CN111563693A - Method and device for scoring health value of rail transit equipment and storage medium - Google Patents

Method and device for scoring health value of rail transit equipment and storage medium Download PDF

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CN111563693A
CN111563693A CN202010428574.4A CN202010428574A CN111563693A CN 111563693 A CN111563693 A CN 111563693A CN 202010428574 A CN202010428574 A CN 202010428574A CN 111563693 A CN111563693 A CN 111563693A
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CN111563693B (en
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薛万彪
陈翱
洪诗意
李宋雄
居天云
王秦斯
赵宝军
杨赞
于洁
邱明明
薛新峰
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Shenzhen Das Intellitech Co Ltd
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Abstract

The invention discloses a method, equipment and a storage medium for scoring a health value of rail transit equipment, wherein the method comprises the following steps: constructing an equipment health level analysis model, and determining N first factors influencing equipment health and M second factors corresponding to each first factor; determining the influence weight of each second factor on the health of the equipment, and calculating the health value of the equipment according to the individual scores and the influence weights of the second factors. Compared with the prior art, the method for scoring the health value of the rail transit equipment classifies the factors influencing the health of the equipment according to the positive and negative influences and the influence degree, models the calculation of the health value of the rail transit equipment by combining an analytic hierarchy process, finally obtains a calculation model of the health value of the equipment, and calculates the health value of the equipment according to the model. Therefore, the equipment is maintained and managed through the health value of the equipment.

Description

Method and device for scoring health value of rail transit equipment and storage medium
Technical Field
The invention relates to the technical field of traffic data processing, in particular to a method and equipment for scoring a health value of rail transit equipment and a storage medium.
Background
The urban rail transit is the backbone of urban public transport, has the characteristics of energy conservation, land conservation, large transportation volume, all weather, no pollution (or little pollution), safety and the like, belongs to a green environment-friendly transportation system, and is particularly suitable for large and medium-sized cities. With the rapid development of the rail transit industry in recent years, the devices governed by the rail transit are more and more huge, so that the management of the rail transit devices is more and more complicated.
The management of the equipment depends on the health degree of the equipment, and how to scientifically and systematically evaluate the health degree of the equipment is a problem to be solved urgently.
Disclosure of Invention
The invention aims to provide a method, equipment and a storage medium for scoring a health value of rail transit equipment.
In order to achieve one of the above objects, an embodiment of the present invention provides a method for scoring a health value of a rail transit device, where the method includes:
constructing a hierarchical analysis model of equipment health, classifying factors influencing the equipment health according to the negative influence degree on the equipment health, and determining N first factors influencing the equipment health and M second factors corresponding to each first factor;
respectively constructing N +1 judgment matrixes according to the important influence degree of the first factor on the equipment health and the important influence degree of each second factor on the corresponding first factor, and performing consistency check on each judgment matrix;
after each judgment matrix passes consistency check, obtaining the influence weight of each second factor on the equipment health according to the N +1 maximum eigenvectors of the N +1 judgment matrices;
and according to the individual scores and the influence weights of the second factors, calculating the health value S1 of the equipment, namely multiplying the individual scores of all the second factors by the corresponding influence weights and then summing the scores.
As a further improvement of an embodiment of the invention, the factors affecting the health of the device further include local optimization factors and global optimization factors with positive effects, and the method further includes:
and calculating the health value S of the equipment according to the score S2 of the local optimization factor and the score S3 of the overall optimization factor and combining the score S1, wherein if the sum of the score S1 and the score S2 is less than a full score, the health value S of the equipment is max { S1+ S2, S3}, and otherwise, the health value S of the equipment is the full score.
As a further improvement of an embodiment of the present invention, the calculating the score of the local optimization factor S2 and the score of the global optimization factor S3 specifically includes:
establishing a grading standard of the local optimization factors and the overall optimization factors, and calculating the grading S2 of the local optimization factors and the grading S3 of the overall optimization factors according to actual data of equipment; wherein the content of the first and second substances,
the scoring standard of the local optimization factors is as follows:
dividing the replaced parts into 4 grades according to the importance degree of the equipment, wherein the grades comprise {0,1,2 and 3}, and the corresponding scores of S2 are {0,10,20 and 30} in turn;
the scoring standard of the overall optimization factors is as follows:
and dividing the equipment overhaul degree into 5 grades including {0,1,2,3 and 4} grades, wherein the corresponding scores of the S3 are {0,70,80,90 and 95} grades in sequence.
As a further improvement of an embodiment of the present invention, the first factor affecting the health of the equipment includes an operating condition and a fault condition, which refer to operating data and fault data, respectively, occurring over a period of time that affect the health of the equipment.
As a further improvement of an embodiment of the present invention, the second factor corresponding to the operation condition includes operation use time, load condition, abnormal operation time, abnormal operation frequency and abnormal operation ratio;
and the second factor corresponding to the fault condition comprises a fault type, fault times, fault frequency, fault duration and maintenance recovery degree.
As a further improvement of an embodiment of the present invention, calculating the individual score of each of the second factors specifically includes:
and establishing a grading standard of each second factor, and individually grading each specific factor according to the actual data of the operation condition and the fault condition of the equipment.
