CN107179064A - 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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Abstract
The present invention discloses a kind of determination method of the confidence level of wheelset profile on-line detecting system measured value, comprises 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 for the confidence level for determining the 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 different evaluation factors calculated using analytic hierarchy process (AHP) under each described evaluation index assessment 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.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 the technical field of traffic safety engineering, and particularly relates to a method for determining the confidence of a measured value of a wheel set dimension online detection system.
Background
In the infrastructure of subways, light rails and the like, the wheel set not only bears the whole weight of a vehicle body, but also is responsible for transmitting the acting force between the wheel set and a steel rail, so that the wheel set belongs to an extremely important part in a vehicle running part. In order to continuously obtain accurate wheel set dimension parameters and master the quality condition of the wheel set in real time, a plurality of experts, scholars and companies at home and abroad develop various on-line wheel set dimension detection systems. However, due to the influence of factors such as imperfection of a measurement method of the detection system, a test instrument, a field environment and the like, uncertainty inevitably exists in the wheel set dimension online detection system in the measurement process, so that the measured value of the wheel set dimension online detection system is unreliable, low in confidence degree and the like.
Based on the above problems, a great deal of research is conducted by relevant scholars aiming at the uncertainty analysis and the error analysis of the measured value of the wheel set geometric parameter measuring system. Such as:
(1) ledevier of the university of transportation in southwest provides a mechanism geometric parameter self-calibration method based on a contact measuring head in a paper 'non-contact wheel set geometric parameter measurement precision research', so that the measurement precision of a wheel set measurement system is further improved;
(2) the rush mat clever of the university of eastern China establishes a comprehensive action error model of wheel eccentricity, sensor offset, intersection angle of a sensor and a measuring surface and wheel axis inclination, and carries out uncertainty evaluation on key measuring points of the tread;
(3) after analyzing several uncertainty factors of the wheel set online measurement system, the high rock of Beijing university of transportation provides a calculation method of synthetic uncertainty.
However, in the existing research and analysis, only the measurement uncertainty and the measurement accuracy of the wheel set dimension online detection system are referred to, and the reliability of each measurement value of the wheel set dimension online detection system cannot be directly and effectively judged, in other words, the confidence of each measurement result is difficult to be reasonably quantized.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a method for determining the confidence of the measured value of the wheel pair size online detection system, which can directly and effectively judge the reliability of the measured value of the wheel pair size online detection system each time.
In order to solve the problems, the invention is realized according to the following technical scheme:
a method for determining the confidence of the measured value of an online wheel pair size detection system is characterized by comprising the following steps:
s1, determining a plurality of evaluation indexes of the measured value of the wheel set dimension online detection system;
s2, dividing the determined evaluation indexes into a set of influence factors so as to determine the evaluation level of the confidence degree of the measured value;
s3, determining evaluation factors capable of accurately evaluating the influence factors, and carrying out grade evaluation on the evaluation factors through fuzzy numbers;
s4, calculating the evaluation weight of different evaluation factors under each evaluation index by adopting an analytic hierarchy process;
s5, comprehensively quantifying parameters of the evaluation indexes;
and S6, determining the confidence coefficient of the measured value of the wheel set dimension online detection system.
Further, the evaluation index includes detection consistency uncertainty, vehicle consistency uncertainty and algorithm consistency uncertainty.
Further, in step S2, dividing each of the determined evaluation indexes into a set of influencing factors specifically includes:
the detection consistency uncertainty comprises sensor precision, sensor sampling frequency and adjacent measurement value difference degree;
the uncertainty of the vehicle consistency comprises the parameter difference degree of coaxial wheel pairs, the parameter difference degree of wheel pairs with the same bogie and the parameter difference degree of wheel pairs with the same carriage;
the uncertainty of the algorithm consistency comprises the centrifugal distance, the right end face inclination angle and the average group number.
Further, in step S3, the blur number is a trapezoidal blur number or a triangular blur number.
