CN107179064B - A kind of determination method of the confidence level of wheelset profile on-line detecting system measured value - Google Patents
A kind of determination method of the confidence level of wheelset profile on-line detecting system measured value Download PDFInfo
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- G01—MEASURING; TESTING
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Abstract
The present invention discloses a kind of determination method of the confidence level of wheelset profile on-line detecting system measured value, comprising the following steps: S1, the multiple evaluation indexes for determining wheelset profile on-line detecting system measured value；S2, the set that the multiple evaluation indexes having determined are divided into influence factor, with the assessment level of the confidence level of the determination measured value；S3, the evaluation factor for being determined to each influence factor of accurate evaluation, and grade assessment is carried out to each evaluation factor by fuzzy number；S4, the assessment weight that difference evaluation factors under each described evaluation index are calculated using analytic hierarchy process (AHP)；The parameter of each evaluation index of S5, comprehensive quantification；S6, the confidence level for determining wheelset profile on-line detecting system measured value.The present invention can directly and efficiently judge the degree of reliability of each measured value of wheelset profile on-line detecting system, so that the confidence level of measurement result can rationally be quantified each time.
Description
Technical field
The invention belongs to traffic safety field of engineering technology, and in particular to a kind of wheelset profile on-line detecting system measured value
Confidence level determination method.
Background technique
In the basic transport development such as subway, light rail, take turns to the total weight for being subjected to car body, and be responsible for biography
Wheel is passed between the active force rail, therefore, belongs to a particularly important component in travelled by vehicle portion.For lasting acquisition
Accurate wheelset profile parameter, with the real-time quality condition for grasping wheel pair, lot of domestic and foreign experts and scholars and company are developed
Various wheelset profile on-line detecting systems.But due to the not perfect of detection system measurement method, test equipment and
The influence of the factors such as site environment, wheelset profile on-line detecting system are inevitably present uncertain in measurement process
Property, there are the unfavorable conditions such as unreliable, confidence level is low so as to cause the measured value of wheelset profile on-line detecting system.
Based on the above issues, for the measured value analysis on Uncertainty of rail wheels geometric parameters measuring system and error analysis,
Related scholar has done a large amount of research.Such as:
(1) Li Dewei of Southwest Jiaotong University is proposed in paper " contactless rail wheels geometric parameters measurement accuracy research "
A kind of mechanism geometric parameter self-calibrating method based on contact measuring head further improves wheel to the measurement essence of measuring system
Degree；
(2) Lin Congcong of East China University of Science establish wheel eccentricities, sensor offset, sensor and measurement interfacial angle and
Wheel axis tilts comprehensive function error model, and has carried out Evaluation of Uncertainty to tyre tread key measurement point；
(3) Gao Yan of Beijing Jiaotong University is mentioned after analyzing wheel to several uncertainty factors of on-line measurement system
The calculation method of Composite Seismogram is gone out.
But in existing research and analyse, referring only to wheelset profile on-line detecting system uncertainty of measurement and
Measurement accuracy can not directly and efficiently judge the degree of reliability of each measured value of wheelset profile on-line detecting system, change speech
It, the confidence level of measurement result is all difficult to reasonably be quantified each time.
Summary of the invention
In order to solve defect present in the prior art, the present invention provides a kind of measurements of wheelset profile on-line detecting system
The determination method of the confidence level of value can directly and efficiently judge the reliable of each measured value of wheelset profile on-line detecting system
Degree.
To solve the above-mentioned problems, the present invention is achieved by following technical scheme:
A kind of determination method of the confidence level of wheelset profile on-line detecting system measured value, which is characterized in that including following
Step:
S1, the multiple evaluation indexes for determining wheelset profile on-line detecting system measured value；
S2, the set that the multiple evaluation indexes having determined are divided into influence factor, with the determination measurement
The assessment level of the confidence level of value；
S3, the evaluation factor for being determined to each influence factor of accurate evaluation, and by fuzzy number to each assessment
The factor carries out grade assessment；
S4, the appraisal right that difference evaluation factors under each described evaluation index are calculated using analytic hierarchy process (AHP)
Weight；
The parameter of each evaluation index of S5, comprehensive quantification；
S6, the confidence level for determining wheelset profile on-line detecting system measured value.
