CN109186813A - A kind of temperature sensor self-checking unit and method - Google Patents
A kind of temperature sensor self-checking unit and method Download PDFInfo
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Abstract
The present invention provides a kind of temperature sensor self-checking unit and method, difference processing is carried out to the temperature data sequence of train under normal circumstances and obtains segmentation criteria difference sequence, and by obtaining abnormality detection threshold value to standard difference sequence is for statistical analysis；Difference processing is carried out to real-time input temp data sequence and obtains segmentation criteria difference sequence；Judge wise temperature sequence of differences with the presence or absence of abnormal based on threshold value and standard difference sequence；If certain section of segmentation criteria difference sequence is greater than or equal to threshold value, it is abnormal to judge that this section of temperature gap sequence exists, and enter in next step, otherwise sensor is normal；There is the distribution consistency of abnormal certain section of temperature gap sequence and normal baseline sequence and previous adjacent time interval temperature gap sequence in judgement；If there is consistency, then judge that sensor is normal, if it is not, then sensor abnormality.The present invention can solve the technical issues of prior art can not carry out quickly and effectively self-test, cannot ensure train safe and efficient operation to temperature sensor.
Description
Technical field
The present invention relates to fault diagnosis technology fields, fill more particularly, to a kind of self-test applied to train temperature sensor
It sets and method.
Background technique
Temperature sensor is one of important components of each system such as train transmission, control, EEF bogie, be responsible for full vehicle with
The monitoring and perceptional function of the related critical component of temperature are the core apparatus for ensureing equipment safety, normal operation, are entirely to arrange
One of key index of monitoring of vehicle.In general, in most cases those skilled in the art are concerned with sensor and are monitored
The operating condition of object, and less consider the problems of human observer and temperature sensor (system) inner link itself.In fact, same
Sample certainly exists the possibility of failure as a device, temperature sensor.When the temperature value measured by the sensor occurs abnormal,
Can not usually affirm completely the exception be surveyed object really have occurred problem or sensor or communication system occur it is different
Often.It is abnormal if it is true measurement object, then it needs to take the safe counter-measure of urgent train, such as drops power or parking maintenance
Deng.However, taking urgent counter-measure that can then increase significantly O&M cost, even if it is because sensor (system) is abnormal
In being also possible to lead to the generation of safety problem in some cases.Therefore, it is necessarily required to solve the problems, such as one in practical application,
That is how to ensure the normality that temperature sensor itself works, or is said differently, i.e., how to determine the exception of appearance
Temperature value is whether that the exception of sensor itself is caused.
Based on problem above, which kind of mode the current desired key problem in technology to be solved, which is that using, finds temperature sensing
Whether device itself is normal, wherein includes: that information relevant to temperature sensor and data how to be utilized effectively to identify anomaly source
Head realizes effective early warning, and how by additionally installing sensor additional or realizing anomalous discrimination by existing data.
Currently, it is many about the research and application for checking sensor operating status itself, wherein different for temperature sensor
Detection means whether often also has very much, has through detection sensor voltage, current conditions and judges working sensor state,
Have in conjunction with professional knowledge set temperature threshold value and judge working sensor state, also has and sensed by being installed additional in analogous location
Device compares the variation of two or more temperature values to judge working sensor state.But these existing temperature sensors
Detection method there is or judging result inaccuracy, there is situations such as erroneous judgement, fail to judge, or need professional using specially
Industry knowledge is judged that application and field are restricted, or needs to install additional additional device, and system complexity is caused to increase
The various technological deficiencies such as add.
Summary of the invention
In view of this, the purpose of the present invention is to provide a kind of temperature sensor self-checking unit and method, it is existing to solve
Train system can not carry out quickly and effectively self-test to temperature sensor, and then cannot ensure the skill of the safe and efficient operation of train
Art problem.
To achieve the goals above, the present invention specifically provides a kind of technology realization side of temperature sensor self-checking unit
Case, temperature sensor self-checking unit, comprising:
Abnormality detection threshold calculation module, for the train temperature data sequence that sensor is surveyed under normal operating conditions
Column T carries out difference processing and obtains the segmentation criteria difference sequence Θ of temperature gap sequence δ T, and by carrying out to standard difference sequence Θ
Statistical analysis obtains abnormality detection threshold k；
Critical eigenvalue extraction module, for being carried out at difference to the sensor measured temperature data sequence t inputted in real time
Reason obtains the segmentation criteria difference sequence η of temperature gap sequence δ t；
First abnormality detection module, the abnormality detection threshold k for being exported according to the abnormality detection threshold calculation module,
And the standard difference sequence η of the critical eigenvalue extraction module output judges wise temperature sequence of differences δ t with the presence or absence of abnormal；
If the segmentation criteria difference sequence η of certain section of temperature gap sequence δ t is greater than or equal to abnormality detection threshold k, this section of temperature is judged
There is exception in sequence of differences δ t, and export this section and there is abnormal temperature gap sequence δ t, otherwise judge that sensor is normal；
There is abnormal temperature gap sequence δ t for exporting to the first abnormality detection module in consistency check module
Distribution consistency check is carried out with normal baseline sequence and previous time adjacent segments temperature gap sequence δ t；
Second abnormality detection module, for judging that it is general that the distribution consistency check of the consistency check module output occurs
Whether rate P value is less than established standards, and if it is less than established standards, then output transducer abnormity early warning signal, otherwise sensor is being just
Often.
Further, the abnormality detection threshold calculation module obtains passes in the operational process of normal condition Train a part
The temperature data sequence T that sensor is surveyed calculates temperature gap sequence δ T by unit time Δ T.To the temperature difference in the unit time
Value sequence δ T is segmented by identical duration T1, calculates the standard deviation θ of every section of temperature gap sequence δ Ti, and form standard difference sequence Θ.
The distribution situation of analytical standard difference sequence Θ, and the mean μ and standard deviation sigma of standard difference sequence Θ are calculated, according to probability of happening The corresponding abnormality detection threshold k in the principle construction train position.
