficient unnatural proportions and factor of created gase unnatural proportions, wherein step S106 carries out processing to discriminant value and waits until shape State value includes:
Step S1062, the corresponding anticipation failure condition of every kind of acquisition valuation to be evaluated and preset weights；
Specifically, in above-mentioned steps S1062, it is contemplated that the confidence level of inhomogeneity valuation to be evaluated, can to confidence level compared with High valuation to be evaluated distributes higher weight.Meanwhile the confidence level of inhomogeneity valuation to be evaluated has with the characteristic of regional power grid again Certain relationship.It, can be to be assessed to every class in the characteristic that regional power grid is still not clear or under the application scenarios of the more golden mean of the Confucian school Value sets identical weight, such as 1.In a kind of more accurate assessment mode, whole point can also carried out to characteristic After analysis, dynamic adjusts the weight of inhomogeneity valuation to be evaluated.
Step S1064 generates the corresponding mistake of this kind of valuation to be evaluated when determining valuation to be evaluated and meeting anticipation failure condition Imitate discriminant value；
Specifically, failure discriminant value can be specific numerical value, such as lose meeting anticipation in above-mentioned steps S1064 When effect condition, which is 1；When being unsatisfactory for anticipation failure condition, the failure discriminant value is not generated, or generate the mistake Imitating discriminant value is 0.
Further, step S1064, when determining valuation to be evaluated and meeting anticipation failure condition, generate this kind it is to be assessed Being worth corresponding failure discriminant value includes:
S1064A, if valuation to be evaluated includes invalid value ratio, when determining invalid value ratio greater than the first preset threshold, Generate the corresponding failure discriminant value P1 of inefficiency；
S1064B, it is pre- equal to second when determining consecutive identical number of days if valuation to be evaluated includes consecutive identical values number If when threshold value, being denoted as one-time continuous identical value, when the number for determining consecutive identical values is greater than third predetermined threshold value, the company of generation The continuous corresponding failure discriminant value P2 of identical value；
For example, in above-mentioned steps S1064B, the second preset threshold example is 7, i.e., when continuous 7 days data are identical, note Record is one-time continuous identical value, when the number for determining consecutive identical values is greater than third predetermined threshold value, generates consecutive identical values Corresponding failure discriminant value P2.Under the application scenarios of slightly wider appearance or when with more characteristic, second and third is default Threshold value can have biggish value.
S1064C is greater than the 4th default threshold when determining the whole coefficient of variation if valuation to be evaluated includes the whole coefficient of variation When value, the corresponding failure discriminant value P3 of the whole coefficient of variation is generated；
S1064D, if valuation to be evaluated includes segmentation coefficient of variation unnatural proportions, according to the first default segments to characteristic According to being segmented, when coefficient of variation of each segmentation is greater than four preset thresholds, it is denoted as exception, when determining abnormal segmentation Accounting of the number of the coefficient of variation in the first default segments when being greater than five preset thresholds, generates the segmentation coefficient of variation pair The failure discriminant value P4 answered；
For example, the segmentation coefficient of variation is abnormal in above-mentioned steps S1064D, it can be the segmentation coefficient of variation and be greater than default threshold Value.When the number for being greater than preset threshold in all segmentation coefficient of variation is more than total number certain proportion, segmentation variation lines are generated The corresponding failure discriminant value P4 of number.
S1064E calculates factor of created gase using adjacent gas content value if valuation to be evaluated includes factor of created gase unnatural proportions, will When factor of created gase is greater than six preset thresholds, it is denoted as factor of created gase exception, counts the ratio of factor of created gase exception, it is different when determining factor of created gase When accounting of the normal number in all numbers is greater than seven preset thresholds, the corresponding failure discriminant value P5 of factor of created gase is generated.
For example, factor of created gase can be factor of created gase greater than preset threshold extremely in above-mentioned steps S1064E.It is default when being greater than When the number of the factor of created gase of threshold value is more than total certain proportion, the corresponding failure discriminant value P5 of factor of created gase is generated.
Further, step S108, to failure discriminant value handled to obtain state value include:
According to formulaThe state value F of sensor is calculated, wherein Pi is that the corresponding failure of valuation to be evaluated is sentenced Be not worth, Ai be the corresponding preset weights of valuation to be evaluated, i=1,2 ... 5；
Further, step S110, state value is compared with tolerance judge sensor whether effectively include:
Step S1102 obtains default tolerance；
Step S1104, if state valueThen determine sensor failure.
Specifically, tolerance can further finely tune the harsh degree of assessment in above-mentioned steps S1102, For example, the efficiency assessment of sensor is just tightened up when lower tolerance is arranged, it is conducive to ensure judgement effective sensor It is authentic and valid；When higher tolerance is arranged, the efficiency assessment of sensor is slightly tolerant, determines in vain conducive to ensureing Sensor is true invalid, is applicable to the system that different requirements are proposed to accuracy.
Only it is convenient for for example, setting failure discriminant value is 1, it is also 1 that each weight, which is arranged, can obtain the sensor State value F=A1 × P1+A2 × P2+A3 × P3+A4 × P4+A5 × P5, it is assumed that assessed value does not include inefficiency, and only generates Failure discriminant value P3, P4, P5 of the coefficient of variation and factor of created gase, at this time state value F=A3 × P3+A4 × P4+A5 × P5=(1 × 1)+(1 × 1)+(1 × 1)=3.It is further assumed that tolerance is set as R=60%, due to (F=3)≤(1+1+1+1) × 60%, so it may be concluded that: the sensor failure.
S106-S110 through the above steps, in the effective status that can be used for assessing sensor with multiclass valuation to be evaluated When, comprehensively consider the confidence level of every one kind value to be assessed, and by the method for setting weight, which, which is embodied in, finally has In the deterministic process of effect property, the precision of sensor availability judgement is improved, while also improving the suitable of appraisal procedure of the present invention Ying Xing allows to carry out the adjustment of adaptability according to the type of different regional power grids, not only improves universality, but also improve and comment Estimate precision.