Further, the evaluation index includes detection consistency uncertainty, vehicle consistency uncertainty, algorithm
Consistency uncertainty.
Further, in step s 2, the multiple evaluation indexes having determined are divided into the collection of influence factor
It closes, specifically includes:
The detection consistency uncertainty includes sensor accuracy, sensor sample frequency, adjacent measured values difference
Degree；
The vehicle consistency uncertainty include coaxial wheels to parameter differences degree, with steering framing wheel to parameter differences
Degree, with compartment wheel to parameter differences degree；
The algorithm consistency uncertainty include centrifugation away from, right side inclination angle, average value group number.
Further, in step s3, the fuzzy number is Trapezoid Fuzzy Number or Triangular Fuzzy Number.
Further, in step s 4, it is calculated described in the difference under each described evaluation index using analytic hierarchy process (AHP)
The specific method of the assessment weight of evaluation factor includes:
S41, evaluation factor assessment hierarchy Model is established, top layer is the evaluation index, and middle layer is the evaluation
The corresponding influence factor of index, bottom are the evaluation factor；If middle layer has m influence factor, it is denoted as e respectively1,e2,
...em, then the corresponding influence factor of evaluation index integrates as Ue={ e1,e2,...em}；If bottom has the n evaluation factors, point
U is not denoted as it1,u2,...un, then corresponding evaluation factor integrates as Uu={ u1,u2,...un}；
S42, a is setijIndicate eiTo ejRelative importance numerical value, aijValue uses 1~9 scaling law；According to 1~9 scale
Numerical scale and its representative meaning in method, building influence factor to the judgment matrix P of evaluation index,
In judgment matrix P of the influence factor to evaluation index,
Wherein i, j=1,2 ... m；
S43, influence factor is set to the degree of consistency index of the judgment matrix P of evaluation index as CR,
Wherein,
RI is corresponding coincident indicator, can be obtained by inquiring the RI table of comparisons, λmaxIt indicates
Maximum eigenvalue of the influence factor to the judgment matrix P of evaluation index；
Examine influence factor to the consistency of the judgment matrix P of evaluation index.
S44, sub-step S42, sub-step S43 are repeated, constructs evaluation factor respectively to the judgment matrix of influence factor
P1,P2,...,Pm, and its consistency is examined respectively；
S45, influence factor is calculated to the corresponding feature vector of judgment matrix P maximum eigenvalue of evaluation index, and return
One changes to obtain weight vectors α；Evaluation factor is calculated separately out to the judgment matrix P of influence factor1,P2,...,PmMaximum eigenvalue pair
The feature vector answered, and normalize to obtain β1,β2,...,βm, then the assessment weight w of evaluation factor be,
W=(β1,β2,...,βm)·α
Further, in step S43, examine influence factor to the specific of the consistency of the judgment matrix P of evaluation index
Method includes:
When CR≤0.1, it is determined that influence factor meets coherence request to the judgment matrix P of evaluation index；If CR > 0.1,
Influence factor is then readjusted to the judgment matrix P of evaluation index until meeting coherence request.
Further, the specific method of the parameter of each evaluation index of comprehensive quantification includes:
Using each evaluation index of fuzzy comprehensive evaluation method comprehensive quantification, to obtain the mould of each evaluation index
Paste value；Again using gravity model appoach to each fuzzy value de-fuzzy.
Further, the formula of the gravity model appoach are as follows:
Further, in step s 6, the specific method of the confidence level of wheelset profile on-line detecting system measured value is determined
Include:
Determine the weight of the parameter of each evaluation index using expert opinion method, and by the weight with have determined
The quantized value of each evaluation index is multiplied, and finally obtains the specific value of the confidence level of the measured value.