Wherein, θ is the standard deviation of temperature gap sequence δ T.
Wherein, ωiFor weighting coefficient, hereinxiFor sample value, n is sample number.
Further, the critical eigenvalue extraction module obtains the temperature data sequence t that real-time input pickup is surveyed,
Temperature gap sequence δ t is calculated by unit time Δ t, the temperature gap sequence δ t in the unit time is segmented by identical duration T2,
The standard deviation of every section of temperature gap sequence δ t is calculated, and forms standard difference sequence η.
Further, the consistency check module finds train by comparing abnormality detection threshold k and standard difference sequence η
After doubtful abnormal data occurs in certain section of temperature gap sequence δ t in operational process, record this section of abnormal temperature sequence of differences δ t's
Information, and obtain this section of temperature gap sequence xtAnd its temperature gap sequences y of previous time adjacent segmentst, while when obtaining identical
Between the temperature gap sequence z1 that is surveyed of the other analogous location sensors of section traint,…,znt, and just doubtful abnormal temperature gap
Sequence xtRespectively with the temperature gap sequences y of previous time adjacent segmentstAnd the temperature gap that other analogous location sensors are surveyed
Sequence { z1t,…,zntSeriatim carry out K-S distribution inspection.
Further, the consistency check module is judging temperature gap sequence x to be testedtWith previous time adjacent segments
Temperature gap sequences ytAnd the temperature gap sequence { z1 that other analogous location sensors are surveyedt,…,zntDistribution it is consistent
When property, by the maximum disparity value D of empirical distribution function between checking sequence come temperature sequence of differences xtConspicuousness.Work as reality
It is small greater than distribution probability P value corresponding to a certain established standards value or maximum disparity value D that border calculates resulting maximum disparity value D
When a certain established standards value, then do not have consistency between two temperature gap sequences.
Wherein, temperature gap sequence xtSample size be n1, temperature gap sequences yt,z1t,…,zntIn any difference sequence
The sample size of column is n2, F1(x) and F2(x) distribution function that accumulates experience of two samples is respectively indicated, j is temperature gap sequence
Segment identification, x are sample.
Remember Dj=F1(xj)-F2(xj), Represent DjThe maximum value of absolute distance.Test statistics
Z is similar to normal distribution, expression formula are as follows:
When null hypothesis is true, Z converges on K according to Density Distribution d and is distributed, i.e., when sample is derived from one-dimensional continuously distributed F,
For the maximum value for taking B (F (x)) absolute distance, x is sample.
Empirical distribution function B (t) are as follows:
Wherein, x is independent variable, and i is natural number.
In addition the present invention also specifically provides a kind of technic relization scheme of temperature sensor self checking method, temperature sensor
Self checking method, comprising the following steps:
S10 difference processing) is carried out to the train temperature data sequence T that sensor is surveyed under normal operating conditions and obtains temperature
The segmentation criteria difference sequence Θ of sequence of differences δ T is spent, and by obtaining abnormality detection threshold to standard difference sequence Θ is for statistical analysis
Value K；
S20) to the sensor measured temperature data sequence t progress inputted in real time, identical difference processing is obtained with step S10)
To the segmentation criteria difference sequence η of temperature gap sequence δ t；
S30) it is based on step S10) obtained abnormality detection threshold k and step S20) obtained standard difference sequence η judgement point
Section temperature gap sequence δ t is with the presence or absence of abnormal；If the segmentation criteria difference sequence η of certain section of temperature gap sequence δ t is greater than or waits
In abnormality detection threshold k, then it is abnormal to judge that this section of temperature gap sequence δ t exists, and enter step S40), otherwise judgement senses
Device is normal；
S40) judgment step S30) it is middle in the presence of abnormal certain section of temperature gap sequence δ t and normal baseline sequence and previous phase
The distribution consistency of adjacent period temperature gap sequence δ t；If there is consistency, then judge that sensor is normal, if there is no
Consistency then judges sensor abnormality.
Further, the step S10) further comprise:
S11 the temperature data sequence T that sensor is surveyed in the operational process of normal condition Train a part) is chosen, by single
Position time Δ T calculates temperature gap sequence δ T；
S12 the temperature gap sequence δ T in the unit time is segmented by identical duration T1), calculates every section of temperature gap sequence
The standard deviation θ of δ Ti, and form standard difference sequence Θ；
S13) the distribution situation of analytical standard difference sequence Θ, and the mean μ and standard deviation sigma of standard difference sequence Θ are calculated, it presses
According to probability of happeningThe corresponding abnormality detection threshold k in the principle construction train position；
Wherein, θ is the standard deviation of temperature gap sequence δ T.
Wherein, ωiFor weighting coefficient, hereinxiFor sample value, n is sample number.
Further, the step S20) further comprise:
S21) the temperature data sequence t that input pickup is surveyed in real time；
S22) temperature gap sequence δ t is calculated by unit time Δ t；
S23 the temperature gap sequence δ t in the unit time is segmented by identical duration T2), calculates every section of temperature gap sequence
The standard deviation of δ t, and form standard difference sequence η.
Further, the step S40) further comprise:
S41) by comparing certain section of temperature gap in abnormality detection threshold k and standard difference sequence η discovery train travelling process
After doubtful abnormal data occurs in sequence δ t, the information of this section of abnormal temperature sequence of differences δ t is recorded, and obtains this section of temperature difference
Value sequence xtAnd its temperature gap sequences y of previous time adjacent segmentst；
S42 the temperature gap sequence z1 that the other analogous location sensors of same time period train are surveyed) is obtainedt,…,znt；
S43) with regard to doubtful abnormal temperature gap sequence xtRespectively with the temperature gap sequences y of previous time adjacent segmentst, and
Temperature gap sequence { the z1 that other analogous location sensors are surveyedt,…,zntSeriatim carry out K-S distribution inspection；
S44) when the probability of happening P value of all inspections is respectively less than established standards, then output transducer abnormity early warning signal, no
Then sensor is normal.