As a further improvement of an embodiment of the present invention, acquiring "actual data of the operating condition and the fault condition of the device" specifically includes:
respectively establishing normal ranges of voltage, current and vibration frequency for equipment, monitoring the voltage, current and vibration frequency of the equipment in real time, carrying out early warning when the voltage, current or vibration frequency exceeds the corresponding normal ranges, and recording early warning information;
obtaining actual data of the operation condition of the equipment by obtaining current operation data and historical early warning information of the equipment;
after the equipment is subjected to fault maintenance, recording corresponding fault information and maintenance information;
and acquiring the historical fault information and the historical maintenance information of the equipment to obtain the actual data of the fault condition of the equipment.
As a further improvement of an embodiment of the present invention, the method further comprises:
and constructing N +1 judgment matrixes which pass consistency check at least twice to obtain at least two characteristic vectors of the total hierarchical ordering, and selecting the characteristic vector of the highest hierarchical ordering with random consistency as a vector formed by the influence weight of the second factor on the health of the equipment by calculating the random consistency ratio of the total hierarchical ordering, wherein the characteristic vector of the total hierarchical ordering is the vector formed by the influence weight of each second factor on the health of the equipment.
In order to achieve one of the above objects, an embodiment of the present invention provides an electronic device, which includes a memory and a processor, where the memory stores a computer program operable on the processor, and the processor implements the steps in the method for scoring the health value of the rail transit equipment when executing the program.
In order to achieve one of the above objects, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the method for scoring the health value of the rail transit equipment.
Compared with the prior art, the method for scoring the health value of the rail transit equipment classifies the factors influencing the health of the equipment according to the positive and negative influences and the influence degree, models the calculation of the health value of the rail transit equipment by combining an analytic hierarchy process, finally obtains a calculation model of the health value of the equipment, and calculates the health value of the equipment according to the model. Therefore, the equipment is maintained and managed through the health value of the equipment. For example, managers and station operation and maintenance personnel can transversely compare health values to know the health condition of the equipment, so that targeted maintenance work can be carried out. And active maintenance of equipment can be realized according to health prediction data, and the operation and maintenance efficiency of the whole station is improved.
Drawings
Fig. 1 is a schematic flow chart of a method for scoring a health value of a rail transit device according to the present invention.
FIG. 2 is a schematic structural diagram of an equipment health level analysis model of the present invention.
Fig. 3 is a schematic structural diagram of the rail transit intelligent operation and maintenance system of the present invention.
Fig. 4 is a schematic diagram of a system monitoring module of the rail transit intelligent operation and maintenance system according to the present invention.
Fig. 5 is another schematic diagram of the system monitoring module of the rail transit intelligent operation and maintenance system according to the present invention.
Fig. 6 is a schematic diagram of a wire network monitoring module of the intelligent operation and maintenance system for rail transit according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to specific embodiments shown in the drawings. These embodiments are not intended to limit the present invention, and structural, methodological, or functional changes made by those skilled in the art according to these embodiments are included in the scope of the present invention.
As shown in fig. 1, the invention provides a method for scoring a health value of rail transit equipment, which calculates a health value of each piece of rail transit equipment by establishing an equipment health value calculation model. The primary task of establishing the equipment health value calculation model is to determine all factors influencing the equipment health condition according to the historical data of the equipment and analyze the weight of all factors in the equipment health score. The invention adopts an Analytic Hierarchy Process (AHP), arranges all factors influencing the health condition of the equipment from top to bottom according to the sequence from total to minutes, and obtains the influence weight of all factors in the model on the health of the equipment through the importance comparison and the analytical calculation between every two elements. The method comprises the following steps:
step S110: and constructing a hierarchical analysis model of equipment health, classifying the factors influencing the equipment health according to the negative influence degree on the equipment health, and determining N first factors influencing the equipment health and M second factors corresponding to each first factor.
The first factor may also be referred to as a primary factor and the second factor may also be referred to as a specific factor. It should be noted that, these factors are classified into N first factors according to the degree of negative influence on the equipment health, so that more accurate and scientific influence weight of each second factor on the equipment health can be obtained, and meanwhile, when the second factors are more, the whole calculation process is greatly simplified.
As shown in fig. 2, it is preferable that the main factors include an operating condition X1 having a slight negative influence on the equipment and a fault condition X2 having a severe negative influence on the equipment, and the operating condition X1 and the fault condition X2 refer to operating data and fault data that occur over a period of time and affect the health of the equipment, respectively.
Further, the specific factors corresponding to the operation condition include operation use time, load condition, abnormal operation time, abnormal operation times and abnormal operation ratio. The specific factors corresponding to the fault condition comprise fault type, fault frequency, fault duration and maintenance recovery degree.
The actual data of the above-mentioned main factors can be obtained as follows:
step S111: the method comprises the steps of setting normal ranges of voltage, current and vibration frequency for equipment respectively, monitoring the voltage, the current and the vibration frequency of the equipment in real time, carrying out early warning when the voltage, the current or the vibration frequency exceeds the corresponding normal ranges, and recording early warning information.
Step S112: actual data of the operation condition of the equipment is obtained by obtaining current operation data and historical early warning information of the equipment.
Step S113: and after the equipment is subjected to fault maintenance, recording corresponding fault information and maintenance information.
Step S114: and acquiring the historical fault information and the historical maintenance information of the equipment to obtain the actual data of the fault condition of the equipment.
Step S120: and respectively constructing N +1 judgment matrixes according to the important influence degree of the first factor on the equipment health and the important influence degree of each second factor on the corresponding first factor, and carrying out consistency check on each judgment matrix.