Further, in step S4, a specific method for calculating the evaluation weight of different evaluation factors under each of the evaluation indexes by using an analytic hierarchy process includes:
s41, establishing an evaluation factor evaluation hierarchical structure model, wherein the top layer is the evaluation index, the middle layer is the influence factor corresponding to the evaluation index, and the bottom layer is the evaluation factor; let the middle layer have m influencing factors, which are respectively marked as e1,e2,...emIf the evaluation index corresponds to the influence factor set Ue＝{e1,e2,...em}; let the bottom layer have n said evaluation factors, which are respectively marked as u1,u2,...unThen the corresponding evaluation factor set is Uu＝{u1,u2,...un}；
S42, let aijDenotes eiTo ejRelative importance value of aijThe value is obtained by a 1-9 scale method; constructing a judgment matrix P of the evaluation index of the influencing factors according to the numerical scale in the scale 1-9 and the meaning represented by the numerical scale,
in the judgment matrix P of the influence factors on the evaluation indexes, aii＝1，Wherein i, j is 1, 2.. m;
s43, setting the consistency degree index of the judgment matrix P of the influence factors to the evaluation index as CR,
wherein,RI is corresponding consistency index and can be obtained by inquiring RI comparison table, lambdamaxRepresenting the maximum characteristic value of the judgment matrix P of the evaluation index by the influence factors;
and (5) checking the consistency of the influencing factors on the judgment matrix P of the evaluation index.
S44, repeatedly executing the substep S42 and the substep S43, and respectively constructing a judgment matrix P of the evaluation factor to the influence factors1,P2,...,PmAnd respectively checking the consistency;
s45, calculating the eigenvector corresponding to the maximum eigenvalue of the judgment matrix P of the evaluation index by the influence factor, normalizing to obtain weight vector α, and calculating the judgment matrix P of the evaluation factor to the influence factor respectively1,P2,...,PmThe feature vector corresponding to the maximum feature value is normalized to β1,β2,...,βmThen the evaluation weight w of the evaluation factor is,
w＝(β1,β2,...,βm)·α
further, in step S43, a specific method of checking the consistency of the influence factors with respect to the determination matrix P of the evaluation index includes:
when CR is less than or equal to 0.1, determining that the judgment matrix P of the evaluation index by the influencing factors meets the consistency requirement; if CR is larger than 0.1, readjusting the judgment matrix P of the influencing factors on the evaluation indexes until the consistency requirement is met.
Further, a specific method for comprehensively quantifying the parameters of each of the evaluation indexes includes:
comprehensively quantifying each evaluation index by using a fuzzy comprehensive evaluation method to obtain a fuzzy value of each evaluation index; and then, defuzzifying each fuzzy value by adopting a gravity center method.
Further, the formula of the gravity center method is as follows:
further, in step S6, the specific method for determining the confidence of the wheel set dimension online detection system measurement value includes:
and determining the weight of the parameter of each evaluation index by using an expert opinion method, and multiplying the weight by the determined quantized value of each evaluation index to finally obtain a specific numerical value of the confidence coefficient of the measured value.
Compared with the prior art, the invention has the beneficial technical effects that:
(1) the method for determining the confidence coefficient of the measured value of the wheel pair size online detection system can directly and effectively judge the reliability degree of each measured value of the wheel pair size online detection system, namely, the confidence coefficient of each measured result can be reasonably quantized well. Specifically, for example: the invention adopts fuzzy number to evaluate the grade of the evaluation factor, which can effectively reduce the influence of the subjective decision of experts in team decision on the result; and secondly, the evaluation weights of different evaluation factors under each evaluation index are calculated by adopting an analytic hierarchy process, so that the calculation result is more reasonable.
(2) According to the method for determining the confidence of the measured value of the wheel set dimension online detection system, the parameters of each evaluation index are comprehensively quantized by using the fuzzy comprehensive evaluation method, and the reliability of the evaluation effect of each evaluation index can be further improved.
In conclusion, the invention is very suitable for the actual requirements of modern traffic engineering construction, and has wide market prospect in the technical field of traffic safety engineering.
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Embodiments of the invention are described in further detail below with reference to the attached drawing figures, wherein:
FIG. 1 is a schematic step diagram of an embodiment of a method for determining confidence of measured values of an online wheel set dimension detection system according to the present invention;
FIG. 2 is a 1-9 scale chart of the present invention;
FIG. 3 is a schematic diagram of a confidence level evaluation of the measured values according to example 1;
FIG. 4 is a table showing the evaluation of the degree of detection in the example 1;
FIG. 5 is a table of the evaluation of the degree of reasonableness of the evaluation of the grades described in example 1;
fig. 6 is a table of rating evaluation of influence degree described in embodiment 1.
