CN114077920A - Intelligent maintenance method, device and equipment for rail transit vehicle - Google Patents

Intelligent maintenance method, device and equipment for rail transit vehicle Download PDF

Info

Publication number
CN114077920A
CN114077920A CN202111134227.1A CN202111134227A CN114077920A CN 114077920 A CN114077920 A CN 114077920A CN 202111134227 A CN202111134227 A CN 202111134227A CN 114077920 A CN114077920 A CN 114077920A
Authority
CN
China
Prior art keywords
time
repaired
repair
determining
component
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202111134227.1A
Other languages
Chinese (zh)
Inventor
霍苗苗
崔霆锐
李莉
赵媛媛
李小东
张欣
刘京
刘畅
张萌
张宇
席伟光
刘洋
李杨紫洁
许岩
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Subway Operation Corp
Original Assignee
Beijing Subway Operation Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Subway Operation Corp filed Critical Beijing Subway Operation Corp
Priority to CN202111134227.1A priority Critical patent/CN114077920A/en
Publication of CN114077920A publication Critical patent/CN114077920A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Marketing (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The application relates to an intelligent maintenance method, device and equipment for rail transit vehicles, belonging to the technical field of rail transit vehicle maintenance, and comprising the steps of predicting the failure prediction time of a component to be repaired according to historical data and current operation data; determining the association degree between the component to be repaired and an associated component according to a pre-established overhaul chain, wherein the associated component refers to the component associated with the component to be repaired; determining the overhaul time of the part to be repaired according to the predicted time, the correlation degree and the existing overhaul data, wherein the existing overhaul data comprises the planned overhaul time of the correlation part; determining the repair cost for repairing the component to be repaired and the replacement cost for replacing the component to be repaired according to the existing repair process data; determining the maintenance mode of the part to be repaired according to the repair cost and the replacement cost, wherein the maintenance mode comprises repair and replacement; and making a maintenance plan according to the maintenance mode and the maintenance time. The method and the device have the effect of reasonably arranging the maintenance plan.