Embodiment 2
According to embodiments of the present invention, a kind of embodiment of the method for the assessment of oil chromatography sensor availability is additionally provided, is needed It is noted that for ease of understanding and reducing redundancy description, following examples are intended merely to be described as follows a kind of situation of complexity, i.e., Valuation to be evaluated includes invalid value ratio, consecutive identical values ratio, the whole coefficient of variation, segmentation coefficient of variation unnatural proportions and produces gas All categories in rate unnatural proportions are illustrated, but are not limited thereto.It only include the embodiment of part valuation classification to be evaluated Also it can refer to.
A kind of appraisal procedure of optional oil chromatography sensor availability, includes the following steps:
Step S202 obtains the characteristic assessed sensor；
Specifically, in above-mentioned steps S202, using day to be assessed on the day of the oil colours modal data that acquires and adjacent thereto Historical data constitutes the sliding section of regular length, and sliding the oil colours modal data in section is to be used to assess sensor availability Characteristic.These characteristic direct sources and collection in worksite system are different from the collected experiment number of off-line test institute According to it is fixed for furthermore sliding the length in section, and it is a dynamic data set, is carrying out sensor availability assessment every time When, a numerical value for sliding the rearmost end in section is the monitor value of day to be assessed.
Classified according to data type to characteristic, " missing values " present in characteristic, " negative value ", " excess Journey value " and " null value " this partial data are known as invalid data, remaining data is known as valid data.
Step S204 differentiates according to the criterion based on mark value, obtains the discriminant value that fails accordingly；
Step S206 differentiates according to the criterion based on consecutive identical values, obtains the discriminant value that fails accordingly；
Step S208 differentiates according to the criterion based on the coefficient of variation, obtains the discriminant value that fails accordingly；
Step S210 differentiates according to the criterion based on factor of created gase, obtains the discriminant value that fails accordingly；
Step S212 distributes weight to each failure discriminant value after obtaining the corresponding failure discriminant value of each criterion Calculate state value；
Step S214, by be arranged tolerance, obtain differentiate as a result, if state value be more than tolerance if sensor failure, Otherwise, sensor is effective.
Specifically, step S204 includes:
S6 uses the invalid data in mark value " 0 " marker characteristic data；
S8 counts the distribution situation of mark value；
S10 is differentiated using the criterion based on mark value, obtains failure discriminant value.Criterion is denoted as T1: of mark value Number is more than the t1% of whole characteristic numbers.If meeting criterion, note failure discriminant value P1=1, if being unsatisfactory for criterion, note failure Discriminant value P1=0.
In the present embodiment, the threshold value t1=40 in criterion T1 is set, i.e. the number of " mark value " is more than whole characteristics According to number 40% when, criterion set up.By real data it is found that criterion is invalid, failure discriminant value P1=0 is obtained；
Specifically, step S206 includes:
S12 uses the invalid data in mark value " 0 " marker characteristic data
There is the case where consecutive identical values in S14, statistics；
S16 is differentiated using the criterion based on consecutive identical values, obtains failure discriminant value.Criterion is denoted as T2: continuous T21 days data are identical, and consecutive identical data segment is more than t22.If meeting criterion, note failure discriminant value P2=1, if not Meet criterion, note failure discriminant value P2=0.
T21=7, the t22=1 in criterion T2 are set in the present embodiment, i.e., the continuous number for occurring identical value for 7 days is more than one A, criterion is set up.By real data it is found that criterion is invalid, failure discriminant value P2=0 is obtained；
Specifically, step S208 includes:
S18 substitutes invalid value using the mode of virtual value
S20, by with mode instead after characteristic be divided into k sections, the selection principle of k is stablized according to the minimum of sensor Depending on the cycle of operation, if certain sensor can minimum stable operation cycle be 30 days, need to choose k value appropriate, so that often The length of section characteristic is 30, and the data of redundancy can be placed in first or final stage；
S22, the coefficient of variation of global feature data and every section of characteristic after calculating instead；
S24 is differentiated using the criterion based on the coefficient of variation, obtains failure discriminant value.Criterion is denoted as T3: characteristic The whole coefficient of variation is more than t3%.If meeting criterion, note failure discriminant value P3=1, if being unsatisfactory for criterion, note failure discriminant value P3=0；
S26 is differentiated using the criterion based on the coefficient of variation, obtains failure discriminant value.Criterion is denoted as T4: all variations Number in coefficient greater than t3% is more than the t4% (i.e. > k × t4%) of total number.If meeting criterion, note failure discriminant value P4= 1, if being unsatisfactory for criterion, note failure discriminant value P4=0.
In the present embodiment, the t3=30 in criterion T3 is set, and when the whole coefficient of variation is more than 30%, criterion establishment.By Real data obtains failure discriminant value P3=1 it is found that criterion establishment；
Taking minimum stable period is one month, global feature data is divided into 12 sections, redundancy section is placed on the first segmentation It is interior, be arranged criterion T4 in t4=40 and 12 coefficient of variation in, when the number greater than 0.3 is more than 40% × 12, criterion at It is vertical.By real data it is found that criterion is set up, failure discriminant value P4=1 is obtained；
Specifically, step S210 includes:
S28 substitutes invalid value using the mode of virtual value；
S30 calculates factor of created gase, calculation formula based on adjacent two days data are as follows:
Wherein, γn+1Indicate (n+1)th day factor of created gase, Cn+1Indicate the concentration of certain gas in (n+1)th day oil, CnIndicate n-th The concentration of certain gas in its oil.
S32 is differentiated using the criterion based on factor of created gase, obtains failure discriminant value.Criterion is denoted as T5: factor of created gase is greater than The number of t51 is more than the t52% of sum.If meeting criterion, note failure discriminant value P5=1, if being unsatisfactory for criterion, note failure differentiates Value P5=0.