FIG. 7 is a table of evaluation ratings for factors affecting the detection of consistency uncertainty as described in example 1.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be embodied in many different forms without departing from the spirit or scope of the present invention, which should not be construed as limited to the specific embodiments set forth herein.
As shown in fig. 1 to fig. 2, the invention discloses a method for determining the confidence of the measured value of a wheel set dimension online detection system, comprising the following steps:
s1, determining a plurality of evaluation indexes of the measured value of the wheel set dimension online detection system;
in the step S1, the evaluation indexes include uncertainty of detection consistency, uncertainty of vehicle consistency, uncertainty of algorithm consistency, and the like, because various aspects affecting the measurement result of the on-line wheel set dimension detection system, such as system structure composition, analysis of measurement algorithm, and influence of vehicle on measurement, are considered comprehensively.
S2, dividing the determined evaluation indexes into a set of influence factors to determine the evaluation level of the confidence degree of the measured value;
in step S2, the determined evaluation indexes are further divided into sets of influencing factors, and the specific content includes:
the detection consistency uncertainty comprises sensor precision, sensor sampling frequency, adjacent measurement value difference degree and the like;
the uncertainty of the vehicle consistency comprises the parameter difference degree of coaxial wheel pairs, the parameter difference degree of wheel pairs with the same bogie, the parameter difference degree of wheel pairs with the same carriage and the like;
the uncertainty of the consistency of the algorithm comprises a centrifugal distance, a right end face inclination angle, an average group number and the like.
S3, researching different influence factors under each evaluation index, determining an evaluation factor capable of accurately evaluating each influence factor, and performing grade evaluation on each evaluation factor through fuzzy numbers, namely determining the corresponding relation between each evaluation factor evaluation grade linguistic variable and fuzzy language (specifically the fuzzy number corresponding to the evaluation grade linguistic variable);
in the above step S3, the blur number is selected as the trapezoidal blur number or the triangular blur number.
S4, calculating the evaluation weight of different evaluation factors under each evaluation index by adopting an analytic hierarchy process, wherein the specific method comprises the following steps:
s41, establishing an evaluation factor evaluation hierarchical structure model, wherein the top layer is an evaluation index, the middle layer is an influence factor corresponding to the evaluation index, and the bottom layer is an evaluation factor; let the middle layer have m influencing factors, which are respectively marked as e1,e2,...emIf the evaluation index corresponds to the influence factor set Ue＝{e1,e2,...em}; let the bottom layer have n said evaluation factors, which are respectively marked as u1,u2,...unThen the corresponding evaluation factor set is Uu＝{u1,u2,...un}；
S42, let aijDenotes eiTo ejRelative importance value of aijThe value is obtained by a 1-9 scale method; constructing a judgment matrix P of the evaluation index of the influencing factors according to the numerical scale in the scale 1-9 and the meaning represented by the numerical scale,
in the judgment matrix P of the influence factors on the evaluation indexes, aii＝1，Wherein i, j is 1, 2.. m;
s43, setting the consistency degree index of the judgment matrix P of the influence factors to the evaluation index as CR,
wherein,RI is corresponding consistency index and can be obtained by inquiring RI comparison table, lambdamaxRepresenting the maximum characteristic value of the judgment matrix P of the evaluation index by the influence factors;
the consistency of the influence factors on the judgment matrix P of the evaluation index is checked, and the specific method comprises the following steps:
when CR is less than or equal to 0.1, determining that the judgment matrix P of the evaluation index by the influencing factors meets the consistency requirement; if CR is larger than 0.1, readjusting the judgment matrix P of the influencing factors on the evaluation indexes until the consistency requirement is met.