The important influence degree is the weight, when determining the weight among the factors of each layer, all the factors are not put together for comparison, but are compared with each other pairwise, so as to reduce the difficulty of comparing the factors with different properties as much as possible, and improve the accuracy.
Factor i to factor j Quantized value
Of equal importance 1
Of slight importance 3
Of greater importance 5
Of strong importance 7
Of extreme importance 9
Intermediate values of two adjacent judgments 2，4，6，8
TABLE 1
After a large amount of research is carried out according to historical data, the relative weight value of the first factors of the middle layer to the health of the highest-layer equipment is determined, and a judgment matrix of a criterion layer is constructed.
Similarly, N judgment matrixes of the bottommost layer are respectively constructed according to the relative weight values of the second factors of the bottommost layer to the first factors of the middle layer. A total of N +1 decision matrices are obtained.
In a specific embodiment, the first factors include an operating condition and a fault condition, a relative weight value to a highest layer between the first factors of the middle layer is determined, and a judgment matrix B is constructed as follows:
Figure BDA0002499638550000061
according to the relative weight values of the second factors at the bottom layer to the first factors at the middle layer, judgment matrixes B1 and B2 are respectively constructed, and the following steps are performed:
Figure BDA0002499638550000062
Figure BDA0002499638550000071
for each judgment matrix, the random consistency ratio needs to be calculated, and if the random consistency ratio is less than 0.1, the judgment matrix is reasonable in structure and can be used for calculating the weight. The process of calculating the random consistency ratio is as follows:
(1) calculating the maximum eigenvector of each judgment matrix by using sum-product method
Each value in the maximum feature vector can be used as a basis for calculating the influence weight of specific factors on the health of the equipment.
(2) Obtaining maximum feature root lambda from maximum feature vectormax
If the maximum eigenvector of the n-order matrix E is ω ═ ω (ω ═ ω)1,ω2,...,ωn)TLet X be E × ω (X)1,x2,...,xn)TThen the maximum feature root
Figure BDA0002499638550000072
(3) And calculating a matrix consistency index C.I.
Figure BDA0002499638550000073
The larger the value of c.i. is, the larger the degree of deviation of the judgment matrix from the full consistency is, and otherwise, the closer the judgment matrix is to the full consistency.
(4) A random consistency ratio c.r is calculated.
Figure BDA0002499638550000074
Wherein, the value of R.I. is related to the order n, and the specific values are shown in the following table 2:
n 1 2 3 4 5 6 7 8 9 10
RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49
n 11 12 13 14 15
RI 1.51 1.48 1.56 1.57 1.59
TABLE 2
As can be seen from table 2, when n is 1 and 2, the value of r.i. is 0, and the denominator value is not allowed to be 0, since the 1 st and 2 nd matrices themselves have consistency, it is not necessary to check the consistency of the 1 st and 2 nd matrices, and it is only necessary to check the consistency of the 3 rd or more matrices.
Step S130: and after each judgment matrix passes consistency check, obtaining the influence weight of each second factor on the equipment health according to the N +1 maximum eigenvectors of the N +1 judgment matrices.
And the vector formed by the influence weight of each second factor on the health of the equipment is the total hierarchical ordering. And synthesizing the weights of the influencing factors in the hierarchical analysis model from top to bottom to obtain the hierarchical total ordering of the second factor at the bottommost layer. That is, assume a hierarchical ordering of criterion layers of (b)1，b2)TThe hierarchical ordering of the bottommost layer relative to the criterion layer is (c)1，c2，c3，c4，c5)TAnd (c)6，c7，c8，c9，c10)TThen the overall hierarchical ordering is:
wc＝(b1c1，b1c2，b1c3，b1c4，b1c5，b2C6，b2c7，b2c8，b2c9，b2。
then, the consistency check is carried out on the total hierarchical ordering, and the hierarchical ordering of the obtained criterion layer is (b)1，b2)TAnd a judgment matrix B1And B2Consistency index of (A)1) And C.I. (B)2) To calculate the overall hierarchical ordering, i.e., the random consistency ratio of the bottom-most layer to the target layer:
Figure BDA0002499638550000081
it should be noted that, if all the decision matrices pass the consistency check, the consistency check of the hierarchical total sorting must pass. The step of consistency checking of the overall ranking can be omitted. The significance of the consistency check for the overall ranking is to measure the data consistency of the entire scheme from the whole, similar to an average. Therefore, in a preferred embodiment, N +1 judgment matrices passing through consistency check may be constructed multiple times to obtain at least two feature vectors of total hierarchical ranking, and a feature vector of the highest hierarchical ranking with random consistency (the highest random consistency, that is, the smallest value of the random consistency ratio) is selected as a vector constituted by the influence weight of the second factor on the health of the device by calculating the random consistency ratio of the total hierarchical ranking.
Step S140: and according to the individual scores and the influence weights of the second factors, calculating the health value S1 of the equipment, namely multiplying the individual scores of all the second factors by the corresponding influence weights and then summing the scores.
Assuming that the second factors C1-C10 are scored as Sc 1-Sc 10, Sc ═ Sc (Sc1, Sc2,. Sc10), Wc is a vector formed by the influence weights of the second factors on the health of the equipment,
wc＝(b1c1，b1c2，b1c3，b1c4，b1c5，b2c6，b2c7，b2c8，b2c9，b2and then, the equipment health value calculation model S-S1-Sc-Wc, wherein the equipment health value is a score calculated according to the calculation model.