Further, the step S103) further comprise:
If temperature gap sequence xtSample size be n1, temperature gap sequences yt,z1t,…,zntIn any sequence of differences
Sample size is n2, F1(x) and F2(x) distribution function that accumulates experience of two samples is respectively indicated, j is temperature gap sequence segment
Mark, x is sample.
Remember Dj=F1(xj)-F2(xj), Represent DjThe maximum value of absolute distance.Test statistics
Z is similar to normal distribution, expression formula are as follows:
When null hypothesis is true, Z converges on K according to Density Distribution d and is distributed, i.e., when sample is derived from one-dimensional continuously distributed F,
For the maximum value for taking B (F (x)) absolute distance, x is sample.
Empirical distribution function B (t) are as follows:
Wherein, x is independent variable, and i is natural number；
Judging temperature gap sequence x to be testedtWith the temperature gap sequences y of previous time adjacent segmentstAnd it is other similar
Temperature gap sequence { the z1 that position sensor is surveyedt,…,zntDistribution consistency when, be distributed by experience between checking sequence
The maximum disparity value D of function carrys out temperature sequence of differences xtConspicuousness.It is greater than when actually calculating resulting maximum disparity value D
When distribution probability P value corresponding to a certain established standards value or maximum disparity value D is less than a certain established standards value, then two temperature
Do not have consistency between degree sequence of differences.
By implementing the technical solution of the temperature sensor self-checking unit that aforementioned present invention provides and method, have has as follows
Beneficial effect:
(1) relatively existing the present invention is based on the changing value progress self-test and early warning of measured temperature value of sensor (system) itself
Have for the technical solution based on other variables such as electric current, voltage or the more device measurement results of comparison in technology, can it is more effective,
More directly have found that it is likely that existing exception, monitoring and early warning result will be more true, accurate；
(2) present invention not merely with threshold value index carry out self-test early warning, and from contrast distribution change angle carry out into
One step detects and notes abnormalities, and only is analyzed to sensor fault using only one to two indication index compared with the prior art
It says, rule, the result of self-test and early warning are more accurate and effective；
(3) the present invention is based on data normal in a large amount of actual moving process and abnormal to carry out analysis and application, relatively existing
Have in technology based on data volume it is less the problems such as, model result it is relatively reliable, the factor of consideration more sufficiently, close
Reason, testability and practicability are also stronger；
(4) the present invention is based on a large amount of temperature datas measured in train travelling process to carry out real-time self-test and early warning, and
Data adjust automatically threshold value and distribution inspection segmented mode based on continuous renewal have significant high efficiency and intelligent water
It is flat.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described.It should be evident that the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
Other embodiments are obtained according to these attached drawings.
Fig. 1 is a kind of system structure diagram of specific embodiment of temperature sensor self-checking unit of the present invention；
Fig. 2 is a kind of workflow schematic illustration of specific embodiment of temperature sensor self checking method of the present invention；
Fig. 3 is a kind of program flow diagram of specific embodiment of temperature sensor self checking method of the present invention；
Fig. 4 is the schematic diagram that K-S is examined in a kind of specific embodiment of temperature sensor self checking method of the present invention；
In figure: 1- abnormality detection threshold calculation module, 2- critical eigenvalue extraction module, 3- the first abnormality detection module,
4- consistency check module, 5- the second abnormality detection module.
Specific embodiment
For the sake of quoting and understanding, will hereafter used in technical term, write a Chinese character in simplified form or abridge and be described below:
Non-parametric test: refer in the case where population variance is unknown or knows very few, using sample data to overall distribution
The method that form etc. is inferred.Since non-parametric test method is not related to the parameter in relation to overall distribution during deduction,
Thus it is referred to as " nonparametric " to examine.
K-S is examined: Kolmogorov-Smirnove test is based on Cumulative Distribution Function, to examine two experiences
Whether distribution is different or whether an experience distribution is different from another ideal distribution.The other methods that it and t are examined etc are not
It is both that K-S is examined and required no knowledge about the distribution situations of data, it can a kind of non-parametric test method at last.It is smaller in sample size
When, K-S, which is examined, to be analyzed between two groups of data as a kind of non-parametric test with the presence or absence of not being a kind of common simultaneously
Method.
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, the technical scheme in the embodiment of the invention is clearly and completely described.Obviously, described embodiment is only
It is only a part of the embodiments of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, the common skill in this field
Art personnel all other embodiment obtained without creative efforts belongs to the model that the present invention protects
It encloses.
As shown in attached drawing 1 to attached drawing 4, the specific embodiment of temperature sensor self-checking unit of the present invention and method is given,
The present invention is further illustrated in the following with reference to the drawings and specific embodiments.
Embodiment 1
By the analysis of mass data and the study found that the temperature change of each region of interest is usually one opposite on train
Slow process.That is, a possibility that fluctuation occur smaller for the variation of temperature in a short time, especially extremely short
Occurring the case where temperature declines to a great extent in time is almost difficult to occur.Thus, based on the considerations of in this respect, if it find that in certain section
There is rapid fluctuation in temperature change value in the shorter time, and in close temporal proximity changing value distribution there are when significant difference, then
Illustrate that corresponding temperature sensor (system) is likely to exception occurred.Therefore, the specific embodiment of the invention passes through the unit time
The standard deviation of interior temperature difference value carrys out metric fluctuation level, and divides to realize the close temporal proximity temperature difference in conjunction with K-S distribution inspection method
The inspection of cloth compares, and carrys out comprehensive descision temperature sensor with the presence or absence of abnormal with this.The temperature sensor of the present embodiment description is certainly
Checking device includes two large divisions's function altogether, and first part is the determination (modeling) of temperature difference abnormality detection threshold value, and second part is knot
It closes threshold value and K-S examines the self-test realized to abnormality of temperature sensors.
In terms of temperature difference abnormality detection threshold value determination, first by train system each temperature classes time sequence under normal circumstances
Column data carries out difference processing, and then with treated, temperature difference data calculate fluctuation (standard deviation) by principle segmentation, forms phase
The standard difference sequence answered, then corresponding distribution situation is estimated on the basis of the standard difference sequence of formation, calculate the equal of volatility series
Value and standard deviation finally combine statistical distribution principle to obtain+3 σ of abnormality detection threshold value μ (i.e. abnormality detection threshold k).