Description

Intelligent maintenance method, device and equipment for rail transit vehicle
Technical Field
The application relates to the technical field of rail transit vehicle maintenance, in particular to a rail transit vehicle intelligent maintenance method, device and equipment.
Background
As for each component of the rail transit vehicle, especially, running gear components such as a bogie, a bearing box and a bearing, certain damage can occur along with the running of the rail transit vehicle, and faults can be caused, so that regular maintenance of the rail transit vehicle is an important measure for guaranteeing the safety of personnel in the running process of the rail transit vehicle.
The conventional planned repair mode specifies a uniform repair cycle, but does not sufficiently consider the actual use of each component. When scheduled maintenance time of a certain component is about to be reached or the component is just maintained, other components are abnormal and need to be maintained, and the condition that high-frequency and unnecessary repeated operation is performed in a short period in maintenance operation can be caused, so that the labor hour utilization rate is reduced, and the labor cost is increased.
Disclosure of Invention
In order to reasonably arrange a maintenance plan, the application provides an intelligent maintenance method, device and equipment for rail transit vehicles.
In a first aspect, the application provides an intelligent maintenance method for rail transit vehicles, which adopts the following technical scheme:
an intelligent maintenance method for rail transit vehicles comprises the following steps:
predicting the failure prediction time of the component to be repaired according to the historical data and the current operation data;
determining the association degree between the component to be repaired and an associated component according to a pre-established overhaul chain, wherein the associated component refers to the component associated with the component to be repaired;
determining the overhaul time of the part to be repaired according to the predicted time, the correlation degree and existing overhaul data, wherein the existing overhaul data comprises the planned overhaul time of the correlation part;
determining the repair cost for repairing the part to be repaired and the replacement cost for replacing the part to be repaired according to the existing repair process data;
determining a maintenance mode of the part to be repaired according to the repair cost and the replacement cost, wherein the maintenance mode comprises repair and replacement;
and making a maintenance plan according to the maintenance mode and the maintenance time.
By adopting the technical scheme, the part to be repaired is possibly influenced by the associated part, so that the overhaul time of the part to be repaired is influenced by the planned overhaul time of the associated part, more accurate overhaul time is determined by the predicted time, the association degree and the existing overhaul schedule data, the overhaul time is reasonably arranged by comprehensively considering the predicted time and the planned overhaul time, the conditions of high-frequency unnecessary repetitive operation on the part to be repaired are reduced, and the labor hour utilization rate is improved; determining the low cost as the maintenance mode of the part to be repaired through the height of the repair cost and the replacement cost so as to reduce the maintenance cost; and a reasonable maintenance plan is formulated through a maintenance mode and maintenance time so as to facilitate maintenance operation.
Preferably, the repair chain is established according to a disassembly sequence between the part to be repaired and the associated part.
Preferably, the determining the association degree between the component to be repaired and the associated component according to the pre-established repair chain includes:
and determining the disassembly interval between the associated part and the part to be repaired, and determining the height of the association degree according to the disassembly interval.
Preferably, the determining the repair time of the component to be repaired according to the predicted time, the correlation degree and the existing repair process data includes:
taking the related component with the highest degree of relevance as a linkage component;
respectively calculating time differences between the predicted time and planned repair times of all the linkage components;
judging whether the time difference is not greater than a preset time or not;
if so, taking the previous time in the scheduled repair time when the time difference between the predicted time and the time difference is not greater than the preset time as the repair time of the part to be repaired;
if not, reducing the association degree by one level, taking the association part with the association degree equal to the association degree after the reduction by one level as a linkage part, and returning to the step of respectively calculating the time difference between the predicted time and the planned repair time of all the linkage parts;
and when the correlation degree is reduced to the lowest level, and the time difference is still larger than the preset time, taking the predicted time of the fault of the part to be repaired as the overhaul time.
By adopting the technical scheme, the higher the association degree is, the larger the influence between the association component and the component to be repaired is, and for the association component with the highest association degree, if the time difference between the planned repair time and the predicted time is not greater than the preset time, the previous time is taken as the repair time of the component to be repaired, so that the component to be repaired is ensured to be repaired before the expected fault occurs, the component to be repaired and the association component with the time difference not greater than the preset time are repaired together, the situations of high-frequency and unnecessary repetitive operation of the component to be repaired are reduced, and the labor hour utilization rate is improved.
Preferably, the determining the repair mode of the component to be repaired according to the repair cost and the replacement cost further includes:
when the repair cost is low, determining that the repair mode of the part to be repaired is the repair;
and when the replacement cost is low, determining the maintenance mode of the part to be repaired as the replacement.
Preferably, after determining that the repair mode of the component to be repaired is the repair mode when the repair cost is low, the method further includes:
determining whether the repair site and repair personnel are both free at the overhaul time;
if so, finally determining the maintenance mode of the part to be repaired as the repair;
and if not, finally determining the maintenance mode of the part to be repaired as the renewal mode.
Preferably, when the replacement cost is low, the method further includes, after determining that the repair mode of the component to be repaired is the replacement, the steps of:
determining whether a replacement part is purchased and completed before the overhaul time, wherein the replacement part is a part for replacing the part to be repaired;
if so, finally determining the maintenance mode of the part to be repaired as the renewal;
and if not, finally determining the maintenance mode of the part to be repaired as the repair mode.