It should be noted that, when calculating the individual scores of the second factors, a scoring standard needs to be established for each second factor, and then the individual score of each second factor is calculated according to the acquired actual data.
In a specific embodiment, the scoring criteria for each second factor is established as follows.
(1) The operation service time is as follows: from the past historical data, the longer the operating and using time of the equipment is, the worse the health condition of the equipment is, and the two are in inverse proportion relation. The scoring criteria for establishing the operational lifetime of the device are shown in table 3.
Figure BDA0002499638550000091
TABLE 3
(2) Load conditions are as follows: when the actual load of the equipment is the same as the rated load, the most efficient operation effect can be achieved; if the load is lower than the rated load, the starting is difficult and the working efficiency is low; if the load is higher than the rated load, the equipment may be abnormally heated due to the increase of the current, and even the machine may be burnt. Therefore, the load condition can also influence the health condition of the equipment, and when the actual load is not equal to the rated load, the equipment is in an abnormal operation condition and is required to be overhauled in time. Wherein, W is actual power and W is rated power.
Figure BDA0002499638550000092
TABLE 4
(3) Abnormal operation time: when the equipment performs abnormal operation, the system should record the time of the abnormal operation, and the scoring standard of the abnormal operation time is shown in table 5.
Figure BDA0002499638550000101
TABLE 5
(4) The abnormal operation times are as follows: when the number of abnormal operation times of the equipment is too large, the equipment should be also emphasized, and the scoring standard of the abnormal operation times is shown in table 6.
Figure BDA0002499638550000102
TABLE 6
(5) Abnormal operation ratio: the abnormal operation ratio is the number of abnormal operations within one quarter, and different from the number of abnormal operations, the number of abnormal operations reflects the total number, and the abnormal operation ratio reflects the frequency, and the scoring criteria thereof are shown in table 7.
Figure BDA0002499638550000103
TABLE 7
(6) The type of failure: according to the influence of the fault on the operation of the machine, the fault can be divided into several grades, wherein the fault is divided into 6 grades from 0 to 5, the higher the grade is, the larger the influence of the fault on the operation of the machine is, namely, the influence of the fault at the 0 grade on the operation of the machine is the smallest, and the influence of the fault at the 5 grade on the operation of the machine is the largest.
Figure BDA0002499638550000104
TABLE 8
(7) The failure times are as follows: the failure frequency is one of the factors of the failure condition branches, and compared with the abnormal operation frequency, the failure frequency has larger influence on the health condition of the equipment, so that every time a failure occurs, attention of maintenance personnel should be paid, and a series of measures such as replacing parts or overhauling the equipment are carried out in time. The scoring criteria for the number of abnormal runs are shown in table 9.
Figure BDA0002499638550000111
TABLE 9
(8) Failure frequency: the failure frequency is the number of failures occurring within one quarter, and the scoring criteria are shown in table 10.
Figure BDA0002499638550000112
Watch 10
(9) The fault duration is as follows: in order to make the maintenance personnel know the fault in time, the fault duration is also used as a factor for evaluating the health model of the equipment, and the longer the fault duration is, the lower the score is, and the more the attention can be easily paid. Evaluation criteria for the length of the failure time are shown in table 11.
Figure BDA0002499638550000113
TABLE 11
(10) And (3) maintenance recovery degree: when a fault occurs, a maintenance engineer can repair the equipment, and the repair recovery degree can affect the subsequent operation condition of the equipment. The scoring criteria for this factor are shown in table 12.
Figure BDA0002499638550000114
TABLE 12
In a preferred embodiment, the factors affecting the health of the device further comprise local optimization factors and global optimization factors with positive effects, and the health value S of the device is calculated according to the score S2 of the local optimization factors and the score S3 of the global optimization factors, in combination with the score S1. Since the score of the local optimization factor C11 is added based on S1, if the score exceeds the full score (the full score may be one hundred), the health value of the equipment is full score. The overall optimization factor C12 is to directly score the whole equipment from the maintenance perspective, and if the current score (i.e. S1+ S2) of the equipment is higher than the score after maintenance (S3), the score is based on the current score, and if the score is lower than the score after maintenance, the score after maintenance is based on the score after maintenance.
Therefore, the equipment health value calculation model is S:
if the sum of the S1 and the S2 is less than the full score, S is max { S1+ S2, S3}, otherwise S is the full score.
And obtaining the health value score through the equipment health value calculation model, namely the health value of the equipment.
It should be noted that, when calculating the individual scores of the local optimization factors and the global optimization factors, a scoring standard needs to be established for the local optimization factors and the global optimization factors, and then the individual scores of the local optimization factors and the global optimization factors are calculated according to the acquired actual data.
Further, the scoring criteria for the local optimization factors and the global optimization factors are established as follows.
(1) Local optimization factors are as follows: the local optimization factor is a replacement component, and the replacement component can add points to the health score of the equipment in addition to the deduction items of the abnormal conditions. Meanwhile, the replaced parts are classified into 4 grades, the higher the replacement grade is, the higher the adding degree of the parts to the equipment is, and the grade 0 represents the non-replaced parts. Note that the score is added based on the overall score, and if the score exceeds 100, the score is counted as 100. The scoring criteria for this factor are shown in table 13.