In terms of combining abnormality detection threshold value and K-S inspection to realize to abnormality of temperature sensors self-test, for inputting in real time
Temperature detection data, determine that stage same rule calculates difference and corresponding wave first, in accordance with temperature difference abnormality detection threshold value
Dynamic sequence, then the abnormal possibility based on the segmentation volatility series of abnormality detection threshold decision obtained by front, such as larger than or are equal to different
Normal detection threshold value then judges the doubtful exception of this section of temperature value, and then further deeply judges this section of temperature difference sequence and basic temperature difference sequence
, such as there is notable difference, then illustrate that there are exceptions for sensor (system) in the distributional difference of column and sequence early period；Conversely, then recognizing
It is temporarily without exception.
By the specific implementation of the temperature sensor self-checking unit based on above-mentioned working principle, details are as follows below.
As shown in Fig. 1, a kind of embodiment of temperature sensor self-checking unit, specifically includes:
Abnormality detection threshold calculation module 1, for the train temperature data that sensor is surveyed under normal operating conditions
Sequence T (such as: can be oil temperature, axis temperature, water temperature data) carries out difference processing and obtains the segmentation criteria of temperature gap sequence δ T
Difference sequence Θ, and by obtaining abnormality detection threshold k to standard difference sequence Θ is for statistical analysis；
Critical eigenvalue extraction module 2, for being carried out at difference to the sensor measured temperature data sequence t inputted in real time
Reason obtains the segmentation criteria difference sequence η of temperature gap sequence δ t；
First abnormality detection module 3, the abnormality detection threshold k for being exported according to abnormality detection threshold calculation module 1, and
The standard difference sequence η that critical eigenvalue extraction module 2 exports judges wise temperature sequence of differences δ t with the presence or absence of abnormal；If certain
The segmentation criteria difference sequence η of section temperature gap sequence δ t is greater than or equal to abnormality detection threshold k, then judges this section of temperature gap sequence
It arranges δ t and there is exception, and export this section and there is abnormal temperature gap sequence δ t, otherwise judge that sensor is normal；
Consistency check module 4, for the first abnormality detection module 3 is exported exist abnormal temperature gap sequence δ t with
Normal baseline sequence and previous time adjacent segments temperature gap sequence δ t carry out distribution consistency check；
Second abnormality detection module 5, the distribution consistency check probability of happening exported for judging consistency check module 4
Whether P value is less than established standards, and if it is less than established standards, then output transducer abnormity early warning signal, otherwise sensor is normal.
Abnormality detection threshold calculation module 1 obtains the temperature that sensor is surveyed in the operational process of normal condition Train a part
Data sequence T is spent, calculates temperature gap sequence δ T by unit time Δ T.Phase is pressed to the temperature gap sequence δ T in the unit time
It is segmented with duration T1, calculates the standard deviation θ of every section of temperature gap sequence δ Ti, and form standard difference sequence Θ.Analytical standard difference sequence
The distribution situation of Θ is arranged, and calculates the mean μ and standard deviation sigma of standard difference sequence Θ, according to probability of happening (i.e. distribution consistency inspection
Test probability of happening)The corresponding abnormality detection threshold k in the principle construction train position.
Wherein, θ is the standard deviation of temperature gap sequence δ T.
Wherein, ωiFor weighting coefficient, hereinxiFor sample value, n is sample number.
Critical eigenvalue extraction module 2 obtains the temperature data sequence t that real-time input pickup is surveyed, by unit time Δ
T calculates temperature gap sequence δ t, is segmented to the temperature gap sequence δ t in the unit time by identical duration T2, calculates every section of temperature
The standard deviation of sequence of differences δ t, and form standard difference sequence η.
Consistency check module 4 is by comparing certain in abnormality detection threshold k and standard difference sequence η discovery train travelling process
After doubtful abnormal data occurs in section temperature gap sequence δ t, the information of this section of abnormal temperature sequence of differences δ t is recorded, and obtain
This section of temperature gap sequence xtAnd its temperature gap sequences y of previous time adjacent segmentst, while obtain same time period train its
The temperature gap sequence z1 that its analogous location sensor is surveyedt,…,znt, and just doubtful abnormal temperature gap sequence xtRespectively
With the temperature gap sequences y of previous time adjacent segmentstAnd the temperature gap sequence that other analogous location sensors are surveyed
{z1t,…,zntSeriatim carry out K-S distribution inspection.
Consistency check module 4 is judging temperature gap sequence x to be testedtWith the temperature gap sequence of previous time adjacent segments
Arrange ytAnd the temperature gap sequence { z1 that other analogous location sensors are surveyedt,…,zntDistribution consistency when, pass through inspection
The maximum disparity value D of empirical distribution function carrys out temperature sequence of differences x between sequencetConspicuousness.It is resulting when actually calculating
Maximum disparity value D is greater than K-S distribution probability P value corresponding to a certain established standards value or maximum disparity value D, and (i.e. distribution is consistent
Property examine probability of happening P value) be less than a certain established standards value when, then do not have consistency between two temperature gap sequences.
Wherein, temperature gap sequence xtSample size be n1, temperature gap sequences yt,z1t,…,zntIn any difference sequence
The sample size of column is n2, F1(x) and F2(x) distribution function that accumulates experience of two samples is respectively indicated, j is temperature gap sequence
Segment identification, x are sample.
Remember Dj=F1(xj)-F2(xj), Represent DjThe maximum value of absolute distance.Test statistics
Z is similar to normal distribution, expression formula are as follows:
When null hypothesis is true, Z converges on K according to Density Distribution d and is distributed, i.e., when sample is derived from one-dimensional continuously distributed F,
For the maximum value for taking B (F (x)) absolute distance, x is sample.
Empirical distribution function B (t) are as follows:
Wherein, x is independent variable, and i is natural number.