In the present embodiment, factor of created gase is calculated according to formula (1), t51=0.063, t52=40 in criterion T5 are set, that is, produced Gas rate is more than 0.063 to think abnormal, and when abnormal number is more than the 10% of whole factor of created gase numbers, criterion is set up.By reality Data obtain failure discriminant value P5=1 it is found that criterion is invalid；
Specifically, step S212 includes:
S34 is distributed respectively for failing discriminant value Pi (i=1 ..., 5) obtained in claim 2 to claim 6 Weight Ai (i=1 ..., 5), and Ai ∈ (0,1]；
S36 sums after each failure discriminant value is multiplied with weight, can be obtained the state value F=A1 of the sensor × P1+A2×P2+A3×P3+A4×P4+A5×P5。
In the present embodiment, each weight being arranged in this example is A1=A2=A3=A4=A5=1, and state value F is calculated =(1 × 0)+(1 × 0)+(1 × 1)+(1 × 1)+(1 × 1)=3.
Specifically, step S214 includes:
S38, setting tolerance is R, if sensor status values F≤(A1+A2+A3+A4+A5) × R, judges the sensor Failure, if sensor status values F < (A1+A2+A3+A4+A5) × R, judges that the sensor is effective.
It is R=60% that tolerance is arranged in the present embodiment, since (F=3)≤(1+1+1+1+1) × 60%, can obtain Conclusion out: the sensor failure.
Embodiment 3
According to embodiments of the present invention, a kind of Installation practice of the assessment device of oil chromatography sensor availability is provided.
Fig. 2 is a kind of signal of the assessment device of optional oil chromatography sensor availability according to an embodiment of the present invention Figure, as shown in Fig. 2, the device includes: acquiring unit 202, it is existing under running state of transformer for obtaining oil colours spectrum sensor The collected characteristic in field；It is corresponding to be evaluated to obtain characteristic for handling characteristic for processing unit 204 Valuation, wherein valuation to be evaluated includes invalid value ratio, consecutive identical values number, the whole coefficient of variation, segmentation coefficient of variation exception At least one of ratio and factor of created gase unnatural proportions；Judgement unit 206 is used for according to valuation to be evaluated, using corresponding criterion Obtain corresponding failure discriminant value；Computing unit 208, for being handled to obtain state value to failure discriminant value；Assessment unit 210, for state value to be compared to judge whether sensor is effective with tolerance.
The H of collected 2013 Nian Yinian of certain sensor is obtained in the present embodiment altogether2Content data (wherein January 1 in 2013 The loss of data of day to 3 days), data format is as shown in table 1, and the length in setting sliding section is 365, i.e., by January 1st, 2013 The data acquired on December 31st, 2013 are as characteristic.Characteristic should have 365, from real data it can be found that 265 are only collected, loses 100, wherein null value 2, therefore invalid data totally 100, valid data 265.
Further, judgement unit includes: the first acquisition module, for obtaining the corresponding anticipation failure of every kind of valuation to be evaluated Condition and preset weights；Generation module, it is to be assessed for when determining valuation satisfaction anticipation failure condition to be evaluated, generating this kind It is worth corresponding failure discriminant value.
Further, computing unit includes: summation module, right for according to the corresponding preset weights of every kind of valuation to be evaluated Failure discriminant value is weighted summation, obtains the state value of sensor.
Further, assessment unit includes: the second acquisition module, for obtaining default tolerance；Judgment module, being used for will State value is compared with default tolerance, determines whether sensor fails.
Further, generation module includes: the first generation submodule, if valuation to be evaluated includes invalid value ratio, for working as When determining invalid value ratio greater than the first preset threshold, the corresponding failure discriminant value P1 of invalid value ratio is generated；Second generates Submodule determines consecutive identical number of days equal to the second default threshold for working as if valuation to be evaluated includes consecutive identical values number When value, it is denoted as one-time continuous identical value, when the number for determining consecutive identical values is greater than third predetermined threshold value, generates continuous phase The corresponding failure discriminant value P2 with value；Third generates submodule, if valuation to be evaluated includes the whole coefficient of variation, determines for working as When the whole coefficient of variation is greater than four preset thresholds, the corresponding failure discriminant value P3 of the whole coefficient of variation is generated；4th generates son Module, if valuation to be evaluated includes segmentation coefficient of variation unnatural proportions, for being carried out according to the first default segments to characteristic Segmentation is denoted as exception when the coefficient of variation of each segmentation is greater than four preset thresholds.When determining abnormal segmentation variation lines Accounting of several numbers in the first default segments when being greater than five preset thresholds, generates segmentation coefficient of variation unnatural proportions Corresponding failure discriminant value P4；5th generates submodule, adjacent for utilizing if valuation to be evaluated includes factor of created gase unnatural proportions Gas content value calculates factor of created gase, when factor of created gase is greater than six preset thresholds, is denoted as factor of created gase exception, statistics factor of created gase is abnormal Ratio when being greater than seven preset thresholds, generate and produce gas when determining accounting of the number of factor of created gase exception in all numbers The corresponding failure discriminant value P5 of rate.
Further, summation module, for according to formulaThe state value F of sensor is calculated, wherein Pi For the corresponding failure discriminant value of valuation to be evaluated, Ai is the corresponding preset weights of valuation to be evaluated, i=1,2 ... 5；
Further, second module is obtained, for obtaining default tolerance R；
Further, judgment module, if being used for state valueThen determine sensor failure.
Further, when valuation to be evaluated includes inefficiency, processing unit includes: the first screening module, for screening spy The invalid data in data is levied, wherein invalid data includes missing data, negative valued data, assigning null data and outranges in data It is at least one；It is corresponding that characteristic is calculated for the accounting according to invalid data in characteristic in first computing module Invalid value ratio.
Further, when valuation to be evaluated includes consecutive identical values, processing unit includes: the second screening module, for sieving The invalid data in characteristic is selected, wherein invalid data includes missing data, negative valued data, assigning null data and outranges data At least one of；Mark module, for invalid data to be marked using preset mark value；Second computing module, is used for Consecutive identical values number is counted according to the characteristic after label, when consecutive identical number of days is equal to the second preset threshold, note For one-time continuous identical value, statistics obtains the corresponding consecutive identical values number of characteristic.