S44, repeatedly executing the substep S42 and the substep S43, and respectively constructing a judgment matrix P of the evaluation factor to the influence factors1,P2,...,PmAnd respectively checking the consistency;
s45, calculating the eigenvector corresponding to the maximum eigenvalue of the judgment matrix P of the evaluation index by the influence factor, normalizing to obtain weight vector α, and calculating the judgment matrix P of the evaluation factor to the influence factor respectively1,P2,...,PmThe feature vector corresponding to the maximum feature value is normalized to β1,β2,...,βmThen the evaluation weight w of the evaluation factor is,
w＝(β1,β2,...,βm)·α
s5, comprehensively quantifying parameters of each evaluation index, wherein the specific method comprises the following steps:
comprehensively quantifying each evaluation index by using a fuzzy comprehensive evaluation method to obtain a fuzzy value of each evaluation index; and then defuzzifying each fuzzy value by adopting a gravity center method.
Wherein, the formula of the gravity center method is as follows:
s6, determining the confidence coefficient of the measured value of the wheel set dimension online detection system, wherein the specific method comprises the following steps:
and determining the weight of the parameter of each evaluation index by using an expert opinion method, and multiplying the weight by the determined quantized value of each evaluation index to finally obtain a specific numerical value of the confidence coefficient of the measured value.
Example 1
A set of wheel pair dimension on-line detection system installed on site is taken as a test object, and two sets of adjacent measurement data measured by the wheel pair dimension on-line detection system are selected.
Defining evaluation indexes of the confidence degree of a measured value of the wheel pair dimension online detection system, wherein the evaluation indexes comprise detection consistency uncertainty J, vehicle consistency uncertainty V and algorithm consistency uncertainty A; each evaluation index is further divided into a set of influencing factors to determine an evaluation hierarchy for the confidence of the measured values, see fig. 3.
After different influence factors under each evaluation index are comprehensively compared, the evaluation factors of each influence factor are determined to be the detection degree lambda (which refers to the degree that each influence factor can be detected), the reasonability degree tau (which refers to the degree that each influence factor value deviates from the expected or standard range), and the influence degree beta (which refers to the degree that each influence factor influences the confidence of the measured value), and then the evaluation factors are subjected to level evaluation by using a trapezoidal fuzzy number, which is specifically as follows:
(1) determining fuzzy grade division of the detection degree lambda, wherein a specific grade evaluation table is shown in figure 4;
(2) determining fuzzy grade division of the reasonableness degree tau, wherein a specific grade evaluation table is shown in figure 5;
(3) the specific rating evaluation table is shown in fig. 6 for determining the fuzzy rating scale of the fusing loudness β.
The evaluation weights of different evaluation factors under each evaluation index are calculated by adopting an analytic hierarchy process, and because the evaluation indexes are more, the following description only takes the detection consistency uncertainty J as an example, and the specific details are as follows:
firstly, constructing a judgment matrix P of the influence factors on the evaluation indexes:
② checking the consistency of the judgment matrix P of the influencing factors to the evaluation index lambdamax＝3.02， Therefore, the judgment matrix P for determining the influence factors to the evaluation index meets the consistency requirement.
③ constructing judgment matrix P of evaluation factor to influence factor1,P2,P3：
④ check the consistency of the evaluation factor to the judgment matrix of the influencing factor1 max＝3.02， Determining a matrix P1The consistency requirement is met; lambda [ alpha ]2 max＝3.05， Determining a matrix P2The consistency requirement is met; lambda [ alpha ]3 max＝3.05， Determining a matrix P3And the consistency requirement is met.
⑤ calculating the eigenvector corresponding to the maximum eigenvalue of the judgment matrix P of the influence factors on the evaluation index, and normalizing to obtain weight vector α (0.14,0.63,0.24)T(ii) a Respectively calculating judgment matrixes P of evaluation factors on influence factors1,P2,P3The feature vector corresponding to the maximum feature value is normalized to 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 calculating an evaluation weight w of the detection consistency uncertainty J1Is (0.2828,0.3408, 0.3864).
Repeating the substeps to obtain an evaluation weight w for the vehicle consistency uncertainty V2(0.40,0.34, 0.26); the evaluation weight of the uncertainty A of the algorithm consistency is w3＝(0.323,0.257,0.42)。
The evaluation indexes are comprehensively quantified by using a fuzzy comprehensive evaluation method, and the description is given by taking the detection consistency uncertainty J as an example according to one group of selected data in the two groups of data, specifically as follows:
first, the three influencing factors of the detection consistency uncertainty J are subjected to the grade evaluation according to the grade evaluation table of each evaluation factor, see fig. 7, and then:
① establishing an initial evaluation matrix F1：
② combines the previously obtained evaluation weights w for the detection consistency uncertainty J1Establishing a weighted normalized decision matrix V1：
Defuzzifying the fuzzy value by adopting a gravity center method, namely normalizing the decision matrix V by weighting according to a formula of the gravity center method1Performing defuzzification processing to obtain:
V1＝[0.846 0.736 0.474]
normalizing decision matrix V for weighting1The elements in the sequence are averaged to obtain a quantitative value Z of the detection consistency uncertainty J1Is 0.685.