Embodiment 2
In the present embodiment, it since used data are mainly the timing type temperature data that sensor is surveyed, is carrying out
When exception self-test in real time because different train local environment, the difference of route and state and there are relative differents, cannot directly lead to
Excess temperature angle value is judged, it is therefore desirable to be carried out data classification reconstruct to temperature sequence data, be established difference sequence and be segmented meter
It is carried out abnormality detection again after calculating fluctuation.As shown in attached drawing 2 and attached drawing 3, a kind of embodiment of temperature sensor self checking method, tool
Body the following steps are included:
S10 difference processing) is carried out to the train temperature data sequence T that sensor is surveyed under normal operating conditions and obtains temperature
The segmentation criteria difference sequence Θ of sequence of differences δ T is spent, and by obtaining abnormality detection threshold to standard difference sequence Θ is for statistical analysis
Value K；
S20) to the sensor measured temperature data sequence t progress inputted in real time, identical difference processing is obtained with step S10)
To the segmentation criteria difference sequence η of temperature gap sequence δ t；
S30) it is based on step S10) obtained abnormality detection threshold k and step S20) obtained standard difference sequence η judgement point
Section temperature gap sequence δ t is with the presence or absence of abnormal；If the segmentation criteria difference sequence η of certain section of temperature gap sequence δ t is greater than or waits
In abnormality detection threshold k, then it is abnormal to judge that this section of temperature gap sequence δ t exists, and enter step S40), otherwise judgement senses
Device is normal；
S40) judgment step S30) it is middle in the presence of abnormal certain section of temperature gap sequence δ t and normal baseline sequence and previous phase
The distribution consistency of adjacent period temperature gap sequence δ t；If there is consistency, then judge that sensor is normal, if there is no
Consistency then judges sensor abnormality.
Step S10) further comprise:
S11 the temperature data sequence T that sensor is surveyed in the operational process of normal condition Train a part) is chosen, by single
Time Δ T (such as: 1s) calculates temperature gap sequence δ T for position；
S12 the temperature gap sequence δ T in the unit time is segmented by identical duration T1), calculates every section of temperature gap sequence
The standard deviation θ of δ Ti, and form standard difference sequence Θ；
S13) the distribution situation of analytical standard difference sequence Θ, and the mean μ and standard deviation sigma of standard difference sequence Θ are calculated, it presses
According to probability of happeningThe corresponding abnormality detection threshold k in the principle construction train position；
S14) according to step S11)~S13) to calculate same train different parts, different each positions of train corresponding for identical mode
Abnormality detection threshold k, and formation temperature sensor abnormality self-test threshold matrix.
Wherein, θ is the standard deviation of temperature gap sequence δ T.
Wherein, ωiFor weighting coefficient, hereinxiFor sample value, n is sample number.
Mean value-μ herein is often referred to the arithmetic average of sample, indicates the amount number of trend in a group data set, refers to
For the sum of all data again divided by the number of this group of data, it is an index for reflecting data central tendency in one group of data.
Standard deviation-σ is the arithmetic square root of the root after sum of sguares of deviation from mean is average namely variance.Standard deviation can reflect
The dispersion degree of one data set, or also referred to as degree of fluctuation.Average is identical, and standard deviation is not necessarily the same.Standard
Difference can be as a kind of probabilistic measurement.Such as: in actual measurement science, when carrying out repetition measurement, measure numerical value
The standard deviation of set represents the accuracy of these measurements.When whether measured value to be determined meets predicted value, the standard of measured value
Difference occupies decisive key player: if measurement average value is too wide in the gap with predicted value (while comparing with standard deviation),
Then think that measured value is conflicting with predicted value.It, then can be reasonable because if measured value is all fallen in except certain numerical value range
Whether inference predicted value is correct.
Step S20) further comprise:
S21) the temperature data sequence t that input pickup is surveyed in real time；
S22) temperature gap sequence δ t is calculated by unit time Δ t；
S23 the temperature gap sequence δ t in the unit time is segmented by identical duration T2), calculates every section of temperature gap sequence
The standard deviation of δ t, and form standard difference sequence η.
Judge that sensor (system) is abnormal due to the distribution threshold value fluctuated by abovementioned steps based on temperature difference fragment sequence,
There is also the problems that some possible wrong reports are abnormal, for example: some time point is originally not belonging to pass because jumping caused by signal problem once in a while
Sensor or system exception, and be then possible to be determined as sensor (system) exception by threshold value differentiation, to report by mistake.
For this reason, it may be necessary to strengthen judgment principle in conjunction with the characteristics of temperature difference overall distribution on the basis of threshold value differentiates.
And the reason of why improving abnormal self-test accuracy rate by distribution inspection, is, it is generally the case that in short-term
In, very big, essence change can not occur for the factor that same train same position influences temperature change, thus adjacent short
Temperature difference distribution should belong to same overall distribution in time, be unlikely to significant distributional difference occur, except the biography of non-measured temperature
Sensor or system there is a problem.In addition, can not determine which kind of parameter distribution different trains, different parts difference variation belong to
In the case where, the characteristics of real data itself changes is more in line with by the consistency that non-parametric test is distributed, thus K-S is different
This distribution method of inspection becomes a kind of most suitable selection in the technical solution that the present embodiment describes.
For respectively from two two different overall samples, it is desirable to examine their behinds overall distribution whether one
It causes, the K-S that can carry out two samples is examined, and principle is identical as single K-S inspection of sample, it is only necessary to by test statistics zero
The distribution of hypothesis changes the experience distribution of another sample into, and specific step is as follows.