Further, when valuation to be evaluated includes the whole coefficient of variation, processing unit includes: third screening module, is used for The invalid data in characteristic is screened, wherein invalid data includes missing data, negative valued data, assigning null data and excess number of passes At least one of according to；First alternative module, for using the mode of valid data in characteristic, in alternative features data It is corresponding to obtain characteristic for calculating the whole coefficient of variation of replaced characteristic for invalid data, third computing module The whole coefficient of variation.
Further, when valuation to be evaluated includes segmentation coefficient of variation unnatural proportions, processing unit includes: the 4th screening mould Block, for screening the invalid data in characteristic, wherein invalid data include missing data, negative valued data, assigning null data and Outrange at least one of data；Second alternative module, for using the mode of valid data in characteristic, alternative features Invalid data in data；First segmentation module, for characteristic being segmented, is obtained multiple according to default segments Segmentation feature data, wherein segments is determined by the minimum stable operation cycle of sensor；4th computing module, it is every for calculating The segmentation coefficient of variation of a segmentation feature data is denoted as exception when each segmentation coefficient of variation is greater than four preset thresholds.Root According to total segments, the corresponding segmentation coefficient of variation unnatural proportions of characteristic are calculated.
Further, when valuation to be evaluated includes factor of created gase unnatural proportions, processing unit includes: the 5th screening module, is used Invalid data in screening characteristic, wherein invalid data includes missing data, negative valued data, assigning null data and outranges At least one of data, third alternative module, for using the mode of valid data in characteristic, in alternative features data Invalid data；5th computing module calculates any two and adopts for acquiring moment corresponding characteristic according to any two Collect the factor of created gase between the moment, obtain the corresponding factor of created gase of characteristic, when factor of created gase is greater than six preset thresholds, is denoted as production Gas rate is abnormal, calculates the number of factor of created gase exception, obtains the corresponding factor of created gase unnatural proportions of characteristic.
Further, acquiring unit includes: data acquisition module, is arrived for obtaining sensor in assessment moment collection in worksite Data, as sliding section end value；Module is filled, for extracting according to the corresponding predetermined time period in sliding section The historical data that the front sensor collection in worksite at assessment moment arrives, the Filling power as sliding section；Determining module, for determining The data in section are slided, data are characterized.
Further, valuation to be evaluated includes the one of which in inefficiency, consecutive identical values, the coefficient of variation and factor of created gase, Wherein judgement unit includes: the first discrimination module, if valuation to be evaluated includes invalid value ratio, is greater than the 8th when determining inefficiency When preset threshold, the corresponding failure discriminant value P1 of invalid value ratio is generated, state value is calculated according to F=P1 × A1, wherein A1 For the corresponding preset weights of valuation to be evaluated；；Second discrimination module, if valuation to be evaluated includes consecutive identical values number, when determining When consecutive identical number of days is equal to nine preset thresholds, it is denoted as one-time continuous identical value, when the number for determining consecutive identical values When greater than ten preset thresholds, the corresponding failure discriminant value P2 of consecutive identical values number is generated, is calculated according to F=P2 × A2 State value, wherein A2 is the corresponding preset weights of valuation to be evaluated；Third discrimination module, if valuation to be evaluated includes whole variation lines Number generates the corresponding failure discriminant value of the whole coefficient of variation when determining the whole coefficient of variation greater than 11 preset threshold State value is calculated according to F=P3 × A3 in P3, and wherein A3 is the corresponding preset weights of valuation to be evaluated；4th discrimination module, if Valuation to be evaluated includes segmentation coefficient of variation unnatural proportions, presets segments according to third and is segmented to characteristic, each When the coefficient of variation of segmentation is greater than four preset thresholds, it is denoted as exception.When the number for determining the abnormal segmentation coefficient of variation exists Third presets the accounting in segments, and when being greater than 92 preset threshold, it is corresponding to generate segmentation coefficient of variation unnatural proportions Fail discriminant value P4, and state value is calculated according to F=P4 × A4, and wherein A4 is the corresponding preset weights of valuation to be evaluated；5th Discrimination module calculates factor of created gase using adjacent gas content value, by factor of created gase if valuation to be evaluated includes factor of created gase unnatural proportions When greater than six preset thresholds, it is denoted as factor of created gase exception, the ratio of factor of created gase exception is counted, when the number of factor of created gase exception is in institute When thering is the accounting in number to be greater than 13 preset threshold, the corresponding failure discriminant value P5 of factor of created gase unnatural proportions is generated, according to F State value is calculated in=P5 × A5, and wherein A5 is the corresponding preset weights of valuation to be evaluated.
Further, valuation to be evaluated includes the one of which in inefficiency, consecutive identical values, the coefficient of variation and factor of created gase, Wherein assessment unit includes: the first evaluation module, if valuation to be evaluated includes invalid value ratio, for obtaining tolerance R, if state Value F >=R × A1 determines sensor failure；Second evaluation module, if valuation to be evaluated includes consecutive identical values number, for obtaining Tolerance R determines sensor failure if state value F >=R × A2；Third evaluation module, if valuation to be evaluated includes whole variation lines Number, if state value F >=R × A3, determines sensor failure for obtaining tolerance R；4th evaluation module, if valuation packet to be evaluated Segmentation coefficient of variation unnatural proportions are included, for obtaining tolerance R, if state value F >=R × A4, determine sensor failure；5th comments Estimate module, if valuation to be evaluated includes factor of created gase unnatural proportions, for obtaining tolerance R, if state value F >=R × A5, determines sensing Device failure；
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
In the above embodiment of the invention, it all emphasizes particularly on different fields to the description of each embodiment, does not have in some embodiment The part of detailed description, reference can be made to the related descriptions of other embodiments.