In a second aspect, the present application provides a rail transit vehicle intelligent maintenance device, which adopts the following technical scheme:
a rail transit vehicle intelligence dimension protects device includes:
the prediction module is used for predicting the prediction time of the fault of the component to be repaired according to the historical data and the current operation data;
the device comprises a first determining module, a second determining module and a judging module, wherein the first determining module is used for determining the association degree between the component to be repaired and an associated component according to a pre-established overhaul chain, and the associated component refers to the component associated with the component to be repaired;
the second determination module is used for determining the overhaul time of the part to be overhauled according to the predicted time, the correlation degree and existing overhaul data, wherein the existing overhaul data comprise the planned overhaul time of the correlation part;
the third determining module is used for determining the repair cost for repairing the part to be repaired and the replacement cost for replacing the part to be repaired according to the existing repair process data;
the fourth determining module is used for determining the maintenance mode of the part to be repaired according to the repair cost and the renewal cost, wherein the maintenance mode comprises repair and renewal; and the number of the first and second groups,
and the making module is used for making a maintenance plan according to the maintenance mode and the maintenance time.
In a third aspect, the present application provides a rail transit vehicle intelligent maintenance device, which adopts the following technical scheme:
an intelligent maintenance device for rail transit vehicles, comprising a memory and a processor, wherein the memory is stored with a computer program which can be loaded by the processor and can execute the intelligent maintenance method for rail transit vehicles in any one of the first aspect.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the part to be repaired is possibly influenced by the associated part, so that the repair time of the part to be repaired is influenced by the planned repair time of the associated part, more accurate repair time is determined through the predicted time, the association degree and the existing repair process data, the repair time is reasonably arranged through comprehensively considering the predicted time and the planned repair time, the conditions of high-frequency unnecessary repetitive operation of the part to be repaired are reduced, and the labor hour utilization rate is improved; determining the low cost as the maintenance mode of the part to be repaired through the height of the repair cost and the replacement cost so as to reduce the maintenance cost; a reasonable maintenance plan is made through a maintenance mode and maintenance time so as to facilitate maintenance operation;
2. the higher the degree of association is, the greater the influence between the associated component and the component to be repaired is, and for the associated component with the highest degree of association, if the time difference between the planned repair time and the predicted time is not greater than the preset time, the previous time is taken as the repair time of the component to be repaired, so that the component to be repaired is ensured to be repaired before the fault is expected to occur, the component to be repaired and the associated component with the time difference not greater than the preset time are repaired together, the situation that the component to be repaired is subjected to high-frequency and unnecessary repetitive operation is reduced, and the labor hour utilization rate is improved.
Drawings
The above and other features, advantages and aspects of various embodiments of the present application will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:
FIG. 1 is a schematic flow chart diagram of an intelligent maintenance method for rail transit vehicles according to an embodiment of the present application;
FIG. 2 is a block diagram of a rail transit vehicle intelligent maintenance device provided in the embodiment of the present application;
fig. 3 is a schematic structural diagram of the rail transit vehicle intelligent maintenance device provided in the embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
The present embodiment provides an intelligent maintenance method for rail transit vehicles, as shown in fig. 1, the main flow of the method is described as follows (steps S101 to S106):
step S101: and predicting the failure prediction time of the part to be repaired according to the historical data and the current operation data.
In this embodiment, the historical data includes historical operating data of the component to be repaired and time of the fault, the historical data is analyzed, if a certain operating data occurs, the component to be repaired is about to fail, the operating data is defined as early warning operating data, and a fault time difference between the time of the occurrence of the early warning operating data and the time of the fault is calculated. For example, if the time of occurrence of the early warning operation data is 1 month and 1 day in 2000, and the time of occurrence of the fault is 1 month and 9 days in 2000, the time difference between the faults is 8 days.
And acquiring multiple early warning operation data, correspondingly calculating multiple fault time differences, and selecting the fault time difference with the highest occurrence frequency as the final fault time difference. For example, within ten years, the same early warning operation data occurs 100 times, wherein the fault time difference is 20 times for 8 days, 60 times for 9 days, and 20 times for 10 days, and then 9 days are taken as the final fault time difference.
The method comprises the steps of obtaining current operation data of a to-be-repaired component through various sensors arranged on a rail transit vehicle, comparing the current operation data with early warning operation data, and predicting the failure prediction time of the to-be-repaired component according to failure time difference and current time if the current operation data is the same as or similar to the early warning operation data.
The specific method for judging that the current operation data is similar to the early warning operation data comprises the following steps: respectively obtaining the average values of the current operation data and the early warning operation data in the same time period, calculating the difference value between the average values of the current operation data and the early warning operation data, and if the difference value is smaller than a preset value, judging that the current operation data is similar to the early warning operation data.
The prediction time for predicting the fault of the component to be repaired according to the fault time difference and the current time is illustrated as follows: the current time is No. 8/1 in 2020, and the fault time difference is 9 days, so that the predicted time is No. 8/10 in 2020.
Step S102: and determining the association degree between the part to be repaired and an associated part according to a pre-established overhaul chain, wherein the associated part refers to the part associated with the part to be repaired.