Repeating the above substeps, one can obtain: quantized value Z of vehicle consistency uncertainty V2Is 0.627, the quantified value of the algorithm consistency uncertainty A, Z3Is 0.436.
The weights of the detection consistency uncertainty J, the vehicle consistency uncertainty V and the algorithm consistency uncertainty A are respectively determined to be 0.3, 0.35 and 0.35 by using an expert opinion method, the weights are multiplied by the quantized values of the evaluation indexes determined before, and finally the specific numerical value of the confidence coefficient of the measured value is 0.578.
Other parts of the method for determining the confidence of the measured value of the wheel set dimension online detection system in the embodiment are referred to in the prior art.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, so that any modification, equivalent change and modification made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.
Claims (9)
1. A method for determining the confidence of the measured value of an online wheel pair size detection system is characterized by comprising the following steps:
s1, determining a plurality of evaluation indexes of the measured value of the wheel set dimension online detection system;
s2, dividing the determined evaluation indexes into a set of influence factors so as to determine the evaluation level of the confidence degree of the measured value;
s3, determining evaluation factors capable of accurately evaluating the influence factors, and carrying out grade evaluation on the evaluation factors through fuzzy numbers;
s4, calculating the evaluation weight of different evaluation factors under each evaluation index by adopting an analytic hierarchy process;
s5, comprehensively quantifying parameters of the evaluation indexes;
and S6, determining the confidence coefficient of the measured value of the wheel set dimension online detection system.
2. The method for determining the confidence of the measured value of the wheel set dimension online detection system according to claim 1, is characterized in that: the evaluation indexes comprise detection consistency uncertainty, vehicle consistency uncertainty and algorithm consistency uncertainty.
3. The method for determining the confidence of the measured value of the wheel set dimension online detection system according to claim 2, wherein in step S2, the steps of dividing the determined evaluation indexes into a set of influencing factors specifically include:
the detection consistency uncertainty comprises sensor precision, sensor sampling frequency and adjacent measurement value difference degree;
the uncertainty of the vehicle consistency comprises the parameter difference degree of coaxial wheel pairs, the parameter difference degree of wheel pairs with the same bogie and the parameter difference degree of wheel pairs with the same carriage;
the uncertainty of the algorithm consistency comprises the centrifugal distance, the right end face inclination angle and the average group number.
4. The method for determining the confidence of the measured value of the wheel set dimension online detection system according to claim 1, is characterized in that: in step S3, the blur number is a trapezoidal blur number or a triangular blur number.