In several embodiments provided herein, it should be understood that disclosed technology contents can pass through others Mode is realized.Wherein, the apparatus embodiments described above are merely exemplary, such as the division of unit, can be one kind Logical function partition, there may be another division manner in actual implementation, such as multiple units or components can combine or can To be integrated into another system, or some features can be ignored or not executed.Another point, shown or discussed is mutual Coupling, direct-coupling or communication connection can be through some interfaces, the indirect coupling or communication connection of unit or module, It can be electrical or other forms.
Unit may or may not be physically separated as illustrated by the separation member, shown as a unit Component may or may not be physical unit, it can and it is in one place, or may be distributed over multiple units On.It can some or all of the units may be selected to achieve the purpose of the solution of this embodiment according to the actual needs.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list Member both can take the form of hardware realization, can also realize in the form of software functional units.
It, can if integrated unit is realized in the form of SFU software functional unit and when sold or used as an independent product To be stored in a computer readable storage medium.Based on this understanding, technical solution of the present invention substantially or Say that all or part of the part that contributes to existing technology or the technical solution can embody in the form of software products Out, which is stored in a storage medium, including some instructions are used so that a computer equipment (can be personal computer, server or network equipment etc.) executes all or part of step of each embodiment method of the present invention Suddenly.And storage medium above-mentioned includes: USB flash disk, read-only memory (ROM, Read-Only Memory), random access memory The various media that can store program code such as (RAM, Random Access Memory), mobile hard disk, magnetic or disk.
The above is only the preferred embodiment of the present invention, it is noted that those skilled in the art are come It says, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications also should be regarded as Protection scope of the present invention.

Claims (24)

1. a kind of appraisal procedure of oil chromatography sensor availability characterized by comprising
Obtain the oil colours spectrum sensor characteristic that collection in worksite arrives under running state of transformer；
The characteristic is handled, the corresponding valuation to be evaluated of the characteristic is obtained, wherein the valuation packet to be evaluated Include invalid value ratio, consecutive identical values number, the whole coefficient of variation, segmentation coefficient of variation unnatural proportions and factor of created gase unnatural proportions At least one of；
According to the valuation to be evaluated, the corresponding failure discriminant value of every kind of valuation to be evaluated is obtained using preset；
The corresponding failure discriminant value of all valuations to be evaluated is handled to obtain state value；
The state value is compared judge whether the sensor is effective with default tolerance；
Wherein, obtain oil colours spectrum sensor under running state of transformer collection in worksite to characteristic include:
Obtain the data that the sensor is arrived in assessment moment collection in worksite, the end value as sliding section；
According to the corresponding predetermined time period in the sliding section, the assessment moment foregoing description sensor collection in worksite is extracted The historical data arrived, the Filling power as the sliding section；
It determines the data in the sliding section, is the characteristic.
2. the method according to claim 1, wherein being obtained often according to the valuation to be evaluated using preset Planting the corresponding failure discriminant value of valuation to be evaluated includes:
Obtain the corresponding anticipation failure condition of described every kind valuation to be evaluated and preset threshold；
When determining the valuation to be evaluated and meeting the anticipation failure condition, generates the corresponding failure of this kind of valuation to be evaluated and differentiate Value.
3. according to the method described in claim 2, it is characterized in that, to the corresponding failure discriminant value of all valuations to be evaluated Reason obtains state value and includes:
Obtain the corresponding preset weights of described every kind valuation to be evaluated；
According to formulaThe state value F of the sensor is calculated, wherein Pi is the corresponding mistake of the valuation to be evaluated Imitate discriminant value, Ai is the corresponding preset weights of the valuation to be evaluated, i=1,2 ... 5.
4. according to the method described in claim 3, it is characterized in that, the state value is compared with default tolerance to Judge the sensor whether effectively include:
Obtain default tolerance R；
If the state valueThen determine the sensor failure.
5. according to the method described in claim 2, it is characterized in that, meeting the anticipation failure determining the valuation to be evaluated When condition, generating the corresponding failure discriminant value of this kind of valuation to be evaluated includes:
It is raw when determining the invalid value ratio greater than the first preset threshold if the valuation to be evaluated includes invalid value ratio At the corresponding failure discriminant value P1 of the invalid value ratio；
If the valuation to be evaluated includes consecutive identical values number, it is equal to the when determining the consecutive identical quantity of the characteristic When two preset thresholds, it is denoted as one-time continuous identical value, when the frequency of occurrence for determining the consecutive identical values is default greater than third When threshold value, the corresponding failure discriminant value P2 of the consecutive identical values number is generated；
If the valuation to be evaluated includes the whole coefficient of variation, when the whole coefficient of variation for determining the characteristic is greater than the 4th When preset threshold, the corresponding failure discriminant value P3 of the whole coefficient of variation is generated；
If the valuation to be evaluated include segmentation coefficient of variation unnatural proportions, according to the first default segments to the characteristic into Row segmentation, calculates the segmentation coefficient of variation of each segmentation, when the number of the abnormal segmentation coefficient of variation is at described first default point When accounting in number of segment is greater than five preset thresholds, the corresponding failure discriminant value of the segmentation coefficient of variation unnatural proportions is generated P4, wherein the abnormal segmentation coefficient of variation includes the segmentation coefficient of variation greater than the 6th preset threshold；
If the valuation to be evaluated includes factor of created gase unnatural proportions, the production gas is calculated using the gas content value at adjacent acquisition moment Rate is greater than the 7th preset threshold when determining accounting of the abnormal factor of created gase number in calculated all factor of created gase numbers When, the corresponding failure discriminant value P5 of the factor of created gase is generated, wherein the abnormal factor of created gase includes being greater than the 8th preset threshold Factor of created gase.
6. method described in any one of -5 according to claim 1, which is characterized in that when the valuation to be evaluated includes invalid value When ratio, the characteristic is handled, obtaining the corresponding valuation to be evaluated of the characteristic includes:
The invalid data in the characteristic is screened, wherein the invalid packets include missing data, negative valued data, null value number According to outrange at least one of data；
According to accounting of the invalid data in the characteristic, the corresponding invalid value ratio of the characteristic is calculated Example.