In this embodiment, the associated component refers to a component having a disassembly relationship with the component to be repaired, and the overhaul chain is established according to the disassembly sequence between the component to be repaired and the associated component. For example, if the part to be repaired is a bearing, the repair chain is X-bogie-bearing box-bearing-motor-Y, wherein X, bogie, bearing box, motor and Y are all related parts, and the disassembly sequence refers to the sequential disassembly of X, bogie, bearing box, bearing, motor and Y.
And determining the disassembly interval between the associated part and the part to be repaired, wherein the smaller the disassembly interval is, the higher the association degree is. For example, if the removal interval between the bearing housing and the bearing is 0, the removal interval between the motor and the bearing is also 0, the removal interval between the bogie and the bearing is 1, the removal interval between Y and the bearing is 1, and the removal interval between X and the bearing is 2, the bearing housing and the motor are associated with the bearing at the highest degree, the bogie and Y are associated with the next level, and X is associated with the next level.
Step S103: and determining the overhaul time of the part to be repaired according to the predicted time, the correlation degree and the existing overhaul data, wherein the existing overhaul data comprises the planned overhaul time of the correlation part.
In this embodiment, the scheduled maintenance time refers to maintenance time that is made according to a daily maintenance schedule.
Respectively calculating time differences between the predicted time and planned repair times of all the linkage components by taking the linkage component with the highest degree of association as a linkage component; then judging whether the time difference is not greater than a preset time or not; if so, taking the previous time in the scheduled repair time of which the difference between the predicted time and the time is not more than the preset time as the repair time of the part to be repaired; if not, reducing the association degree by one level, taking the association parts with the association degrees equal to the association degrees after the reduction by one level as linkage parts, and returning to the step of respectively calculating the time difference between the predicted time and the planned repair time of all the linkage parts. And analogizing until the time difference is not greater than the preset time, taking the previous time as the overhaul time of the part to be repaired, or taking the predicted time of the part to be repaired when the time difference is still greater than the preset time when the degree of association is reduced to the lowest level as the overhaul time.
For example, the preset time is 20 days; the bearing box has the highest degree of association with the bearing and the highest degree of association between the motor and the bearing, and the bearing is calculatedTime difference T between the predicted time of (a) and the planned repair time of the bearing housing1Time difference T between predicted time of bearing and planned repair time of motor2. If the time difference T1Sum time difference T2If the time is not more than 20 days, the time and the time difference T are predicted1Sum time difference T2The previous time in (1) is taken as the overhaul time of the part to be repaired; if only the time difference T1If not more than 20 days, the time and time difference T will be predicted1The previous time in (1) is taken as the overhaul time of the part to be repaired; if only the time difference T2If not more than 20 days, the time and time difference T will be predicted2The previous time in (1) is taken as the overhaul time of the part to be repaired; if the time difference T1Sum time difference T2Both greater than 20 days, the bogie and Y are taken as linkage members, and then the time difference between the predicted time and the planned repair time of the linkage members is calculated.
Further, the scheduled repair time of the linkage component can be changed, specifically, the previous time is used as the scheduled repair time of the linkage component with the time difference not greater than the preset time again, so that the component to be repaired and the linkage component are repaired together. For example, the planned repair time of the bearing housing is No. 11/1/2020, the predicted repair time of the bearing is No. 11/10/2020, and the planned repair time of the motor is No. 11/20/2020, and as a result, the time difference T is found1Sum time difference T2The number of the bearing boxes is not more than 20 days, so that the number of 11/month 1 in 2020 is used as the maintenance time of the bearing, and the number of 11/month 1 in 2020 is used as the planned maintenance time of the bearing boxes and the motor.
The repair time of the part to be repaired can also be determined in another way: when the rail transit vehicle runs to a preset mileage, the rail transit vehicle is overhauled once by a whole vehicle overhaul, namely the rail transit vehicle is overhauled, the renovating time of the rail transit vehicle for the whole vehicle overhaul is determined, and if the renovating time is before the predicted time and the time difference between the renovating time and the predicted time is not more than the preset time, the renovating time is used as the overhaul time of the part to be repaired. Similarly, if the truing time is before the scheduled truing time of a certain associated part and the time difference between the truing time and the scheduled truing time is not greater than the preset time, the truing time is used as the scheduled truing time of the associated part again.
Step S104: and determining the repair cost for repairing the part to be repaired and the replacement cost for replacing the part to be repaired according to the existing repair process data.
In this embodiment, the existing repair procedure data includes a planned repair mode of the component to be repaired and a corresponding repair cost, the repair mode includes repair and renewal, and the repair cost includes repair cost and renewal cost.
The existing repair process data also comprises specific information about repair and renewal, and for repairing the repair mode, the specific information comprises repair personnel information, repair site information and expected repair time; for the repair mode of renewing, the specific information comprises information of a renewing person and information of a renewing component, wherein the renewing component is a new component for replacing the component to be repaired, and the information of the renewing component comprises the model and the manufacturer of the renewing component and whether the renewing component is purchased or not.
The repair cost is the sum of labor cost of repair personnel and loss cost of the rail transit vehicle incapable of running in the expected repair time, the existing repair data comprises single-day loss cost of the rail transit vehicle incapable of running in one day, and the loss cost of the rail transit vehicle incapable of running in the expected repair time can be calculated through the single-day loss cost; the replacement cost is the sum of the labor cost of the replacement person and the cost of purchasing the replacement part.
Step S105: and determining the maintenance mode of the part to be repaired according to the repair cost and the replacement cost, wherein the maintenance mode comprises repair and replacement.