Figure BDA0002499638550000121
Watch 13
(2) Overall optimization factors: the overall optimization factors are equipment maintenance: the equipment maintenance is one of the optimization factors and is also an addendum factor. The equipment maintenance is divided into 5 grades, the higher the grade is, the larger the representative maintenance degree is, the higher the grade is, the 0 grade represents that the equipment maintenance is not carried out, and the 4 grade represents the whole machine maintenance. This score directly changes the whole score of equipment, if the equipment is present to be graded after having been higher than the maintenance, then according to present score as accurate, if be less than the score after the maintenance, then according to the score after the maintenance as accurate.
Figure BDA0002499638550000131
TABLE 14
In one embodiment of the method for scoring the health value of the rail transit equipment, the method comprises the following steps:
step S210: and establishing an equipment health level analysis model.
Fig. 2 is a schematic structural diagram of an equipment health level analysis model, as shown in fig. 2, which divides a plurality of influencing factors related to equipment health into 3 levels. Wherein, the highest layer is a target layer, namely equipment health A; the middle layer is a criterion layer and represents main influence factors (main factors for short) for evaluating the health value of the equipment, historical operating data of a large number of electromechanical equipment is analyzed and summarized, and the factors influencing the health of the equipment are classified according to the influence degree on the health of the equipment by combining opinions and suggestions of equipment maintenance engineers with sophisticated experience, namely, the criterion layer is divided into an operating condition X1 and a fault condition X2 with different negative influence degrees on the health of the equipment, and an optimization condition X3 with positive influence factors on the health of the equipment; the bottom layer is the specific influence factor (specific factor for short) corresponding to each main factor.
Since both the operational and fault conditions of the equipment have a negative impact on the health of the equipment, optimization improves the health of the equipment. Therefore, the factor having a negative influence and the factor having a positive influence are evaluated separately.
As shown in fig. 2, it should be noted that the specific factors corresponding to the operation condition X1 include an operation use time C1, a load condition C2, an abnormal operation time C3, an abnormal operation frequency C4, and an abnormal operation ratio C5. The specific factors corresponding to the fault condition X2 include a fault type C6, a fault frequency C7, a fault frequency C8, a fault duration C9 and a maintenance recovery degree C10. The specific factors corresponding to the optimization condition X3 comprise a local optimization factor C11 and an overall optimization factor C12. The specific factors corresponding to the main factors can be modified according to actual conditions.
Step S220: decision matrices B, B1 and B2 are constructed.
After a large amount of research is carried out according to historical data, the relative weight value of factors X1 and X2 in the middle layer to the highest layer A is determined, and is shown as a matrix B:
Figure BDA0002499638550000141
similarly, according to the relative weight values of the factors X1 and X2 corresponding to the middle layer among the specific factors at the bottom layer, judgment matrixes B1 and B2 are respectively constructed as follows:
Figure BDA0002499638550000142
Figure BDA0002499638550000143
step S230: calculating the random consistency ratio and the maximum eigenvector of each judgment matrix
And calculating the maximum eigenvectors WB, WB1 and WB2 of each judgment matrix by using a sum-product method as follows:
ωB＝(0.167,0.833)T
Figure BDA0002499638550000144
Figure BDA0002499638550000145
each value in the maximum feature vector can be used as a basis for calculating the influence weight of specific factors on the health of the equipment.
Since matrix B is a 2 nd order matrix, matrix B passes the consistency check.
The random consistency ratios of the judgment matrixes B1 and B2 calculated according to the steps are respectively as follows:
C.R.(B1)＝0.0089＜0.1，
C.R.(B2)＝0.0089＜0.1，
it follows that decision matrices B1 and B2 both pass the consistency check.
Step S240: calculating the total rank of the hierarchy, i.e. the vector formed by the weight of the influence of each specific factor on the health of the equipment
According to WB, WB1 and WB2, calculating the total hierarchical ranking of the bottommost layer relative to the topmost layer, which is a vector formed by the influence weight of each specific factor on the health of the equipment, as follows:
wc＝(0.01052，0.02756，0.0738，0.2756，0.2756，0.05245，0.137４5，0.3682，0.13754,0.13754)T
then, the consistency check is carried out on the total hierarchical ordering, and the hierarchical ordering of the obtained criterion layer is (b)1，b2)TAnd a judgment matrix B1And B2Consistency index of (A)1) And C.I. (B)2) To calculate the overall hierarchical ordering, i.e., the random consistency ratio of the bottom-most layer to the target layer:
Figure BDA0002499638550000151
it should be noted that, if all the decision matrices pass the consistency check, the consistency check of the hierarchical total sorting must pass. The step of consistency checking of the overall ranking can be omitted.
Step S250: establishing a grading standard of each specific factor
The scoring criteria for C1-C12 can be found in the above paragraphs.
Step S160: determining a device health value calculation model
Actual data of specific factors C1-C10 are acquired, scores Sc 1-Sc 10 of C1-C10 are respectively acquired according to the scoring standards, and assuming that Sc is equal to (Sc1, Sc 2.., Sc10), an overall score S1 of two main factors, namely an operation condition B1 and a fault condition B2, is calculated according to a vector Wc formed by influence weights of the specific factors on equipment health.