Compared with prior art, the method have the benefit that:
(1) the determination method of the confidence level of a kind of wheelset profile on-line detecting system measured value provided by the invention, can be with
Directly and efficiently judge the degree of reliability of each measured value of wheelset profile on-line detecting system, i.e., it can be well to every
The confidence level of one-shot measurement result is rationally quantified.Specifically, such as: 1. the present invention using fuzzy number to evaluation factor into
The assessment of row grade can significantly reduce influence of the subjective resolution of expert in Team Decision Making to result；2. the present invention uses
Analytic hierarchy process (AHP) calculates the assessment weight of the different evaluation factors under each evaluation index, so that calculated result has more reasonability.
(2) the determination method of the confidence level of a kind of wheelset profile on-line detecting system measured value provided by the invention, application
The parameter of each evaluation index of fuzzy comprehensive evaluation method comprehensive quantification, can be further improved the evaluation effect of each evaluation index
Reliability.
In conclusion the present invention meets the actual demand of Modern Traffic engineering construction very much, in traffic safety engineering technology
Field has a vast market foreground.
Detailed description of the invention
Specific embodiments of the present invention will be described in further detail with reference to the accompanying drawing, in which:
Fig. 1 is one embodiment party of determination method of the confidence level of wheelset profile on-line detecting system measured value of the present invention
The step schematic diagram of formula；
Fig. 2 is 1~9 scaling law chart of the present invention；
Fig. 3 is the assessment level schematic diagram of the confidence level of measured value described in embodiment 1；
Fig. 4 is the grade assessment table of detection degree described in embodiment 1；
Fig. 5 is the grade assessment table of rationality described in embodiment 1；
Fig. 6 is the grade assessment table of disturbance degree described in embodiment 1.
Fig. 7 is the evaluation grade evaluation form of the influence factor of detection consistency uncertainty described in embodiment 1.
Specific embodiment
In the following description, numerous specific details are set forth in order to facilitate a full understanding of the present invention, but the present invention can be with
It is different from the other modes of this description much to implement, those skilled in the art can be without violating the connotation of the present invention
Similar popularization is done, therefore the present invention is not limited by following public specific embodiment.
As shown in FIG. 1 to FIG. 2, the invention discloses a kind of confidence levels of wheelset profile on-line detecting system measured value really
Determine method, comprising the following steps:
S1, the multiple evaluation indexes for determining wheelset profile on-line detecting system measured value；
Comprehensively consider influence wheelset profile on-line detecting system measurement result various aspects, such as system structure composition,
The influence etc. of Measurement Algorithm analysis and vehicle for measurement, therefore, in above-mentioned steps S1, evaluation index includes detection
Consistency uncertainty, vehicle consistency uncertainty, algorithm consistency uncertainty etc..
S2, the set that the multiple evaluation indexes having determined are divided into influence factor, to determine the confidence of measured value
The assessment level of degree；
In above-mentioned steps S2, further, the multiple evaluation indexes having determined are divided into the collection of influence factor
It closes, particular content includes:
Detecting consistency uncertainty includes sensor accuracy, sensor sample frequency, adjacent measured values difference degree
Deng；
Vehicle consistency uncertainty include coaxial wheels to parameter differences degree, with steering framing wheel to parameter difference off course
Degree, with compartment wheel to parameter differences degree etc.；
Algorithm consistency uncertainty includes centrifugation away from, right side inclination angle, average value group number etc..
Different affecting factors under S3, each evaluation index of research, are determined to commenting for each influence factor of accurate evaluation
Estimate the factor, and grade assessment is carried out to each evaluation factor by fuzzy number, that is, determines each evaluation factor opinion rating language
Say the corresponding relationship of variable and fuzzy language (specially its corresponding fuzzy number)；
In above-mentioned steps S3, fuzzy number is selected as Trapezoid Fuzzy Number or Triangular Fuzzy Number.