Judging the repair cost and the replacement cost, and determining the maintenance mode of the part to be repaired as repair when the repair cost is low; and when the replacement cost is low, determining that the maintenance mode of the part to be repaired is replacement.
Further, after the repair cost is determined to be low, whether the repair site and repair personnel are free in the repair time is determined, namely, whether the repair site is a free site for the rail transit vehicle to stay for repair or not is determined on the day of the repair time, and whether the repair personnel can repair the rail transit vehicle in the repair site is also determined; if so, finally determining the maintenance mode of the part to be repaired as repairing; and if not, finally determining that the maintenance mode of the part to be repaired is to be replaced by new one.
Further, after determining that the replacement cost is low, determining whether the replacement part is purchased and completed before the overhaul time (including the same day of overhaul), namely, determining whether the replacement part can replace the part to be repaired; if so, finally determining the maintenance mode of the part to be repaired as replacement; and if not, finally determining that the maintenance mode of the part to be repaired is repair.
Further, if at least one of the repair site and the repair staff is not free on the day of the overhaul time, and the replacement part still cannot be purchased and completed on the day of the overhaul time, determining the free time of both the repair site and the repair staff, and determining the free time closest to the current time as the repairable time. For example, if the repair site and the repair personnel are free between 11/5/2020 and 11/15/2020, 11/5/2020 is used as the repairable time. The repair cost from the current time to the repairable time is recalculated.
A determination is also made that the renewal component can purchase the projected renewal time for completion, recalculating the renewal cost from the current time to the projected renewal time.
When the recalculated repair cost is low, determining that the repair mode of the part to be repaired is repair; and when the recalculated renewal cost is low, determining the maintenance mode of the part to be repaired as renewal.
Step S106: and making a maintenance plan according to the maintenance mode and the maintenance time.
In this embodiment, the maintenance plan may be embodied in a form, and the maintenance mode and the maintenance time are filled in the form according to a preset format for the staff to check.
Optionally, the specific information for repairing or replacing can be filled in the form.
In order to better implement the method, the embodiment of the application further provides an intelligent maintenance device for the rail transit vehicle, which may be specifically integrated in an intelligent maintenance device for the rail transit vehicle, such as a terminal or a server, where the terminal may include, but is not limited to, a mobile phone, a tablet computer, or a desktop computer.
Fig. 2 is a structural block diagram of an intelligent maintenance device for a rail transit vehicle according to an embodiment of the present application, and as shown in fig. 2, the device mainly includes:
the prediction module 201 is used for predicting the prediction time of the fault of the component to be repaired according to the historical data and the current operation data;
the first determining module 202 is configured to determine a degree of association between a component to be repaired and an associated component according to a pre-established overhaul chain, where the associated component refers to a component associated with the component to be repaired;
the second determining module 203 is configured to determine the repair time of the component to be repaired according to the predicted time, the association degree and the existing repair process data, where the existing repair process data includes the planned repair time of the associated component;
a third determining module 204, configured to determine, according to the existing repair process data, a repair cost for repairing the component to be repaired and a replacement cost for replacing the component to be repaired;
a fourth determining module 205, configured to determine a maintenance manner of the component to be repaired according to the repair cost and the replacement cost, where the maintenance manner includes repair and replacement; and the number of the first and second groups,
and a making module 206 for making a maintenance plan according to the maintenance mode and the maintenance time.
Various changes and specific examples in the method provided by the above embodiment are also applicable to the rail transit vehicle intelligent maintenance device of the embodiment, and through the foregoing detailed description of the rail transit vehicle intelligent maintenance method, those skilled in the art can clearly know the implementation method of the rail transit vehicle intelligent maintenance device in the embodiment, and for the sake of brevity of the description, detailed description is not given here again.
In order to better execute the program of the method, the embodiment of the application also provides a rail transit vehicle intelligent maintenance device, as shown in fig. 3, the rail transit vehicle intelligent maintenance device 300 comprises a memory 301 and a processor 302.
The rail transit vehicle intelligent maintenance device 300 may be implemented in various forms including devices such as a mobile phone, a tablet computer, a palm computer, a notebook computer, a desktop computer, and the like.
The memory 301 may be used to store, among other things, instructions, programs, code sets, or instruction sets. The memory 301 may include a storage program area and a storage data area, wherein the storage program area may store instructions for implementing an operating system, instructions for at least one function (such as predicting a predicted time of failure of a component to be repaired based on historical data and current operating data, etc.), instructions for implementing the rail transit vehicle intelligent maintenance method provided by the above-described embodiment, and the like; the storage data area can store data and the like involved in the rail transit vehicle intelligent maintenance method provided by the embodiment.
Processor 302 may include one or more processing cores. The processor 302 may invoke the data stored in the memory 301 by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 301 to perform the various functions of the present application and to process the data. The Processor 302 may be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, and a microprocessor. It is understood that the electronic devices for implementing the functions of the processor 302 may be other devices, and the embodiments of the present application are not limited thereto.
An embodiment of the present application provides a computer-readable storage medium, including: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk. The computer readable storage medium stores a computer program that can be loaded by a processor and executes the rail transit vehicle intelligent maintenance method of the above-mentioned embodiment.
The specific embodiments are merely illustrative and not restrictive, and various modifications that do not materially contribute to the embodiments may be made by those skilled in the art after reading this specification as required, but are protected by patent laws within the scope of the claims of this application.