And acquiring actual data of the local optimization factor C11 and the overall optimization factor C12, and respectively obtaining scores S2 and S3 of C11 and C12 according to the scoring standards. Therefore, the equipment health value calculation model is S:
if the sum of the S1 and the S2 is less than the full score, S is max { S1+ S2, S3}, otherwise S is the full score.
And obtaining the health value score through the equipment health value calculation model, namely the health value of the equipment.
According to the method for scoring the health value of the rail transit equipment, the calculation of the health value of the rail transit equipment is modeled by combining the running condition, the fault condition and the optimization condition with an analytic hierarchy process, a calculation model of the health value of the equipment is finally obtained, and the health value of the equipment is calculated according to the model. Therefore, the equipment is maintained and managed through the health value of the equipment. For example, managers and station operation and maintenance personnel can transversely compare health values to know the health condition of the equipment, so that targeted maintenance work can be carried out. And active maintenance of equipment can be realized according to health prediction data, and the operation and maintenance efficiency of the whole station is improved.
The invention further provides an electronic device, which comprises a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor executes the program to realize any one step of the method for scoring the health value of the rail transit equipment, namely, to realize the step of any one technical scheme of the method for scoring the health value of the rail transit equipment.
The invention also provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements any one of the steps in the above-described method for scoring the health value of the rail transit equipment, that is, implements the steps in any one of the above-described methods for scoring the health value of the rail transit equipment.
As shown in fig. 3, the invention further provides an intelligent operation and maintenance system for rail transit, which establishes an equipment health value calculation model according to historical data of rail transit equipment, calculates health values of each piece of rail transit equipment, and manages and displays the equipment according to the health values of the equipment. The system includes a data acquisition subsystem 10, a data processing subsystem 20, and a device management subsystem 30.
The data acquisition subsystem 10 is used for acquiring operation data and fault maintenance data of the rail transit equipment, wherein the operation data comprises operation state, operation time, voltage data, current data and vibration data. Preferably, the data acquisition subsystem 10 includes an acquisition module 11 and a cleaning module 12, where the acquisition module 10 is configured to acquire operation data and maintenance data of the rail transit equipment, and the cleaning module 12 is configured to clean garbage data in the operation data and the maintenance data.
The data processing subsystem 20 is configured to calculate the equipment health value of each piece of equipment using the equipment health value calculation model 31 established 30 by the equipment management subsystem based on the operation data and the repair failure data.
Specifically, the data processing subsystem includes an early warning module 21 and a calculation module 22. The early warning module 21 is configured to perform early warning when the voltage data, the current data, or the vibration data exceeds a normal range, and record early warning information of a corresponding device. The calculation module 22 is configured to calculate the equipment health value of each piece of equipment using the equipment health value calculation model according to the operating state, the operating time, the warning information, and the maintenance data.
Preferably, the data required to be used in the device health value calculation model includes: the operation service time, the load condition, the abnormal operation time, the abnormal operation times and the abnormal operation ratio of the equipment, and the historical fault type, the fault times, the fault frequency, the fault duration and the maintenance recovery degree of the equipment. The early warning module 21 obtains data required to be used in the equipment health value calculation model through the running state, the running time, the early warning information and the fault maintenance data.
Further, the early warning module 21 is further configured to: and determining normal ranges of voltage data, current data and vibration data of different types of equipment according to factory specifications of the equipment.
The equipment management subsystem 30 is configured to establish an equipment health value calculation model using an analytic hierarchy process, and manage the equipment according to the equipment health value calculated by the data processing subsystem.
Specifically, the equipment management subsystem 30 includes a model building module 31 for building an equipment health value calculation model, where the model building module 31 uses any one of the above-mentioned scoring methods for the rail transit equipment health value to build the equipment health value calculation model, and the steps may include the following steps:
constructing a hierarchical analysis model of equipment health, classifying factors influencing the equipment health according to the influence degree on the equipment health, and determining main factors influencing the equipment health and specific factors corresponding to each main factor, wherein the main factors comprise an operation condition, a fault condition and an optimization condition;
calculating the influence weight of the specific factors corresponding to the operation condition and the fault condition on the equipment health by using an analytic hierarchy process, and calculating the sum of the scores of the specific factors corresponding to the operation condition and the fault condition by combining the score of each specific factor S1;
the optimization condition comprises a local optimization factor and an overall optimization factor, and a health value calculation model S of the equipment is obtained according to the score S2 of the local optimization factor and the score S3 of the overall optimization factor in combination with the score S1:
if the sum of S1 and S2 is less than full score, the equipment health value calculation model S is max { S1+ S2, S3}, otherwise, the equipment health value calculation model S is equal to full score.
Further, the specific factors corresponding to the operation condition include operation service time, load condition, abnormal operation time, abnormal operation times and abnormal operation ratio; the specific factors corresponding to the fault condition comprise fault type, fault frequency, fault duration and maintenance recovery degree.
Further, the model building module 31 is further configured to build a scoring criterion for each of the specific factors, the local optimization factors, and the global optimization factors. The data processing subsystem 20 is further configured to calculate individual scores for each specific factor, local optimization factor, and global optimization factor based on the operational data and the troubleshooting data, in combination with the scoring criteria.