7. method described in any one of -5 according to claim 1, which is characterized in that when the valuation to be evaluated includes continuous phase When with value number, the characteristic is handled, obtaining the corresponding valuation to be evaluated of the characteristic includes:
The invalid data in the characteristic is screened, wherein the invalid packets include missing data, negative valued data, null value number According to outrange at least one of data；
The invalid data is marked using preset mark value；
Consecutive identical values number is counted according to the characteristic after label, when the consecutive identical quantity of the characteristic is equal to the When two preset thresholds, it is denoted as one-time continuous identical value, the frequency of occurrence for counting the consecutive identical values obtains the characteristic Corresponding consecutive identical values number.
8. method described in any one of -5 according to claim 1, which is characterized in that when the valuation to be evaluated includes whole becomes When different coefficient, the characteristic is handled, obtaining the corresponding valuation to be evaluated of the characteristic includes:
The invalid data in the characteristic is screened, wherein the invalid packets include missing data, negative valued data, null value number According to outrange at least one of data；
Using the mode of valid data in the characteristic, the invalid data in the characteristic is substituted；
The whole coefficient of variation for calculating replaced characteristic obtains the corresponding whole coefficient of variation of the characteristic.
9. method described in any one of -5 according to claim 1, which is characterized in that when the valuation to be evaluated includes that segmentation becomes When different coefficient unnatural proportions, the characteristic is handled, obtaining the corresponding valuation to be evaluated of the characteristic includes:
The invalid data in the characteristic is screened, wherein the invalid packets include missing data, negative valued data, null value number According to outrange at least one of data；
Using the mode of valid data in the characteristic, the invalid data in the characteristic is substituted；
According to default segments, the characteristic is segmented, obtains multiple segmentation feature data, wherein described default point Number of segment is determined by the minimum stable operation cycle of the sensor；
The segmentation coefficient of variation for calculating each segmentation feature data, according to the number of the abnormal segmentation coefficient of variation described default Accounting in segments obtains the corresponding segmentation coefficient of variation unnatural proportions of the characteristic, wherein abnormal segmentation variation lines Number includes the segmentation coefficient of variation greater than the 6th preset threshold.
10. method described in any one of -5 according to claim 1, which is characterized in that when the valuation to be evaluated includes described When factor of created gase unnatural proportions, the characteristic is handled, obtaining the corresponding valuation to be evaluated of the characteristic includes:
The invalid data in the characteristic is screened, wherein the invalid packets include missing data, negative valued data, null value number According to outrange at least one of data；
Using the mode of valid data in the characteristic, the invalid data in the characteristic is substituted；
The gas content value for calculating the every two adjacent acquisition moment calculates the factor of created gase, is being counted according to abnormal factor of created gase number The accounting in all factor of created gase numbers calculated obtains the corresponding factor of created gase unnatural proportions of the characteristic, wherein abnormal production Gas rate includes the factor of created gase greater than the 8th preset threshold.
11. the method according to claim 1, wherein the valuation to be evaluated include invalid value ratio, it is consecutive identical The one of which being worth in number, the whole coefficient of variation, segmentation coefficient of variation unnatural proportions and factor of created gase unnatural proportions, wherein right Failure discriminant value, which is handled to obtain state value, includes:
It is raw when determining the invalid value ratio greater than nine preset thresholds if the valuation to be evaluated includes invalid value ratio At the corresponding failure discriminant value P1 of invalid value ratio, the state value is calculated according to F=P1 × A1, wherein A1 be it is described to The corresponding preset weights of assessed value；
If the valuation to be evaluated includes consecutive identical values number, it is equal to the when determining the consecutive identical quantity of the characteristic When ten preset thresholds, it is denoted as one-time continuous identical value, when the frequency of occurrence for determining the consecutive identical values is greater than the 11st in advance If when threshold value, generating the corresponding failure discriminant value P2 of consecutive identical values number, the state being calculated according to F=P2 × A2 Value, wherein A2 is the corresponding preset weights of the valuation to be evaluated；
If the valuation to be evaluated includes the whole coefficient of variation, when determining the whole coefficient of variation greater than 12 preset threshold, The corresponding failure discriminant value P3 of the whole coefficient of variation is generated, the state value is calculated according to F=P3 × A3, wherein A3 is institute State the corresponding preset weights of valuation to be evaluated；
If the valuation to be evaluated include segmentation coefficient of variation unnatural proportions, according to third preset segments to the characteristic into Row segmentation, calculates the coefficient of variation of each segmentation, when the number of the abnormal segmentation coefficient of variation presets segments in the third In accounting when being greater than 13 preset threshold, generate the corresponding failure discriminant value P4 of the segmentation coefficient of variation unnatural proportions, The state value is calculated according to F=P4 × A4, wherein A4 is the corresponding preset weights of the valuation to be evaluated, described abnormal The segmentation coefficient of variation includes the segmentation coefficient of variation greater than the 14th preset threshold；
If the valuation to be evaluated includes factor of created gase unnatural proportions, the production gas is calculated using the gas content value at adjacent acquisition moment Rate is greater than the 15th preset threshold when determining accounting of the abnormal factor of created gase number in calculated all factor of created gase numbers When, the corresponding failure discriminant value P5 of the factor of created gase is generated, the state value is calculated according to F=P5 × A5, wherein A5 is The corresponding preset weights of the valuation to be evaluated, the abnormal factor of created gase include the factor of created gase greater than the 16th preset threshold.