S4, the assessment weight that different evaluation factors under each evaluation index are calculated using analytic hierarchy process (AHP), it is specific
Method includes:
S41, evaluation factor assessment hierarchy Model is established, top layer is evaluation index, and middle layer is corresponding for evaluation index
Influence factor, bottom is evaluation factor；If middle layer has m influence factor, it is denoted as e respectively1,e2,...em, then evaluation index
Corresponding influence factor integrates as Ue={ e1,e2,...em}；If bottom there are the n evaluation factors, it is denoted as u respectively1,u2,
...un, then corresponding evaluation factor integrates as Uu={ u1,u2,...un}；
S42, a is setijIndicate eiTo ejRelative importance numerical value, aijValue uses 1~9 scaling law；According to 1~9 scale
Numerical scale and its representative meaning in method, building influence factor to the judgment matrix P of evaluation index,
In judgment matrix P of the influence factor to evaluation index,
Wherein i, j=1,2 ... m；
S43, influence factor is set to the degree of consistency index of the judgment matrix P of evaluation index as CR,
Wherein,
RI is corresponding coincident indicator, can be obtained by inquiring the RI table of comparisons, λmaxIt indicates
Maximum eigenvalue of the influence factor to the judgment matrix P of evaluation index；
Examine influence factor to the consistency of the judgment matrix P of evaluation index, its specific method includes:
When CR≤0.1, it is determined that influence factor meets coherence request to the judgment matrix P of evaluation index；If CR > 0.1,
Influence factor is then readjusted to the judgment matrix P of evaluation index until meeting coherence request.
S44, sub-step S42, sub-step S43 are repeated, constructs evaluation factor respectively to the judgment matrix of influence factor
P1,P2,...,Pm, and its consistency is examined respectively；
S45, influence factor is calculated to the corresponding feature vector of judgment matrix P maximum eigenvalue of evaluation index, and return
One changes to obtain weight vectors α；Evaluation factor is calculated separately out to the judgment matrix P of influence factor1,P2,...,PmMaximum eigenvalue pair
The feature vector answered, and normalize to obtain β1,β2,...,βm, then the assessment weight w of evaluation factor be,
W=(β1,β2,...,βm)·α
The parameter of each evaluation index of S5, comprehensive quantification, its specific method includes:
Using each evaluation index of fuzzy comprehensive evaluation method comprehensive quantification, to obtain the fuzzy value of each evaluation index；Again
Using gravity model appoach to each fuzzy value de-fuzzy.
Wherein, the formula of gravity model appoach are as follows:
S6, the confidence level for determining wheelset profile on-line detecting system measured value, its specific method includes:
Determine the weight of the parameter of each evaluation index using expert opinion method, and by the weight with have determined it is each
The quantized value of evaluation index is multiplied, and finally obtains the specific value of the confidence level of measured value.
Using field erected a set of wheelset profile on-line detecting system as subjects, the wheelset profile on-line checking is chosen
Two groups of adjacent measurement data that system measures.
The evaluation index for defining the measured value confidence level of the wheelset profile on-line detecting system includes to detect consistency not
Degree of certainty J, vehicle consistency uncertainty V, algorithm consistency uncertainty A；Each evaluation index is further divided into shadow
The set of the factor of sound is shown in attached drawing 3 to determine the assessment level of the confidence level of measured value.
After the different affecting factors being comprehensively compared under each evaluation index, the evaluation factor of each influence factor is determined to visit
Estimate λ (referring to the degree that each influence factor can be detected), rationality τ (refers to that each influence factor value is inclined
From expected or critical field degree), disturbance degree β (refer to the journey that each influence factor influences measured value confidence level
Degree), grade assessment then is carried out to each evaluation factor with Trapezoid Fuzzy Number, specific as follows:
(1) determine that the fuzzy class of detection degree λ divides, specific grade assessment table is shown in Fig. 4；
(2) determine that the fuzzy class of rationality τ divides, specific grade assessment table is shown in Fig. 5；
(3) determine that the fuzzy class of disturbance degree β divides, specific grade assessment table is shown in Fig. 6.