Preferably, the equipment management subsystem 30 further includes a work order management module 32, which is configured to automatically generate a fault maintenance work order when the health value of the equipment is lower than a predetermined threshold value, notify related personnel to maintain or repair the equipment, avoid repairing the equipment after the equipment fails, reduce potential safety hazards, and greatly reduce the probability that the whole rail transit cannot run due to the equipment failure. Preferably, the equipment management subsystem 30 further comprises a system monitoring module 33 and a wire network monitoring module 34 for real-time monitoring and display.
The system monitoring module 33 is configured to divide the devices in the entire operation and maintenance system into a plurality of device systems by type, monitor each device system, and calculate a comprehensive health value of each device system according to the health value of the devices in each device system. As shown in fig. 4 and 5, in a specific embodiment, the system monitoring module divides the devices in the whole operation and maintenance system into 8 device systems, and then monitors and displays the health value of each device in the 8 systems and the comprehensive health value of each system.
The net monitoring module 34 is configured to divide the entire operation and maintenance system into a plurality of sites, monitor each site, and calculate a comprehensive health value of each site according to a health value of equipment in each site. As can be seen with reference to fig. 6.
In a preferred embodiment, the system further comprises a maintenance subsystem 40, wherein the maintenance subsystem 40 comprises a maintenance module 41 and a service module 42, the maintenance module 41 is used for maintaining the equipment after the equipment fails, and recording the failure information and the maintenance information of the equipment; the maintenance module 42 is used for maintaining the equipment when the equipment needs to be maintained, and recording maintenance information of the equipment. The data acquisition subsystem 10 is configured to acquire the fault information, the maintenance information, and the overhaul information to obtain the fault maintenance data.
The intelligent operation and maintenance system for the rail transit calculates the health value of each equipment of the rail transit through the equipment health value calculation model, and manages and displays the equipment according to the health value of the equipment. When the health value of the equipment is lower than a preset threshold value, a fault maintenance work order is automatically generated to maintain the equipment, the equipment is prevented from being maintained after the equipment is in fault, potential safety hazards are reduced, and the probability that the whole rail transit cannot run due to the equipment fault is greatly reduced. Furthermore, the equipment can be maintained and repaired pertinently according to the health value of the equipment, invalid maintenance work is avoided, the workload of operation and maintenance personnel is reduced, the operation and maintenance efficiency is greatly improved, and therefore the labor cost is reduced.
It should be understood that although the present description refers to embodiments, not every embodiment contains only a single technical solution, and such description is for clarity only, and those skilled in the art should make the description as a whole, and the technical solutions in the embodiments can also be combined appropriately to form other embodiments understood by those skilled in the art.
The above-listed detailed description is only a specific description of a possible embodiment of the present invention, and they are not intended to limit the scope of the present invention, and equivalent embodiments or modifications made without departing from the technical spirit of the present invention should be included in the scope of the present invention.

Claims (10)

1. A method for scoring a health value of rail transit equipment, the method comprising:
constructing a hierarchical analysis model of equipment health, classifying factors influencing the equipment health according to the negative influence degree on the equipment health, and determining N first factors influencing the equipment health and M second factors corresponding to each first factor;
respectively constructing N +1 judgment matrixes according to the important influence degree of the first factor on the equipment health and the important influence degree of each second factor on the corresponding first factor, and performing consistency check on each judgment matrix;
after each judgment matrix passes consistency check, obtaining the influence weight of each second factor on the equipment health according to the N +1 maximum eigenvectors of the N +1 judgment matrices;
and according to the individual scores and the influence weights of the second factors, calculating the health value S1 of the equipment, namely multiplying the individual scores of all the second factors by the corresponding influence weights and then summing the scores.
2. The method for scoring the health value of the rail transit equipment according to claim 1, wherein the factors affecting the health of the equipment further include a local optimization factor and an overall optimization factor having a positive effect, and the method further comprises:
and calculating the health value S of the equipment according to the score S2 of the local optimization factor and the score S3 of the overall optimization factor and combining the score S1, wherein if the sum of the score S1 and the score S2 is less than a full score, the health value S of the equipment is max { S1+ S2, S3}, and otherwise, the health value S of the equipment is the full score.
3. The method for scoring the health value of the rail transit equipment as claimed in claim 2, wherein calculating the score S2 of the local optimization factor and the score S3 of the global optimization factor specifically comprises:
establishing a grading standard of the local optimization factors and the overall optimization factors, and calculating the grading S2 of the local optimization factors and the grading S3 of the overall optimization factors according to actual data of equipment; wherein the content of the first and second substances,
the scoring standard of the local optimization factors is as follows:
dividing the replaced parts into 4 grades according to the importance degree of the equipment, wherein the grades comprise {0,1,2 and 3}, and the corresponding scores of S2 are {0,10,20 and 30} in turn;
the scoring standard of the overall optimization factors is as follows:
and dividing the equipment overhaul degree into 5 grades including {0,1,2,3 and 4} grades, wherein the corresponding scores of the S3 are {0,70,80,90 and 95} grades in sequence.
4. The method for scoring the health value of the rail transit equipment as claimed in claim 1, wherein: the first factors affecting the health of the equipment include operating conditions and fault conditions, which refer to operating data and fault data, respectively, that occur over a period of time that affect the health of the equipment.
5. The method for scoring the health value of the rail transit equipment as recited in claim 4, wherein:
the second factor corresponding to the running condition comprises running service time, load condition, abnormal running time, abnormal running times and abnormal running ratio;
and the second factor corresponding to the fault condition comprises a fault type, fault times, fault frequency, fault duration and maintenance recovery degree.