12. according to the method for claim 11, which is characterized in that the valuation to be evaluated includes invalid value ratio, continuous phase One of which in same value number, the whole coefficient of variation, segmentation coefficient of variation unnatural proportions and the factor of created gase unnatural proportions, Be characterized in that, state value is compared with default tolerance judge sensor whether effectively include:
If the valuation to be evaluated includes invalid value ratio, tolerance R is obtained, if the state value F >=R × A1, determines the biography Sensor failure；
If the valuation to be evaluated includes consecutive identical values number, tolerance R is obtained, if the state value F >=R × A2, determines institute State sensor failure；
If the valuation to be evaluated includes the whole coefficient of variation, tolerance R is obtained, if the state value F >=R × A3, described in judgement Sensor failure；
If the valuation to be evaluated includes segmentation coefficient of variation unnatural proportions, tolerance R is obtained, if state value F >=R × A4, is determined The sensor failure；
If the valuation to be evaluated includes the factor of created gase unnatural proportions, tolerance R is obtained, if the state value F >=R × A5, sentences The fixed sensor failure.
13. a kind of assessment device of oil chromatography sensor availability characterized by comprising
Acquiring unit, for obtaining the oil colours spectrum sensor characteristic that collection in worksite arrives under running state of transformer；
Processing unit obtains the corresponding valuation to be evaluated of the characteristic for handling the characteristic, wherein The valuation to be evaluated include invalid value ratio, consecutive identical values number, the whole coefficient of variation, segmentation coefficient of variation unnatural proportions and At least one of factor of created gase unnatural proportions；
Judgement unit obtains the corresponding failure differentiation of every kind of valuation to be evaluated using preset for according to the valuation to be evaluated Value；
Computing unit, for being handled to obtain state value to the corresponding failure discriminant value of all valuations to be evaluated；
The state value is compared judge whether the sensor is effective with default tolerance by assessment unit；
Wherein, the acquiring unit includes:
Data acquisition module, the data arrived for obtaining the sensor in assessment moment collection in worksite, as sliding section End value；
Module is filled, for extracting the assessment moment foregoing description according to the corresponding predetermined time period in the sliding section The historical data that sensor collection in worksite arrives, the Filling power as the sliding section；
Determining module is the characteristic for determining the data in the sliding section.
14. device according to claim 13, which is characterized in that according to the valuation to be evaluated, obtained using preset The corresponding failure discriminant value of every kind of valuation to be evaluated includes:
First obtains module, for obtaining the corresponding anticipation failure condition of described every kind valuation to be evaluated and preset weights；
Generation module, for generating this kind of valuation to be evaluated when determining the valuation to be evaluated and meeting the anticipation failure condition Corresponding failure discriminant value.
15. device according to claim 14, which is characterized in that handle the corresponding discriminant value of all valuations to be evaluated State value is obtained, wherein computing unit includes:
Module is obtained, for obtaining the corresponding preset weights of described every kind valuation to be evaluated；
Summation module, for according to formulaThe state value F of the sensor is calculated, wherein Pi is described to be evaluated The corresponding failure discriminant value of valuation, Ai are the corresponding preset weights of the valuation to be evaluated, i=1,2 ... 5.
16. device according to claim 15, which is characterized in that the assessment unit includes:
Second obtains module, for obtaining default tolerance R；
Judgment module, if being used for the state valueThen determine the sensor failure.
17. device according to claim 14, which is characterized in that the generation module includes:
First generates submodule, if the valuation to be evaluated includes invalid value ratio, for big when determining the invalid value ratio When the first preset threshold, the corresponding failure discriminant value P1 of the invalid value ratio is generated；
Second generates submodule, if the valuation to be evaluated includes consecutive identical values number, determines the characteristic for working as When consecutive identical quantity is equal to the second preset threshold, it is denoted as one-time continuous identical value, when determining the consecutive identical values When frequency of occurrence is greater than third predetermined threshold value, the corresponding failure discriminant value P2 of the consecutive identical values number is generated；
Third generates submodule, if the valuation to be evaluated includes the whole coefficient of variation, determines the characteristic for working as When the whole coefficient of variation is greater than four preset thresholds, the corresponding failure discriminant value P3 of the whole coefficient of variation is generated；
4th generates submodule, if the valuation to be evaluated includes segmentation coefficient of variation unnatural proportions, for according to first default point Number of segment is segmented the characteristic, calculates the coefficient of variation of each segmentation, when the number of the abnormal segmentation coefficient of variation When accounting in the described first default segments is greater than five preset thresholds, the segmentation coefficient of variation unnatural proportions pair are generated The failure discriminant value P4 answered, wherein the abnormal segmentation coefficient of variation includes the segmentation variation lines greater than the 6th preset threshold Number；
5th generates submodule, if the valuation to be evaluated includes the factor of created gase unnatural proportions, for utilizing the adjacent acquisition moment Gas content value calculate the factor of created gase, when determining abnormal factor of created gase number in calculated all factor of created gase numbers Accounting be greater than seven preset thresholds when, the corresponding failure discriminant value P5 of the factor of created gase is generated, wherein the abnormal production gas Rate includes the factor of created gase greater than the 8th preset threshold.
18. device described in any one of 3-17 according to claim 1, which is characterized in that when the valuation to be evaluated includes nothing When valid value ratio, the processing unit includes:
First screening module, for screening the invalid data in the characteristic, wherein the invalid packets include missing number According to, negative valued data, assigning null data and at least one of outrange data；
The feature is calculated for the accounting according to the invalid data in the characteristic in first computing module The corresponding invalid value ratio of data.
19. device described in any one of 3-17 according to claim 1, which is characterized in that when the valuation to be evaluated includes connecting When continuous identical value number, the processing unit includes:
Second screening module, for screening the invalid data in the characteristic, wherein the invalid packets include missing number According to, negative valued data, assigning null data and at least one of outrange data；
Mark module, for the invalid data to be marked using preset mark value；
Second computing module, for counting consecutive identical values number according to the characteristic after label, when the characteristic connects When continuing identical quantity equal to the second preset threshold, it is denoted as one-time continuous identical value, count the consecutive identical values goes out occurrence Number obtains the corresponding consecutive identical values number of the characteristic.