The assessment weight that the different evaluation factors under each evaluation index are calculated using analytic hierarchy process (AHP), since evaluation refers to
It marks more, is hereafter only illustrated for detecting consistency uncertainty J, specific as follows:
1. constructing influence factor to the judgment matrix P of evaluation index:
2. examining influence factor to the consistency of the judgment matrix P of evaluation index: λmax=3.02,
Accordingly, it is determined that judgment matrix P of the influence factor to evaluation index
Meet coherence request.
3. constructing evaluation factor to the judgment matrix P of influence factor1,P2,P3:
4. examining evaluation factor to the consistency of the judgment matrix of influence factor: λ1 max=3.02, 
Determine matrix P1Meet coherence request；λ2 max=3.05,
Determine matrix P2Meet coherence request；λ3 max=
3.05
Determine matrix P3Meet coherence request.
5. calculating influence factor to the corresponding feature vector of judgment matrix P maximum eigenvalue of evaluation index, and normalize
Obtain weight vectors α=(0.14,0.63,0.24)T；Evaluation factor is calculated separately to the judgment matrix P of influence factor1,P2,P3Most
The corresponding feature vector of big characteristic value, and normalize to obtain β1=(0.10,0.33,0.57)T, β2=(0.32,0.22,0.46)T, β3
=(0.28,0.65,0.07)T。
6. the assessment weight w of detection consistency uncertainty J is calculated1For (0.2828,0.3408,0.3864).
Above-mentioned sub-step is repeated, the assessment weight for obtaining vehicle consistency uncertainty V is w2=(0.40,0.34,
0.26)；The assessment weight of algorithm consistency uncertainty A is w3=(0.323,0.257,0.42).
Using each evaluation index of fuzzy comprehensive evaluation method comprehensive quantification, hereafter for detecting consistency uncertainty J,
It is described according to one group of data in the two of selection groups of data, specific as follows:
Firstly, table is assessed according to the grade of each evaluation factor, to three influence factors of detection consistency uncertainty J
Grade assessment is carried out, sees Fig. 7, then:
1. establishing initial evaluation matrix F1:
2. the assessment weight w of the detection consistency uncertainty J obtained in conjunction with before1, establish weighted normal decision square
Battle array V1:
Using gravity model appoach to above-mentioned fuzzy value de-fuzzy, i.e., according to the formula of gravity model appoach to weighted normal decision matrix
V1De-fuzzy processing is carried out, is obtained:
V1=[0.846 0.736 0.474]
To weighted normal decision matrix V1In element be averaged, obtain detection consistency uncertainty J quantization
Value Z1It is 0.685.
Above-mentioned sub-step is repeated, it is available: the quantized value Z of vehicle consistency uncertainty V2It is 0.627, algorithm is consistent
The quantized value Z of property uncertainty A3It is 0.436.
Detection consistency uncertainty J, vehicle consistency uncertainty V, algorithm consistency are determined using expert opinion method
The weight of uncertainty A is respectively 0.3,0.35,0.35, by the quantized value phase of weight and predetermined each evaluation index
Multiply, the specific value for finally obtaining the confidence level of measured value is 0.578.
The other parts of the determination method of the confidence level of wheelset profile on-line detecting system measured value described in the present embodiment are joined
See the prior art.
The above described is only a preferred embodiment of the present invention, be not intended to limit the present invention in any form, therefore
Without departing from the technical solutions of the present invention, according to the technical essence of the invention it is to the above embodiments it is any modification,
Equivalent variations and modification, all of which are still within the scope of the technical scheme of the invention.