Claims (9)

1. An intelligent maintenance method for rail transit vehicles is characterized by comprising the following steps:
predicting the failure prediction time of the component to be repaired according to the historical data and the current operation data;
determining the association degree between the component to be repaired and an associated component according to a pre-established overhaul chain, wherein the associated component refers to the component associated with the component to be repaired;
determining the overhaul time of the part to be repaired according to the predicted time, the correlation degree and existing overhaul data, wherein the existing overhaul data comprises the planned overhaul time of the correlation part;
determining the repair cost for repairing the part to be repaired and the replacement cost for replacing the part to be repaired according to the existing repair process data;
determining a maintenance mode of the part to be repaired according to the repair cost and the replacement cost, wherein the maintenance mode comprises repair and replacement;
and making a maintenance plan according to the maintenance mode and the maintenance time.
2. The method of claim 1, wherein the service chain is established in accordance with a disassembly sequence between the component to be serviced and the associated component.
3. The method of claim 2, wherein determining the degree of association between the part to be repaired and the associated part according to a pre-established service chain comprises:
and determining the disassembly interval between the associated part and the part to be repaired, and determining the height of the association degree according to the disassembly interval.
4. The method of claim 3, wherein determining a repair time for the component to be repaired based on the predicted time, the degree of correlation, and existing repair trip data comprises:
taking the related component with the highest degree of relevance as a linkage component;
respectively calculating time differences between the predicted time and planned repair times of all the linkage components;
judging whether the time difference is not greater than a preset time or not;
if so, taking the previous time in the scheduled repair time when the time difference between the predicted time and the time difference is not greater than the preset time as the repair time of the part to be repaired;
if not, reducing the association degree by one level, taking the association part with the association degree equal to the association degree after the reduction by one level as a linkage part, and returning to the step of respectively calculating the time difference between the predicted time and the planned repair time of all the linkage parts;
and when the correlation degree is reduced to the lowest level, and the time difference is still larger than the preset time, taking the predicted time of the fault of the part to be repaired as the overhaul time.
5. The method according to any one of claims 1 to 4, wherein the determining the repair mode of the component to be repaired according to the repair cost and the replacement cost further comprises:
when the repair cost is low, determining that the repair mode of the part to be repaired is the repair;
and when the replacement cost is low, determining the maintenance mode of the part to be repaired as the replacement.
6. The method according to claim 5, wherein after determining that the repair mode of the component to be repaired is the repair mode when the repair cost is low, the method further comprises:
determining whether the repair site and repair personnel are both free at the overhaul time;
if so, finally determining the maintenance mode of the part to be repaired as the repair;
and if not, finally determining the maintenance mode of the part to be repaired as the renewal mode.
7. The method according to claim 5, wherein after determining that the repair manner of the component to be repaired is the replacement when the replacement cost is low, the method further comprises:
determining whether a replacement part is purchased and completed before the overhaul time, wherein the replacement part is a part for replacing the part to be repaired;
if so, finally determining the maintenance mode of the part to be repaired as the renewal;
and if not, finally determining the maintenance mode of the part to be repaired as the repair mode.
8. The utility model provides a rail transit vehicle intelligence dimension protects device which characterized in that includes:
the prediction module is used for predicting the prediction time of the fault of the component to be repaired according to the historical data and the current operation data;
the device comprises a first determining module, a second determining module and a judging module, wherein the first determining module is used for determining the association degree between the component to be repaired and an associated component according to a pre-established overhaul chain, and the associated component refers to the component associated with the component to be repaired;
the second determination module is used for determining the overhaul time of the part to be overhauled according to the predicted time, the correlation degree and existing overhaul data, wherein the existing overhaul data comprise the planned overhaul time of the correlation part;
the third determining module is used for determining the repair cost for repairing the part to be repaired and the replacement cost for replacing the part to be repaired according to the existing repair process data;
the fourth determining module is used for determining the maintenance mode of the part to be repaired according to the repair cost and the renewal cost, wherein the maintenance mode comprises repair and renewal; and the number of the first and second groups,
and the making module is used for making a maintenance plan according to the maintenance mode and the maintenance time.
9. A rail transit vehicle intelligent maintenance device comprising a memory and a processor, the memory having stored thereon a computer program that can be loaded by the processor and that executes the method of any one of claims 1 to 7.
CN202111134227.1A 2021-09-27 2021-09-27 Intelligent maintenance method, device and equipment for rail transit vehicle Pending CN114077920A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111134227.1A CN114077920A (en) 2021-09-27 2021-09-27 Intelligent maintenance method, device and equipment for rail transit vehicle