20. device described in any one of 3-17 according to claim 1, which is characterized in that when the valuation to be evaluated includes whole When the body coefficient of variation, the processing unit includes:
Third screening module, for screening the invalid data in the characteristic, wherein the invalid packets include missing number According to, negative valued data, assigning null data and at least one of outrange data；
First alternative module substitutes the institute in the characteristic for using the mode of valid data in the characteristic Invalid data is stated,
It is corresponding to obtain the characteristic for calculating the whole coefficient of variation of replaced characteristic for third computing module The whole coefficient of variation.
21. device described in any one of 3-17 according to claim 1, which is characterized in that when the valuation to be evaluated includes point When section coefficient of variation unnatural proportions, the processing unit includes:
4th screening module, for screening the invalid data in the characteristic, wherein the invalid packets include missing number According to, negative valued data, assigning null data and at least one of outrange data；
Second alternative module substitutes the institute in the characteristic for using the mode of valid data in the characteristic State invalid data；
First segmentation module, for the characteristic being segmented, multiple segmentation feature numbers are obtained according to default segments According to wherein the default segments is determined by the minimum stable operation cycle of the sensor；
4th computing module, for calculating the segmentation coefficient of variation of each segmentation feature data, according to abnormal segmentation variation lines Accounting of several numbers in the default segments obtains the corresponding segmentation coefficient of variation unnatural proportions of the characteristic, The middle abnormal segmentation coefficient of variation includes the segmentation coefficient of variation greater than the 6th preset threshold.
22. device described in any one of 3-17 according to claim 1, which is characterized in that when the valuation to be evaluated includes institute When stating factor of created gase unnatural proportions, the processing unit includes:
5th screening module, for screening the invalid data in the characteristic, wherein the invalid packets include missing number According to, negative valued data, assigning null data and at least one of outrange data,
Third alternative module substitutes the institute in the characteristic for using the mode of valid data in the characteristic State invalid data；
5th computing module, the gas content value for calculating the every two adjacent acquisition moment calculates the factor of created gase, according to different It is different that accounting of the normal factor of created gase number in calculated all factor of created gase numbers obtains the corresponding factor of created gase of the characteristic Normal ratio, wherein abnormal factor of created gase includes the factor of created gase greater than the 8th preset threshold.
23. device according to claim 13, which is characterized in that the valuation to be evaluated includes invalid value ratio, continuous phase One of which in same value number, the whole coefficient of variation, segmentation coefficient of variation unnatural proportions and the factor of created gase unnatural proportions, Described in judgement unit include:
First discrimination module is greater than the 9th when determining the invalid value ratio if the valuation to be evaluated includes invalid value ratio When preset threshold, the corresponding failure discriminant value P1 of invalid value ratio is generated, the state value is calculated according to F=P1 × A1, Wherein A1 is the corresponding preset weights of the valuation to be evaluated；
Second discrimination module, if the valuation to be evaluated includes consecutive identical values number, when determining the characteristic continuous phase When same quantity is equal to ten preset thresholds, it is denoted as one-time continuous identical value, when the occurrence out for determining the consecutive identical values When number is greater than 11 preset threshold, the corresponding failure discriminant value P2 of consecutive identical values number is generated, is calculated according to F=P2 × A2 The state value is obtained, wherein A2 is the corresponding preset weights of the valuation to be evaluated；
Third discrimination module is greater than the tenth when determining the whole coefficient of variation if the valuation to be evaluated includes the whole coefficient of variation When two preset thresholds, the corresponding failure discriminant value P3 of the whole coefficient of variation is generated, the state is calculated according to F=P3 × A3 Value, wherein A3 is the corresponding preset weights of the valuation to be evaluated；
4th discrimination module presets segments pair according to third if the valuation to be evaluated includes segmentation coefficient of variation unnatural proportions The characteristic is segmented, and the coefficient of variation of each segmentation is calculated, when the number of the abnormal segmentation coefficient of variation is described When third presets the accounting in segments greater than 13 preset threshold, it is corresponding to generate the segmentation coefficient of variation unnatural proportions Fail discriminant value P4, and the state value is calculated according to F=P4 × A4, and wherein A4 is the corresponding default power of the valuation to be evaluated Value, the abnormal segmentation coefficient of variation include the segmentation coefficient of variation greater than the 14th preset threshold；
5th discrimination module utilizes the gas at adjacent acquisition moment if the valuation to be evaluated includes the factor of created gase unnatural proportions Content value calculates the factor of created gase, when determining accounting of the abnormal factor of created gase number in calculated all factor of created gase numbers When greater than 15 preset threshold, the corresponding failure discriminant value P5 of the factor of created gase is generated, institute is calculated according to F=P5 × A5 State value is stated, wherein A5 is the corresponding preset weights of the valuation to be evaluated, and the abnormal factor of created gase includes being greater than the 16th in advance If the factor of created gase of threshold value.
24. device according to claim 23, which is characterized in that the valuation to be evaluated includes invalid value ratio, continuous phase One of which in same value number, the whole coefficient of variation, segmentation coefficient of variation unnatural proportions and the factor of created gase unnatural proportions, Described in assessment unit include:
First evaluation module, if the valuation to be evaluated includes invalid value ratio, for obtaining tolerance R, if the state value F >= R × A1 determines the sensor failure；
Second evaluation module, if the valuation to be evaluated includes consecutive identical values number, for obtaining tolerance R, if the state Value F >=R × A2 determines the sensor failure；
Third evaluation module, if the valuation to be evaluated includes the whole coefficient of variation, for obtaining tolerance R, if the state value F >=R × A3 determines the sensor failure；
4th evaluation module, if the valuation to be evaluated includes segmentation coefficient of variation unnatural proportions, for obtaining tolerance R, if institute State value F >=R × A4 is stated, determines the sensor failure；
5th evaluation module, if the valuation to be evaluated includes the factor of created gase unnatural proportions, for obtaining tolerance R, if described State value F >=R × A5 determines the sensor failure.
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