Claims (5)
1. a kind of determination method of the confidence level of wheelset profile on-line detecting system measured value, which is characterized in that including following step
It is rapid:
S1, the multiple evaluation indexes for determining wheelset profile on-line detecting system measured value；The evaluation index includes detection one
Cause property uncertainty, vehicle consistency uncertainty, algorithm consistency uncertainty；
S2, the set that the multiple evaluation indexes having determined are divided into influence factor, with the determination measured value
The assessment level of confidence level, specifically includes:
The detection consistency uncertainty includes sensor accuracy, sensor sample frequency, adjacent measured values difference degree；
The vehicle consistency uncertainty include coaxial wheels to parameter differences degree, with steering framing wheel to parameter difference off course
Degree, with compartment wheel to parameter differences degree；
The algorithm consistency uncertainty include centrifugation away from, right side inclination angle, average value group number；
S3, the evaluation factor for being determined to each influence factor of accurate evaluation, and by fuzzy number to each evaluation factor
Carry out grade assessment；The evaluation factor includes detection degree, rationality, disturbance degree；
S4, the assessment weight that difference evaluation factors under each described evaluation index are calculated using analytic hierarchy process (AHP)；
The parameter of each evaluation index of S5, comprehensive quantification, specific method include:
Using each evaluation index of fuzzy comprehensive evaluation method comprehensive quantification, to obtain the fuzzy of each evaluation index
Value；Again using gravity model appoach to each fuzzy value de-fuzzy；
S6, the confidence level for determining wheelset profile on-line detecting system measured value, specific method include:
Determine the weight of the parameter of each evaluation index using expert opinion method, and by the weight with have determined it is each
The quantized value of the evaluation index is multiplied, and finally obtains the specific value of the confidence level of the measured value.
2. the determination method of the confidence level of wheelset profile on-line detecting system measured value, feature exist according to claim 1
In: in step s3, the fuzzy number is Trapezoid Fuzzy Number or Triangular Fuzzy Number.
3. the determination method of the confidence level of wheelset profile on-line detecting system measured value, feature exist according to claim 1
In in step s 4, the assessment of the difference evaluation factors under each described evaluation index being calculated using analytic hierarchy process (AHP)
The specific method of weight includes:
S41, evaluation factor assessment hierarchy Model is established, top layer is the evaluation index, and middle layer is the evaluation index
Corresponding influence factor, bottom are the evaluation factor；If middle layer has m influence factor, it is denoted as e respectively1,e2,...em, then
The corresponding influence factor of evaluation index integrates as Ue={ e1,e2,...em}；If bottom there are the n evaluation factors, it is denoted as u respectively1,
u2,...un, then corresponding evaluation factor integrates as Uu={ u1,u2,...un}；
S42, a is setijIndicate eiTo ejRelative importance numerical value, aijValue uses 1~9 scaling law；According in 1~9 scaling law
Numerical scale and its representative meaning, building influence factor to the judgment matrix P of evaluation index,
In judgment matrix P of the influence factor to evaluation index, aii=1,
Wherein i, j=1,2 ... m；
S43, influence factor is set to the degree of consistency index of the judgment matrix P of evaluation index as CR,
Wherein,
RI is corresponding coincident indicator, can be obtained by inquiring the RI table of comparisons, λmaxIt indicates to influence
Maximum eigenvalue of the factor to the judgment matrix P of evaluation index；
Examine influence factor to the consistency of the judgment matrix P of evaluation index；
S44, sub-step S42, sub-step S43 are repeated, constructs evaluation factor respectively to the judgment matrix P of influence factor1,
P2,...,Pm, and its consistency is examined respectively；
S45, influence factor is calculated to the corresponding feature vector of judgment matrix P maximum eigenvalue of evaluation index, and normalize
Obtain weight vectors α；Evaluation factor is calculated separately out to the judgment matrix P of influence factor1,P2,...,PmMaximum eigenvalue is corresponding
Feature vector, and normalize to obtain β1,β2,...,βm, then the assessment weight w of evaluation factor be,
W=(β1,β2,...,βm)·α。
4. the determination method of the confidence level of wheelset profile on-line detecting system measured value, feature exist according to claim 3
In in step S43, inspection influence factor includes: to the specific method of the consistency of the judgment matrix P of evaluation index
When CR≤0.1, it is determined that influence factor meets coherence request to the judgment matrix P of evaluation index；If CR > 0.1 weighs
New adjustment influence factor is to the judgment matrix P of evaluation index until meeting coherence request.
5. the determination method of the confidence level of wheelset profile on-line detecting system measured value, feature exist according to claim 1
In the formula of the gravity model appoach are as follows:
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