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111134227.1A CN114077920A (en) 2021-09-27 2021-09-27 Intelligent maintenance method, device and equipment for rail transit vehicle

Publications (1)

Publication Number Publication Date
CN114077920A true CN114077920A (en) 2022-02-22

Family

ID=80283491

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111134227.1A Pending CN114077920A (en) 2021-09-27 2021-09-27 Intelligent maintenance method, device and equipment for rail transit vehicle

Country Status (1)

Country Link
CN (1) CN114077920A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115171238A (en) * 2022-06-27 2022-10-11 重庆钢铁股份有限公司 Bearing use management method, device, equipment and medium

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115171238A (en) * 2022-06-27 2022-10-11 重庆钢铁股份有限公司 Bearing use management method, device, equipment and medium

Similar Documents

Publication Publication Date Title
De Jonge et al. A review on maintenance optimization
Prytz et al. Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data
CN104966141B (en) Method and system for updating a model used to generate an industrial asset health profile
Huang et al. Two-dimensional warranty with reliability-based preventive maintenance
US20130173325A1 (en) Capital asset investment planning systems
CN109543850B (en) Method and device for processing full-life-cycle state data of railway wagon
Cowing et al. Dynamic modeling of the tradeoff between productivity and safety in critical engineering systems
CN111796953A (en) Failure prediction device and method, storage medium, and model learning and generating method
WO2018135171A1 (en) Maintenance management system and maintenance management checking device used therefor
CN102955876A (en) System and method for dynamic spare part management
CN111786385B (en) Power grid operation and maintenance scheme planning method, system and equipment
CN114862275B (en) Storage logistics system reliability assessment method and system based on digital twin model
JP2008090682A (en) Apparatus and method for providing module trouble information
CN114077920A (en) Intelligent maintenance method, device and equipment for rail transit vehicle
Cao Modeling the effects of dependence between competing failure processes on the condition-based preventive maintenance policy
CN110264055B (en) Component hazard assessment method, device, equipment and computer-readable storage medium
CN109635001B (en) Product reliability improving method and system based on equipment failure data analysis
CN116258482A (en) Method for automatically selecting maintenance scheme, server and electronic equipment
CN116256184A (en) Method, device, equipment and medium for evaluating health state of railway wagon
Majeske et al. Determining warranty benefits for automobile design changes
He et al. Joint optimization of bi‐level imperfect maintenance and spare parts inventory considering order quantity
CN116324638A (en) Preventive maintenance and service life analysis tool
JP2012234226A (en) Plant equipment operable period evaluation method and operable period evaluation device
CN112508366B (en) Equipment comprehensive cost evaluation and prediction method, electronic equipment and storage medium
Hilton Visualization Techniques for Simulation-Based Dependent Failure Analysis

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination