CN111737530B - Rail transit vehicle fault data analysis method based on weighted mapping - Google Patents
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Abstract
The invention discloses a rail transit vehicle fault data analysis method based on weighted mapping, which comprises the following steps: establishing a node weighted mapping relation of product structure decomposition and output structure decomposition; establishing a fault weighted mapping relation between fault records and product structure decomposition; calculating the fault counting result of each level node in the output structure decomposition according to the node weighted mapping relation; and calculating and analyzing the RAMS index of the rail transit vehicle. According to the method, when facing different users or different analysis occasions, RAMS indexes are required to be calculated, analyzed and output according to the specified structural decomposition form, so that the required RAMS calculation and analysis result can be obtained very conveniently, time and labor are saved, and the analysis efficiency is improved; meanwhile, the weighted mapping relation is applied, so that single faults can be analyzed in a distinguishing way when the faults are analyzed, and the analysis result is more reasonable and close to reality.
Description
Technical Field
The invention belongs to the technical field of rail transit vehicle RAMS data analysis, and particularly relates to a rail transit vehicle fault data analysis method based on weighted mapping.
Background
Along with the increasing market preservation amount of rail transit products in recent years, fault data are continuously increased, and aiming at different users or different analysis occasions, the calculation analysis and the output of RAMS indexes are required to be carried out according to a specified structural decomposition form; however, in the process of use and maintenance, different users have different decomposition modes and different decomposition structures guided by product factories or enterprises, and particularly when the new terminal user needs to analyze faults, the subsequent analysis of fault data is time-consuming and labor-consuming.
Disclosure of Invention
The invention aims to provide a rail transit vehicle fault data analysis method based on weighted mapping, which at least partially solves the technical problems, and can realize the calculation analysis and output of RAMS indexes according to a specified structural decomposition form facing different users or different analysis occasions.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
A rail transit vehicle fault data analysis method based on weighted mapping comprises the following steps:
Obtaining a product decomposition structure tree used for analysis in an enterprise, which is called a first decomposition structure tree; and obtaining an output decomposition structure tree required by the end user, called a second decomposition structure tree;
Constructing a node weighted mapping relation between the first decomposition structure tree and the second decomposition structure tree; the node weighted mapping relation comprises the following steps: one-to-one, one-to-many, many-to-one, many-to-many; each mapping corresponds to a node weighting mapping coefficient and is determined according to a preset rule;
acquiring fault record, operation record, maintenance record and technical improvement record data of the rail transit vehicle;
Establishing a fault weighted mapping relation between the fault record and the nodes of the first decomposition structure tree; determining a fault weighted mapping coefficient according to the preset rule;
calculating a fault count value for each node according to the first decomposition structure tree;
Calculating an output fault count value of each node in the second decomposition structure tree according to the node weighted mapping relation;
and evaluating the relevant RAMS index of the vehicle according to the output fault count value of each node in the second decomposition structure tree.
Further, constructing a node weighted mapping relationship between the first decomposition structure tree and the second decomposition structure tree, including:
Assuming that the nodes in the first decomposition structure tree are identified as Item i, the nodes in the second decomposition structure tree are identified as O j,
The node weighted mapping relationship between the first and second decomposition structure trees is expressed as:
Oj＝∑i(ajiItemi) (1)
(1) Wherein a ji represents the node weighted mapping coefficient of the Item i mapped to O j, and is a non-negative real number; a ji is 0 when there is no mapping relationship between Item i and O j; i represents the number of node identities in the first decomposition structure tree; j represents the number of node identities in the second decomposition structure tree;
the node weighted mapping relation in the matrix form is expressed as:
O＝A*Item (2)
(2) Wherein A represents a node weighted mapping matrix of j rows and i columns; item represents a node of the first decomposition structure tree; o represents a node of the second decomposition structure tree.
Further, establishing a fault weighted mapping relationship between the fault record and the nodes of the first decomposition structure tree, including:
assume that the fault records have m total pieces;
Rm＝[rm1 rm2…rmi] (3)
(3) Wherein r mi represents a fault weighted mapping coefficient between the mth fault record and the first decomposition structure tree node Item i, and the fault weighted mapping coefficient is a non-negative real number; r m is represented in row vector form;
the fault weighted mapping matrix is expressed as:
(4) Where R is represented as a fault weighted mapping matrix of m rows and i columns.
Further, according to the first decomposition structure tree, calculating a failure count value for each node includes:
summing all fault records mapped to the same node in the statistical range according to the corresponding fault weighted mapping coefficients:
Fi＝∑m rmi (5)
(5) Wherein F i represents a failure count value of each node in the first decomposition structure tree; m represents the number of faults; r mi denotes a fault weighted mapping coefficient, which is a non-negative real number;
the calculation formula of the matrix form is as follows:
F＝RT*M (6)
(6) Where F represents a failure count column vector containing i elements, and M is a sum column vector of M1's:
R T represents the transpose of the failure weighted mapping matrix R.
Further, calculating an output fault count value of each node in the second decomposition structure tree according to the node weighted mapping relation, including:
Multiplying fault count values of nodes of a first decomposition structure tree mapped to the same node by corresponding weighted distribution coefficients respectively, and summing:
Foj＝∑i(ajiFi) (7)
(7) Where Fo j represents the failure count value of each node in the second decomposition structure tree; f i represents a failure count value for each node in the first decomposition structure tree;
the calculation formula of the matrix form is as follows:
Fo＝A*F (8)
(8) Where Fo represents the output failure count column vector of j elements of the second decomposition structure tree.
Compared with the prior art, the invention has the following beneficial effects:
by applying the rail transit vehicle fault data analysis method based on weighted mapping, according to the method, the required RAMS calculation analysis result can be obtained very conveniently, time and labor are saved, and the analysis efficiency is improved when the calculation analysis and the output of RAMS indexes are required to be carried out according to the specified structural decomposition form in the face of different users or different analysis occasions; meanwhile, the weighted mapping relation is applied, so that single faults can be analyzed in a distinguishing way when the faults are analyzed, and the analysis result is more reasonable and close to reality.
Drawings
FIG. 1 is a flow chart of a method for analyzing rail transit vehicle fault data based on weighted mapping provided by the invention;
FIG. 2 is a schematic diagram of a product decomposition structure tree for use in enterprise internal analysis provided by the present invention;
FIG. 3 is a schematic diagram of an end user desired output decomposition structure tree provided by the present invention;
FIG. 4 is a schematic diagram of another output decomposition structure tree required by an end user in accordance with the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1, a rail transit vehicle fault data analysis method based on weighted mapping includes:
s10, acquiring a product decomposition structure tree used for analysis in an enterprise, namely a first decomposition structure tree; and obtaining an output decomposition structure tree required by the end user, called a second decomposition structure tree;
S20, constructing a node weighted mapping relation between the first decomposition structure tree and the second decomposition structure tree; the node weighted mapping relation comprises the following steps: one-to-one, one-to-many, many-to-one, many-to-many; each mapping corresponds to a node weighting mapping coefficient and is determined according to a preset rule;
S30, acquiring fault record, operation record, maintenance record and technical improvement record data of the rail transit vehicle; evaluating the RAMS index for a subsequent step;
S40, establishing a fault weighted mapping relation between the fault record and the nodes of the first decomposition structure tree; determining a fault weighted mapping coefficient according to the preset rule;
s50, calculating a fault count value for each node according to the first decomposition structure tree;
s60, calculating an output fault count value of each node in the second decomposition structure tree according to the node weighted mapping relation;
s70, evaluating relevant RAMS indexes of the vehicle according to the output fault count value of each node in the second decomposition structure tree.
In step S10, the first decomposition structure tree is generally an enterprise, or a decomposition mode adopted by a manufacturer is generally a service provider for providing services, for example, may be functional decomposition, assembly structure decomposition or maintenance structure decomposition; the second decomposition structure tree is, for example, a decomposition scheme required by the end user, and is generally a decomposition scheme adopted by the service object. Such as decomposition according to a product installation deployment manner, or decomposition based on structural components defined in national standards or industry standards; or other decomposition that is end user-defined.
In steps S20 and S40, the preset rules are, for example, theoretical knowledge, engineering experience, statistical analysis result and/or specified rules; the specified rules include, but are not limited to, rules formed by performing operations and logical combinations based on values of failure time, accumulated operation mileage at failure, late time, failure result, failure influence, failure level, and the like in the vehicle history data.
In step S50, when analyzing the fault data, first, according to the product structure decomposition, a fault count value is calculated for each node, and the calculation method is as follows: summing all fault records mapped to the node in the statistical range according to the corresponding weighting coefficients, wherein the summation result is the fault count value of the node;
in step S60, then, according to the weighted mapping relationship between the product structure decomposition and the output structure decomposition, the fault count value of each node in the output structure decomposition is calculated, and the calculation method is as follows: multiplying the fault count value of each node mapped to the product structure decomposition of the node by the corresponding weighted distribution coefficient, and then summing, wherein the summation result is the fault count value of the node;
In step S70, the values of the accumulated running mileage, running time, maintenance time, etc. of the vehicle can be obtained through statistics of the running record, the maintenance record, and the technical improvement record data, and further, according to the fault count value of each node in the output structure decomposition number, relevant RAMS indexes including, but not limited to, a million kilometer fault rate, a million hour fault rate, an average fault interval mileage, an average fault interval time, an average maintenance time, availability, reliability, fault probability, etc. and calculation and evaluation operations such as duty ratio analysis, comparative analysis, trend analysis, etc. are calculated;
When the new output structure is decomposed, only the step S20 is needed, and the steps S30 to S50 are not needed, so that the steps S60 and S70 can be directly executed to obtain the required analysis result.
In the embodiment, a node weighted mapping relation of product structure decomposition and output structure decomposition is established; establishing a fault weighted mapping relation between fault records and product structure decomposition; calculating the fault counting result of each level node in the output structure decomposition according to the node weighted mapping relation; and further, the RAMS index of the rail transit vehicle is calculated and analyzed. In the face of different users or different analysis occasions, RAMS indexes are required to be calculated, analyzed and output according to a specified structural decomposition form, so that required RAMS calculation and analysis results can be obtained very conveniently, time and labor are saved, and analysis efficiency is improved; meanwhile, the weighted mapping relation is applied, so that single faults can be analyzed in a distinguishing way when the faults are analyzed, and the analysis result is more reasonable and close to reality.
The above steps are described in detail below by way of a specific example.
1) Obtaining a product decomposition structure tree (a first decomposition structure tree) and outputting a decomposition structure tree (a second decomposition structure tree);
the nodes in the product-decomposition structure tree are numbered sequentially from 1, and then a product-decomposition structure tree with a total number of nodes i can be represented by a column vector Item containing i elements:
Each element Item i of the column vector corresponds to a node in the product-decomposition-structure tree. Wherein, the product decomposition structure tree is exemplified as shown in fig. 2; taking a brake system as an example, the product breakdown structure tree includes: two layers; the first layer is: item 1 -brake system; the second layer is: the Item 1 -brake system nodes include 10 nodes of Item 2 -BCU component, item 3 -EP brake solenoid valve, item 4 -EP release solenoid valve, item 5 -pressure sensor, item 6 -emergency solenoid valve, item 7 -empty-weight car trim valve, item 8 -cut-off cock, item 9 -park brake device, item 10 -anti-skid valve and Item 11 -foundation brake, respectively. The corresponding column vector Item has a total of 11 elements, which can be expressed as:
Similarly, the nodes in the output decomposition structure tree are numbered sequentially from 1, and then an output decomposition structure tree with a total of j nodes can be represented by a column vector O containing j elements:
Each element O j of the column vector corresponds to a node in the output decomposition-structure tree. An example of an output decomposition structure tree is shown in FIG. 3; also taking a brake system as an example, the output decomposition structure tree includes: three layers. The first layer is: o 1 -brake control system; the second layer is: the O 1 -brake control system comprises an O 2 -brake control device and an O 7 -clamp; the third layer is as follows: the O 2 -brake control device comprises an O 3 -BCU component, an O 4 -electric-air switching valve, an O 5 -emergency electromagnetic valve and an O 6 -cutoff plug door. The corresponding column vector O has 7 elements in total, which can be expressed as:
2) And establishing a weighted mapping relation between the product structural decomposition tree and the output structural decomposition tree, wherein the product structural decomposition tree is functional decomposition, assembly structural decomposition or maintenance structural decomposition. The node mapping relation between the product structural decomposition tree and the nodes of the output structural decomposition tree can be one-to-one, one-to-many, many-to-one, many-to-many, and each mapping corresponds to a node weighted mapping coefficient which can be determined according to theoretical knowledge, engineering experience, statistical analysis results and/or specified rules;
Assuming that the nodes in the product-decomposition structure tree are identified as Item i and the nodes in the output-decomposition structure tree are identified as O j, the node-weighted mapping relationship between the product-decomposition structure tree and the output-decomposition structure tree can be expressed as:
Oj＝∑i(aji Itemi) (1)
(1) Where a ji represents the structure weighted mapping coefficient of Item i to O j, which is usually a non-negative real number, and a ji is 0 when there is no mapping relationship between Item i and O j.
The weighted mapping relation in the form of a matrix can be expressed as:
O＝A*Item (2)
(2) Wherein A is a structural weighted mapping matrix of j rows and i columns.
Structural weighted mapping matrix example:
3) Acquiring record data such as fault records, operation records, maintenance records and technical improvement records of the vehicle;
4) Establishing a fault weighted mapping relation between the vehicle fault record and the nodes of the product decomposition structure tree; the fault mapping relationship may be one-to-one, one-to-many, many-to-one, many-to-many, and each mapping corresponds to a fault weighted mapping coefficient, which may likewise be determined according to theoretical knowledge, engineering experience, statistical analysis results, and/or specified rules.
Assume that the fault records have m total pieces;
Rm＝[rm1 rm2…rmi] (3)
(3) Wherein r mi represents a fault weighted mapping coefficient between the mth fault record and the first decomposition structure tree node Item i, and the fault weighted mapping coefficient is a non-negative real number; r m is represented in row vector form;
the fault weighted mapping matrix is expressed as:
(4) Where R is represented as a fault weighted mapping matrix of m rows and i columns.
The specified rules include, but are not limited to, rules formed by performing calculation and logic combination according to data such as fault time, accumulated running mileage during fault, delay time, fault result, fault influence, fault level, system components to which the fault belongs and the like in the vehicle history data;
As an example, assume that there are 10 fault records in total, and if the fault results in a later time of more than 5 minutes, or the fault results are not out of stock, dropped, cleared, and rescue, the fault weighted mapping coefficient of the system component to which the corresponding fault belongs is 1 (the fault caused by its own cause) or 0.5 (the fault caused by the operating environment), and the rest is 0, and the corresponding fault weighted mapping matrix R is:
5) When analyzing fault data, firstly, calculating a fault count value F i for each node according to a product structure decomposition tree, wherein the calculation method is to sum all fault records mapped to the node in a statistical range according to corresponding fault weighted mapping coefficients:
Fi＝∑m rmi (5)
(5) Wherein F i represents a failure count value of each node in the first decomposition structure tree; m represents the number of faults; r mi denotes a fault weighted mapping coefficient, which is a non-negative real number;
the calculation formula of the matrix form is as follows:
F＝RT*M (6)
(6) Where F represents a failure count column vector containing i elements, and M is a sum column vector of M1's:
R T represents the transpose of the failure weighted mapping matrix R.
The failure count vector F calculated as in the example above is:
6) And then calculating an output fault count value Fo j of each node in the output structural decomposition tree according to the node weighted mapping relation between the product structural decomposition tree and the output structural decomposition tree, wherein the calculation method comprises the following steps: multiplying the fault count value of each node of the product structure decomposition tree mapped to the node by a corresponding weighted distribution coefficient, and summing the multiplied fault count values:
Foj＝∑i(ajiFi) (7)
(7) Where Fo j represents the failure count value of each node in the second decomposition structure tree; f i represents a failure count value for each node in the first decomposition structure tree;
the calculation formula of the matrix form is as follows:
Fo＝A*F (8)
(8) Where Fo represents the output failure count column vector of j elements of the second decomposition structure tree.
The output failure count vector calculated as in the example above is:
7) The accumulated running mileage, running time, maintenance time and other values of the vehicle can be obtained through running record, maintenance record and technical improvement record data statistics, and then related RAMS indexes including but not limited to million kilometers fault rate, million hours fault rate, average fault interval mileage, average fault interval time, average maintenance time, availability, reliability, fault probability and the like, and calculation evaluation operations such as duty ratio analysis, comparison analysis, trend analysis and/or the like are calculated according to the fault count value of each node in the output structure decomposition tree;
For example, when the accumulated running mileage of the vehicle is counted to be 500 km, fo may be further calculated to obtain that the million km failure rate of the brake control device (O 2) in the output decomposition structure is Fo 2/5=1.1.
Finally, when a new output structure decomposition tree (second decomposition structure tree) exists, only the step 2) is needed, and the steps 3) to 5) are not needed, so that the steps 6) and 7) can be directly executed to obtain the required analysis result.
For example, it is necessary to analyze the tree in a new output decomposition structure as shown in FIG. 4: also taking the above brake system as an example, the output decomposition structure tree includes three layers: the first layer is: o 1 -brake control system; the second layer is: the O 1 -brake control system comprises an O 2 -brake control device, an O 6 -auxiliary brake control device and an O 9 -auxiliary brake control device; the third layer includes two nodes, the first node O 2 -brake control device includes: o 3 -service brake control, O 4 -emergency brake control, and O 5 -anti-slip control; the second node O 6 -auxiliary brake control device includes: o 7 -park brake control and 8-brake cut-off. The corresponding column vector O has 9 elements in total, which can be expressed as:
the new structural weight mapping matrix can be expressed as:
the calculated new output failure count vector is:
When the accumulated running mileage of the vehicle is counted to be 500 km, the Fo new can be used for further calculating to obtain that the fault rate of million km of the brake control device (O 2) in the new output decomposition structure is
The foregoing has shown and described the basic principles and main features of the present invention and the advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
1. A rail transit vehicle fault data analysis method based on weighted mapping is characterized in that: comprising the following steps:
Obtaining a product decomposition structure tree used for enterprise internal analysis, which is called a first decomposition structure tree, wherein the number of node identifiers in the first decomposition structure tree is i; obtaining an output decomposition structure tree required by the end user, namely a second decomposition structure tree, wherein the number of node identifiers in the second decomposition structure tree is j;
Constructing a j-row i-column node weighted mapping relation matrix between the first decomposition structure tree and the second decomposition structure tree; the node weighted mapping relation comprises the following steps: one-to-one, one-to-many, many-to-one, many-to-many; each mapping corresponds to a node weighting mapping coefficient and is determined according to a preset rule;
Acquiring fault record, operation record, maintenance record and technical improvement record data of the rail transit vehicle, wherein the number of the fault records is recorded as m;
establishing an m-row i-column fault weighted mapping relation matrix between the fault record and the nodes of the first decomposition structure tree; determining a fault weighted mapping coefficient according to the preset rule;
Calculating a fault count value for each node according to the first decomposition structure tree; the calculation method is that all fault records mapped to the node in the statistical range are summed according to the corresponding fault weighted mapping coefficient;
Calculating the output fault count value of each node in the second decomposition structure tree according to the node weighted mapping relation matrix; the calculation method comprises the following steps: multiplying the fault count value of each first decomposition structure tree node mapped to the node by a corresponding node weighted distribution coefficient, and then summing, wherein the summation result is the fault count value of the node;
Evaluating relevant RAMS indexes of the vehicle according to the output fault count value of each node in the second decomposition structure tree;
Wherein, constructing a node weighted mapping relation between the first decomposition structure tree and the second decomposition structure tree
A system matrix comprising:
assume that a node in a first decomposition structure tree is identified as Node labels in a second decomposition structure tree
Is recognized as，
The node weighted mapping relationship between the first and second decomposition structure trees is expressed as:
（1）
(1) In the method, in the process of the invention, Representation/>Mapping to/>Is a non-negative real number; when/>And/>When there is no mapping relation between the twoIs 0;
the node weighted mapping relation in the matrix form is expressed as:
（2）
(2) In the formula, A represents a node weighted mapping relation matrix of j rows and i columns; item represents a node of the first decomposition structure tree; o represents a node of the second decomposition structure tree.
2. The rail transit vehicle fault data analysis method based on weighted mapping of claim 1, wherein: establishing an m-row i-column fault weighted mapping relation matrix between the fault record and the nodes of the first decomposition structure tree, wherein the m-row i-column fault weighted mapping relation matrix comprises the following steps:
（3）
(3) In the method, in the process of the invention, Representing the mth fault record and the first decomposition structure tree node/>Is a fault of (a)
Weighting mapping coefficients, which are non-negative real numbers; represented in a row vector form;
The failure weighted mapping relation matrix is expressed as:
（4）
(4) In the formula, R is expressed as a fault weighted mapping relation matrix of m rows and i columns.
3. The rail transit vehicle fault data analysis method based on weighted mapping of claim 2, wherein: calculating a failure count value for each node according to the first decomposition structure tree, comprising:
The fault records mapped to the same node in the statistical range are mapped according to the corresponding fault weighting system
And (3) sum of numbers:
（5）
(5) In the method, in the process of the invention, A fault count value representing each node in the first decomposition structure tree;
the calculation formula of the matrix form is as follows:
（6）
(6) Where F represents a failure count column vector containing i elements, and M is a sum column vector of M1's: ；
Is the transpose of the failure weighted mapping relation matrix R in the expression (3).
4. A rail transit vehicle fault data analysis method based on weighted mapping as claimed in claim 3, wherein: calculating an output fault count value of each node in the second decomposition structure tree according to the node weighted mapping relation matrix, wherein the output fault count value comprises:
Multiplying fault count values of nodes of a first decomposition structure tree mapped to the same node by corresponding weighted distribution coefficients respectively, and summing:
（7）
(7) In the method, in the process of the invention, A fault count value representing each node in the second decomposition structure tree;
the calculation formula of the matrix form is as follows:
（8）
(8) Where Fo represents the output failure count column vector of j elements of the second decomposition structure tree.
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| CN106202665A (en) * | 2016-06-30 | 2016-12-07 | 北京航空航天大学 | Initial failure root primordium recognition methods based on domain mapping Yu weighted association rules |
| CN108710726A (en) * | 2018-04-16 | 2018-10-26 | 西安飞机工业（集团）有限责任公司 | A kind of aircraft configuration control method based on unique design data source |
| CN109165430A (en) * | 2018-08-09 | 2019-01-08 | 北京汽车研究总院有限公司 | A kind of Vehicular intelligent process route modeling method |
| CN110188040A (en) * | 2019-05-21 | 2019-08-30 | 江苏锐天信息科技有限公司 | A kind of software platform for software systems fault detection and health state evaluation |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106202665A (en) * | 2016-06-30 | 2016-12-07 | 北京航空航天大学 | Initial failure root primordium recognition methods based on domain mapping Yu weighted association rules |
| CN108710726A (en) * | 2018-04-16 | 2018-10-26 | 西安飞机工业（集团）有限责任公司 | A kind of aircraft configuration control method based on unique design data source |
| CN109165430A (en) * | 2018-08-09 | 2019-01-08 | 北京汽车研究总院有限公司 | A kind of Vehicular intelligent process route modeling method |
| CN110188040A (en) * | 2019-05-21 | 2019-08-30 | 江苏锐天信息科技有限公司 | A kind of software platform for software systems fault detection and health state evaluation |
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