Step S40) further comprise:
S41) by comparing certain section of temperature gap in abnormality detection threshold k and standard difference sequence η discovery train travelling process
After doubtful abnormal data occurs in sequence δ t, the information of this section of abnormal temperature sequence of differences δ t is recorded, and obtains this section of temperature difference
Value sequence xtAnd its temperature gap sequences y of previous time adjacent segmentst；
S42 the temperature gap sequence z1 that the other analogous location sensors of same time period train are surveyed) is obtainedt,…,znt；
S43) with regard to doubtful abnormal temperature gap sequence xtRespectively with the temperature gap sequences y of previous time adjacent segmentst, and
Temperature gap sequence { the z1 that other analogous location sensors are surveyedt,…,zntSeriatim carry out K-S distribution inspection；
S44) when the probability of happening P value of all inspections is respectively less than established standards, then output transducer abnormity early warning signal, no
Then sensor is normal, not output transducer abnormity early warning signal.
Step S103) further comprise:
If temperature gap sequence xtSample size be n1, temperature gap sequences yt,z1t,…,zntIn any sequence of differences
Sample size is n2, F1(x) and F2(x) distribution function that accumulates experience of two samples is respectively indicated, j is temperature gap sequence segment
Mark, x is sample.
Remember Dj=F1(xj)-F2(xj), Represent DjThe maximum value of absolute distance.Test statistics
Z is similar to normal distribution, expression formula are as follows:
When null hypothesis is true, Z converges on K according to Density Distribution d and is distributed, i.e., when sample is derived from one-dimensional continuously distributed F,
For the maximum value for taking B (F (x)) absolute distance, x is sample.
Empirical distribution function (i.e. Kolmogonov distribution function) B (t) are as follows:
Wherein, x is independent variable, and i is natural number；
As shown in Fig. 4, judging temperature gap sequence x to be testedtWith the temperature gap sequence of previous time adjacent segments
ytAnd the temperature gap sequence { z1 that other analogous location sensors are surveyedt,…,zntDistribution consistency when, pass through examine sequence
The maximum disparity value D of empirical distribution function carrys out temperature sequence of differences x between columntConspicuousness.When actually calculating is resulting most
Big gap value D is greater than K-S distribution probability P value corresponding to a certain established standards value or maximum disparity value D and marks less than a certain setting
When quasi- value, then refuse two sequences from same distribution totality it is assumed that obviously not having between two temperature gap sequences
Consistency.It has reason to illustrate, there are temperature difference sequences in apparent otherness, with aforementioned thought short time between two sequences
Column distribution is not in that the premise greatly changed is not inconsistent, further relate to it is abnormal be by temperature sensor (system) reason caused by.
The specific embodiment of the invention description temperature sensor self-checking unit and method based on big data platform, in conjunction with
The resulting temperature of sensor measurement for each associated components of train (axle, motor, transformer, current transformer etc.) that scene is fed back on the spot
Data construct the abnormal self-checking unit of a set of temperature sensor (system) and method, realize train sensor-based system automation,
Intelligent self-test and early warning can be realized train system to temperature by organically combining real data and statistical analysis algorithms
It spends that sensor is quick, effective self-test, and then has ensured the safe and efficient operation of train.The specific embodiment of the invention passes through calculating
The distribution characteristics of the volatility series of temperature gap, with certain determine the probability temperature difference abnormality detection threshold value, by sensor abnormality number
It is embodied according to fluctuating change in a short time is good, effectively detects the abnormal caused measurement of sensor (system)
The anomalous variation of temperature value greatly improves the abnormal self-test efficiency of sensor (system).Meanwhile specific embodiment of the invention base
In the train part temperature change the characteristics of and rule, using the little property of temperature difference changes in distribution in the short time, in conjunction with nonparametric
The method of inspection compares the otherness that the temperature difference between different periods is distributed, and extremely effective reduces the abnormal self-test of sensor (system) and pre-
Alert rate of false alarm substantially increases the overall accuracy of prediction.
Especially, it should be noted that in the above-mentioned specific embodiment of the present invention, by standard deviation to temperature difference sequence at
The mode of reason can also be conducted a research and be applied by the indexs such as coefficient of dispersion, very poor.Meanwhile in the specific embodiment of the invention
Used K-S nonparametric distribution inspection method, can also attempt using non-several ginseng methods such as unit root test, character check and its
Its Parametric test carries out distributional difference inspection.The temperature sensor self-checking unit and method of specific embodiment of the invention description
Reference code can be used for R and Python code, or can also be had using a series of language such as C, MATLAB, Java
Body is realized.
It, can by implementing the temperature sensor self-checking unit of specific embodiment of the invention description and the technical solution of method
It has the following technical effects:
(1) the temperature sensor self-checking unit and method of specific embodiment of the invention description, originally based on sensor (system)
The changing value of body measured temperature value carries out self-test and early warning, compared with the prior art in based on other variables or right such as electric current, voltages
For technical solution than more device measurement results, existing exception can more effectively, be more directly had found that it is likely that, monitor and pre-
Alert result will be more true, accurate；
(2) specific embodiment of the invention description temperature sensor self-checking unit and method, not merely with threshold value index into
Row self-test early warning, and further detected and noted abnormalities from the angle that contrast distribution changes, compared with the prior art only
Using one to two indication index for sensor fault is analyzed, the rule of self-test and early warning, result it is more accurate and
Effectively；
(3) the temperature sensor self-checking unit and method of specific embodiment of the invention description, is based on a large amount of actual motion mistakes
Normal and abnormal data carry out analysis and application in journey, compared with the prior art in based on data volume it is less the problems such as,
Model result it is relatively reliable, the factor of consideration more sufficiently, rationally, testability and practicability are also stronger；
(4) the temperature sensor self-checking unit and method of specific embodiment of the invention description, based in train travelling process
A large amount of temperature datas of measurement carry out real-time self-test and early warning, and data adjust automatically threshold value and distribution based on continuous renewal
Segmented mode is examined, there is significant high efficiency and intelligent level.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with it is other
The difference of embodiment, the same or similar parts in each embodiment may refer to each other.
The above described is only a preferred embodiment of the present invention, being not intended to limit the present invention in any form.Though
So the present invention is disclosed as above with preferred embodiment, and however, it is not intended to limit the invention.It is any to be familiar with those skilled in the art
Member, in the case where not departing from Spirit Essence of the invention and technical solution, all using in the methods and techniques of the disclosure above
Appearance makes many possible changes and modifications or equivalent example modified to equivalent change to technical solution of the present invention.Therefore,
Anything that does not depart from the technical scheme of the invention are made to the above embodiment any simple according to the technical essence of the invention
Modification, equivalent replacement, equivalence changes and modification still fall within the range of technical solution of the present invention protection.