5. The method for determining the confidence of the measured value of the wheel set dimension online detection system according to claim 1, wherein in step S4, the specific method for calculating the evaluation weight of different evaluation factors under each evaluation index by using an analytic hierarchy process comprises:
s41, establishing an evaluation factor evaluation hierarchical structure model, wherein the top layer is the evaluation index, the middle layer is the influence factor corresponding to the evaluation index, and the bottom layer is the evaluation factor; let the middle layer have m influencing factors, which are respectively marked as e1,e2,...emIf the evaluation index corresponds to the influence factor set Ue＝{e1,e2,...em}; let the bottom layer have n said evaluation factors, which are respectively marked as u1,u2,...unThen the corresponding evaluation factor set is Uu＝{u1,u2,...un}；
S42, let aijDenotes eiTo ejRelative importance value of aijThe value is obtained by a 1-9 scale method; constructing a judgment matrix P of the evaluation index of the influencing factors according to the numerical scale in the scale 1-9 and the meaning represented by the numerical scale,
<mrow> <mi>P</mi> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>a</mi> <mn>11</mn> </msub> </mtd> <mtd> <msub> <mi>a</mi> <mn>12</mn> </msub> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <msub> <mi>a</mi> <mrow> <mn>1</mn> <mi>m</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>a</mi> <mn>21</mn> </msub> </mtd> <mtd> <msub> <mi>a</mi> <mn>22</mn> </msub> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <msub> <mi>a</mi> <mrow> <mn>2</mn> <mi>m</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <mo>...</mo> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <mo>...</mo> </mtd> </mtr> <mtr> <mtd> <msub> <mi>a</mi> <mn>41</mn> </msub> </mtd> <mtd> <msub> <mi>a</mi> <mn>42</mn> </msub> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <msub> <mi>a</mi> <mrow> <mi>m</mi> <mi>m</mi> </mrow> </msub> </mtd> </mtr> </mtable> </mfenced> </mrow>
in the judgment matrix P of the influence factors on the evaluation indexes, aii＝1，Wherein i, j is 1, 2.. m;
s43, setting the consistency degree index of the judgment matrix P of the influence factors to the evaluation index as CR,
<mrow> <mi>C</mi> <mi>R</mi> <mo>=</mo> <mfrac> <mrow> <mi>C</mi> <mi>I</mi> </mrow> <mrow> <mi>R</mi> <mi>I</mi> </mrow> </mfrac> </mrow>1
wherein,RI is corresponding consistency index and can be obtained by inquiring RI comparison table, lambdamaxRepresenting the maximum characteristic value of the judgment matrix P of the evaluation index by the influence factors;
and (5) checking the consistency of the influencing factors on the judgment matrix P of the evaluation index.
S44, repeatedly executing the substep S42 and the substep S43, and respectively constructing a judgment matrix P of the evaluation factor to the influence factors1,P2,...,PmAnd separately testing one of themCausing sex;
s45, calculating the eigenvector corresponding to the maximum eigenvalue of the judgment matrix P of the evaluation index by the influence factor, normalizing to obtain weight vector α, and calculating the judgment matrix P of the evaluation factor to the influence factor respectively1,P2,...,PmThe feature vector corresponding to the maximum feature value is normalized to β1,β2,...,βmThen the evaluation weight w of the evaluation factor is,
w＝(β1,β2,...,βm)·α
6. the method for determining the confidence of the measured value of the wheel set dimension online detection system according to claim 5, wherein in the step S43, the specific method for checking the consistency of the influencing factors on the judgment matrix P of the evaluation index comprises the following steps:
when CR is less than or equal to 0.1, determining that the judgment matrix P of the evaluation index by the influencing factors meets the consistency requirement; if CR is larger than 0.1, readjusting the judgment matrix P of the influencing factors on the evaluation indexes until the consistency requirement is met.
7. The method for determining the confidence of the measured value of the wheel set dimension online detection system according to claim 1, wherein the specific method for comprehensively quantifying the parameters of each evaluation index comprises the following steps:
comprehensively quantifying each evaluation index by using a fuzzy comprehensive evaluation method to obtain a fuzzy value of each evaluation index; and then, defuzzifying each fuzzy value by adopting a gravity center method.
8. The method for determining the confidence of the measured value of the wheel set dimension online detection system according to claim 7, wherein the formula of the gravity center method is as follows:
<mrow> <msub> <mover> <mi>x</mi> <mo>~</mo> </mover> <mn>0</mn> </msub> <mrow> <mo>(</mo> <mover> <mi>A</mi> <mo>~</mo> </mover> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mo>&Integral;</mo> <msub> <mi>x&mu;</mi> <mover> <mi>A</mi> <mo>~</mo> </mover> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mi>d</mi> <mi>x</mi> </mrow> <mrow> <mo>&Integral;</mo> <msub> <mi>&mu;</mi> <mover> <mi>A</mi> <mo>~</mo> </mover> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mi>d</mi> <mi>x</mi> </mrow> </mfrac> </mrow>
9. the method for determining the confidence level of the measured value of the wheel set size online detection system according to the claim 1, wherein in the step S6, the specific method for determining the confidence level of the measured value of the wheel set size online detection system comprises the following steps:
and determining the weight of the parameter of each evaluation index by using an expert opinion method, and multiplying the weight by the determined quantized value of each evaluation index to finally obtain a specific numerical value of the confidence coefficient of the measured value.
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