6. A method for scoring a health value of rail transit equipment as claimed in claim 5, wherein calculating an individual score for each of the second factors specifically comprises:
and establishing a grading standard of each second factor, and individually grading each specific factor according to the actual data of the operation condition and the fault condition of the equipment.
7. The method for scoring the health value of the rail transit equipment as claimed in claim 6, wherein the acquiring of the actual data of the running condition and the fault condition of the equipment specifically comprises:
respectively establishing normal ranges of voltage, current and vibration frequency for equipment, monitoring the voltage, current and vibration frequency of the equipment in real time, carrying out early warning when the voltage, current or vibration frequency exceeds the corresponding normal ranges, and recording early warning information;
obtaining actual data of the operation condition of the equipment by obtaining current operation data and historical early warning information of the equipment;
after the equipment is subjected to fault maintenance, recording corresponding fault information and maintenance information;
and acquiring the historical fault information and the historical maintenance information of the equipment to obtain the actual data of the fault condition of the equipment.
8. The method for scoring the health value of the rail transit equipment as recited in claim 1, further comprising:
and constructing N +1 judgment matrixes which pass consistency check at least twice to obtain at least two characteristic vectors of the total hierarchical ordering, and selecting the characteristic vector of the highest hierarchical ordering with random consistency as a vector formed by the influence weight of the second factor on the health of the equipment by calculating the random consistency ratio of the total hierarchical ordering, wherein the characteristic vector of the total hierarchical ordering is the vector formed by the influence weight of each second factor on the health of the equipment.
9. An electronic device comprising a memory and a processor, the memory storing a computer program operable on the processor, wherein the processor executes the program to implement the steps of the method for scoring a health value of a rail transit device as claimed in any one of claims 1 to 8.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for scoring a health value of a rail transit device according to any one of claims 1 to 8.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112561352A (en) * 2020-12-21 2021-03-26 卡斯柯信号有限公司 Rail transit signal system health assessment method and device
CN115760854A (en) * 2023-01-09 2023-03-07 佰聆数据股份有限公司 Deep learning-based power equipment defect detection method and device and electronic equipment
CN117077873A (en) * 2023-10-18 2023-11-17 金现代信息产业股份有限公司 Tool scrapping prediction method, system, equipment and medium
CN117077873B (en) * 2023-10-18 2024-06-04 金现代信息产业股份有限公司 Tool scrapping prediction method, system, equipment and medium

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102663505A (en) * 2012-04-10 2012-09-12 北京交通大学 Comprehensive evaluation method for utilization performance of track circuit
CN102682213A (en) * 2012-05-11 2012-09-19 广州市地下铁道总公司 Quality assessment method of rail transit equipment
WO2018053934A1 (en) * 2016-09-22 2018-03-29 北京国电通网络技术有限公司 Early-warning method and early-warning apparatus for devices in power grid
CN108985649A (en) * 2018-08-01 2018-12-11 国电联合动力技术有限公司 Timing ambiguity step analysis appraisal procedure based on wind power equipment anomaly analysis data
WO2019080367A1 (en) * 2017-10-25 2019-05-02 南京航空航天大学 Method for evaluating health status of mechanical device
CN110163486A (en) * 2019-03-19 2019-08-23 广州中国科学院计算机网络信息中心 A kind of project risk different degree appraisal procedure and system
CN110210161A (en) * 2019-06-12 2019-09-06 中国神华能源股份有限公司 Appraisal procedure, device and the storage medium of rail vehicle health status

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102663505A (en) * 2012-04-10 2012-09-12 北京交通大学 Comprehensive evaluation method for utilization performance of track circuit
CN102682213A (en) * 2012-05-11 2012-09-19 广州市地下铁道总公司 Quality assessment method of rail transit equipment
WO2018053934A1 (en) * 2016-09-22 2018-03-29 北京国电通网络技术有限公司 Early-warning method and early-warning apparatus for devices in power grid
WO2019080367A1 (en) * 2017-10-25 2019-05-02 南京航空航天大学 Method for evaluating health status of mechanical device
CN108985649A (en) * 2018-08-01 2018-12-11 国电联合动力技术有限公司 Timing ambiguity step analysis appraisal procedure based on wind power equipment anomaly analysis data
CN110163486A (en) * 2019-03-19 2019-08-23 广州中国科学院计算机网络信息中心 A kind of project risk different degree appraisal procedure and system
CN110210161A (en) * 2019-06-12 2019-09-06 中国神华能源股份有限公司 Appraisal procedure, device and the storage medium of rail vehicle health status

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
任星辰: "城市轨道交通运营设备设施安全评价体系研究" *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112561352A (en) * 2020-12-21 2021-03-26 卡斯柯信号有限公司 Rail transit signal system health assessment method and device
CN112561352B (en) * 2020-12-21 2022-07-26 卡斯柯信号有限公司 Rail transit signal system health assessment method and device
CN115760854A (en) * 2023-01-09 2023-03-07 佰聆数据股份有限公司 Deep learning-based power equipment defect detection method and device and electronic equipment
CN117077873A (en) * 2023-10-18 2023-11-17 金现代信息产业股份有限公司 Tool scrapping prediction method, system, equipment and medium
CN117077873B (en) * 2023-10-18 2024-06-04 金现代信息产业股份有限公司 Tool scrapping prediction method, system, equipment and medium

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