Claims (10)
1. a kind of temperature sensor self-checking unit characterized by comprising
Abnormality detection threshold calculation module (1), for the train temperature data sequence that sensor is surveyed under normal operating conditions
Column T carries out difference processing and obtains the segmentation criteria difference sequence Θ of temperature gap sequence δ T, and by carrying out to standard difference sequence Θ
Statistical analysis obtains abnormality detection threshold k；
Critical eigenvalue extraction module (2), for carrying out difference processing to the sensor measured temperature data sequence t inputted in real time
Obtain the segmentation criteria difference sequence η of temperature gap sequence δ t；
First abnormality detection module (3), the abnormality detection threshold value for being exported according to the abnormality detection threshold calculation module (1)
K and the standard difference sequence η of the critical eigenvalue extraction module (2) output judge that wise temperature sequence of differences δ t whether there is
It is abnormal；If the segmentation criteria difference sequence η of certain section of temperature gap sequence δ t is greater than or equal to abnormality detection threshold k, judgement should
There is exception in section temperature gap sequence δ t, and export this section and there is abnormal temperature gap sequence δ t, otherwise judge sensor just
Often；
There is abnormal temperature gap sequence for exporting to the first abnormality detection module (3) in consistency check module (4)
δ t and normal baseline sequence and previous time adjacent segments temperature gap sequence δ t carry out distribution consistency check；
Second abnormality detection module (5), for judging that the distribution consistency check of consistency check module (4) output occurs
Whether probability P value is less than established standards, if it is less than established standards, then output transducer abnormity early warning signal, and otherwise sensor
Normally.
2. temperature sensor self-checking unit according to claim 1, it is characterised in that: the abnormality detection threshold calculations mould
Block (1) obtains the temperature data sequence T that sensor is surveyed in the operational process of normal condition Train a part, by unit time Δ
T calculates temperature gap sequence δ T；Temperature gap sequence δ T in unit time is segmented by identical duration T1, calculates every section of temperature
The standard deviation θ of sequence of differences δ Ti, and form standard difference sequence Θ；The distribution situation of analytical standard difference sequence Θ, and calculate standard
The mean μ and standard deviation sigma of difference sequence Θ, according to probability of happeningThe principle construction train position pair
The abnormality detection threshold k answered；
Wherein, θ is the standard deviation of temperature gap sequence δ T；
Wherein, ωiFor weighting coefficient, hereinxiFor sample value, n is sample number.
3. temperature sensor self-checking unit according to claim 1 or 2, it is characterised in that: the critical eigenvalue extracts
Module (2) obtains the temperature data sequence t that real-time input pickup is surveyed, and calculates temperature gap sequence δ t by unit time Δ t,
Temperature gap sequence δ t in unit time is segmented by identical duration T2, calculates the standard deviation of every section of temperature gap sequence δ t,
And form standard difference sequence η.
4. temperature sensor self-checking unit according to claim 3, it is characterised in that: the consistency check module (4)
It is doubted by comparing certain section of temperature gap sequence δ t in abnormality detection threshold k and standard difference sequence η discovery train travelling process
After abnormal data, the information of this section of abnormal temperature sequence of differences δ t is recorded, and obtain this section of temperature gap sequence xtAnd its
The temperature gap sequences y of previous time adjacent segmentst, while obtaining what the other analogous location sensors of same time period train were surveyed
Temperature gap sequence z1t..., znt, and just doubtful abnormal temperature gap sequence xtRespectively with the temperature of previous time adjacent segments
Sequence of differences ytAnd the temperature gap sequence { z1 that other analogous location sensors are surveyedt..., zntSeriatim carry out K-S distribution
It examines.
5. according to claim 1,2 or 4 described in any item temperature sensor self-checking units, it is characterised in that: the consistency
Inspection module (4) is judging temperature gap sequence x to be testedtWith the temperature gap sequences y of previous time adjacent segmentstAnd other phases
Temperature gap sequence { the z1 surveyed like position sensort..., zntDistribution consistency when, pass through experience between checking sequence point
The maximum disparity value D of cloth function carrys out temperature sequence of differences xtConspicuousness；It is big when actually calculating resulting maximum disparity value D
When the distribution probability P value corresponding to a certain established standards value or maximum disparity value D is less than a certain established standards value, then two
Do not have consistency between temperature gap sequence；
Wherein, temperature gap sequence xtSample size be n1, temperature gap sequences yt, z1t..., zntIn any sequence of differences sample
This amount is n2, F1(x) and F2(x) distribution function that accumulates experience of two samples is respectively indicated, j is temperature gap sequence segment mark
Know, x is sample；
Remember Dj=F1(xj)-F2(xj), Represent DjThe maximum value of absolute distance；Test statistics Z is approximate
In normal distribution, expression formula are as follows:
When null hypothesis is true, Z converges on K according to Density Distribution d and is distributed, i.e., when sample is derived from one-dimensional continuously distributed F,
For the maximum value for taking B (F (x)) absolute distance, x is sample；
Empirical distribution function B (t) are as follows:
Wherein, x is independent variable, and i is natural number.
6. a kind of temperature sensor self checking method, which comprises the following steps:
S10 difference processing) is carried out to the train temperature data sequence T that sensor is surveyed under normal operating conditions and obtains temperature difference
The segmentation criteria difference sequence Θ of value sequence δ T, and by obtaining abnormality detection threshold k to standard difference sequence Θ is for statistical analysis；
S20 the identical difference processing with step S10)) is carried out to the sensor measured temperature data sequence t inputted in real time and obtains temperature
Spend the segmentation criteria difference sequence η of sequence of differences δ t；
S30) it is based on step S10) obtained abnormality detection threshold k and step S20) obtained standard difference sequence η judgement segmentation temperature
Sequence of differences δ t is spent with the presence or absence of abnormal；If the segmentation criteria difference sequence η of certain section of temperature gap sequence δ t is greater than or equal to different
It is abnormal then to judge that this section of temperature gap sequence δ t exists, and enters step S40 by normal detection threshold value K), otherwise judging sensor just
Often；
S40) judgment step S30) in exist abnormal certain section of temperature gap sequence δ t and normal baseline sequence and it is previous adjacent when
Between section temperature gap sequence δ t distribution consistency；If there is consistency, then judge that sensor is normal, if there is no consistent
Property, then judge sensor abnormality.
7. temperature sensor self checking method according to claim 6, which is characterized in that the step S10) further wrap
It includes:
S11 the temperature data sequence T that sensor is surveyed in the operational process of normal condition Train a part) is chosen, when by unit
Between AT calculate temperature gap sequence δ T；
S12 the temperature gap sequence δ T in the unit time is segmented by identical duration T1), calculates every section of temperature gap sequence δ T's
Standard deviation θj, and form standard difference sequence Θ；
S13) the distribution situation of analytical standard difference sequence Θ, and the mean μ and standard deviation sigma of standard difference sequence Θ are calculated, according to hair
Raw probabilityThe corresponding abnormality detection threshold k in the principle construction train position；
Wherein, θ is the standard deviation of temperature gap sequence δ T；
Wherein, ωiFor weighting coefficient, hereinxiFor sample value, n is sample number.
8. temperature sensor self checking method according to claim 6 or 7, which is characterized in that the step S20) further
Include:
S21) the temperature data sequence t that input pickup is surveyed in real time；
S22) temperature gap sequence δ t is calculated by unit time Δ t；
S23 the temperature gap sequence δ t in the unit time is segmented by identical duration T2), calculates every section of temperature gap sequence δ t's
Standard deviation, and form standard difference sequence η.
9. temperature sensor self checking method according to claim 8, which is characterized in that the step S40) further wrap
It includes:
S41) by comparing certain section of temperature gap sequence δ in abnormality detection threshold k and standard difference sequence η discovery train travelling process
After doubtful abnormal data occurs in t, the information of this section of abnormal temperature sequence of differences δ t is recorded, and obtains this section of temperature gap sequence
xtAnd its temperature gap sequences y of previous time adjacent segmentst；
S42 the temperature gap sequence z1 that the other analogous location sensors of same time period train are surveyed) is obtainedt..., znt；
S43) with regard to doubtful abnormal temperature gap sequence xtRespectively with the temperature gap sequences y of previous time adjacent segmentstAnd it is other
Temperature gap sequence { the z1 that analogous location sensor is surveyedt..., zntSeriatim carry out K-S distribution inspection；
S44) when the probability of happening P value of all inspections is respectively less than established standards, then output transducer abnormity early warning signal, otherwise passes
Sensor is normal.
10. according to any one of claim 6,7 or 9 or the temperature sensor self checking method, which is characterized in that the step
S103) further comprise:
If temperature gap sequence xtSample size be n1, temperature gap sequences yt, z1t..., zntIn any sequence of differences sample
Amount is n2, F1(x) and F2(x) distribution function that accumulates experience of two samples is respectively indicated, j is temperature gap sequence segment mark,
X is sample；
Remember Dj=F1(xj)-F2(xj), Represent DjThe maximum value of absolute distance；Test statistics Z is approximate
In normal distribution, expression formula are as follows:
When null hypothesis is true, Z converges on K according to Density Distribution d and is distributed, i.e., when sample is derived from one-dimensional continuously distributed F,
For the maximum value for taking B (F (x)) absolute distance, x is sample；
Empirical distribution function B (t) are as follows:
Wherein, x is independent variable, and i is natural number；
Judging temperature gap sequence x to be testedtWith the temperature gap sequences y of previous time adjacent segmentstAnd other analogous locations
Temperature gap sequence { the z1 that sensor is surveyedt..., zntDistribution consistency when, pass through empirical distribution function between checking sequence
Maximum disparity value D carry out temperature sequence of differences xtConspicuousness；When reality calculates resulting maximum disparity value D greater than a certain
When distribution probability P value corresponding to established standards value or maximum disparity value D is less than a certain established standards value, then two temperature differences
Do not have consistency between value sequence.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6013533A (en) * | 1997-09-05 | 2000-01-11 | Lsi Logic Corporation | Real time quiescent current test limit methodology |
| CN101581587A (en) * | 2009-06-23 | 2009-11-18 | 北京航空航天大学 | Method for automatically evaluating uncertainty of measurement of virtual instrument |
| CN104408907A (en) * | 2014-10-31 | 2015-03-11 | 重庆大学 | Highway traffic incident duration time prediction method with on-line optimization capability |
| CN106112697A (en) * | 2016-07-15 | 2016-11-16 | 西安交通大学 | A kind of milling parameter automatic alarm threshold setting method based on 3 σ criterions |
| CN106570073A (en) * | 2016-10-14 | 2017-04-19 | 周磊 | Method and apparatus for screening rough errors of ground surface water quality data |
-
2018
- 2018-10-23 CN CN201811236829.6A patent/CN109186813B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6013533A (en) * | 1997-09-05 | 2000-01-11 | Lsi Logic Corporation | Real time quiescent current test limit methodology |
| CN101581587A (en) * | 2009-06-23 | 2009-11-18 | 北京航空航天大学 | Method for automatically evaluating uncertainty of measurement of virtual instrument |
| CN104408907A (en) * | 2014-10-31 | 2015-03-11 | 重庆大学 | Highway traffic incident duration time prediction method with on-line optimization capability |
| CN106112697A (en) * | 2016-07-15 | 2016-11-16 | 西安交通大学 | A kind of milling parameter automatic alarm threshold setting method based on 3 σ criterions |
| CN106570073A (en) * | 2016-10-14 | 2017-04-19 | 周磊 | Method and apparatus for screening rough errors of ground surface water quality data |
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