CN116279700A - Method for realizing fault early warning and management of electronic related equipment of railway signal system - Google Patents
Method for realizing fault early warning and management of electronic related equipment of railway signal system Download PDFInfo
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- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
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Abstract
The invention provides a method for realizing fault early warning and management of electronic related equipment of a railway signal system, which comprises the following steps: retrieving delay passenger clearing data and determining a fault mode causing passenger clearing operation delay; establishing a fault logic model, a fault evaluation model and a relation between a fault mode and an operation state and a relation between maintenance measures and the fault mode in a monitoring and early warning system; extracting fault characteristic parameters according to the relation among the fault logic model, the fault mode and the running state, and judging whether the fault characteristic parameters are in a normal monitoring threshold value or not; and/or obtaining the occurrence probability of the fault mode according to the fault evaluation model; giving different weights to fault characteristic parameters and occurrence probability corresponding to the fault mode in combination with key factors, and calculating the early warning grade of the fault mode; and selecting a method for eliminating the fault early warning level according to the relation between the maintenance measure and the fault mode, realizing early warning and management of the fault mode, and effectively reducing the fault rate.
Description
Technical Field
The invention belongs to the field of operation and maintenance analysis of electronic systems of railway signal systems, and particularly relates to a fault early warning and management implementation method for electronic related equipment of a railway signal system.
Background
At present, railway signal industry specifications such as 'novel train control system vehicle-mounted equipment temporary technical condition', 'novel train control system train control interlocking integrated equipment temporary technical condition', 'CTCS-2 level train control vehicle-mounted equipment technical condition', 'autonomous CTCS-3 level train control vehicle-mounted equipment technical condition' are required for RAM indexes such as failure rate, maintenance time, maintenance guarantee and the like of the vehicle-mounted or interlocking equipment. Although the signal system is designed to reduce the fault inflow to the site through the forward focusing of the full life cycle, faults such as ATP downtime of the train or loss of integrity of the train still exist in the operation through combing, so that line-level operation delay is caused, and the operation service capacity of the railway signal system is reduced.
Firstly, aiming at major faults which affect operation, such as ATP downtime, incapability of releasing emergency braking, incapability of opening a vehicle door, incapability of restarting vehicle-mounted equipment and the like, most of the current strategies adopted are maintenance strategies after faults, which have certain limitations and cannot avoid line chain reactions caused by sudden faults or emergency accidents. Secondly, if operation delay caused by passenger clearing or vehicle dropping and the like can not be early warned, only regular inspection is carried out, and the defects of long time consumption, high inspection cost, high labor consumption, low efficiency and the like exist, so that the economic benefit is low.
Disclosure of Invention
According to the fault characteristics of the operation service capability requirement of the railway signal system and the randomness of the signal electronic equipment, the invention provides a fault early warning and management implementation method suitable for the electronic related equipment of the railway signal system, early warning is carried out on operation delay faults such as passenger clearing or vehicle dropping in advance, and line chain reaction or emergency accidents caused by sudden faults are avoided; the service life index of the wear-type component can be given according to the operation parameters of the on-line diagnosis, and fed back to the design, so that the product type selection efficiency is improved, and the failure rate is reduced; meanwhile, possible fault reasons, maintenance measures and other maintenance information are given out while fault early warning is carried out, preventive maintenance is carried out, an effective active fault processing mode is realized, the periodic checking cost is reduced, the limitations of the existing fault diagnosis-fault maintenance are overcome, more time is given to maintenance preparation management, and unnecessary cost is avoided. Finally, the fault early warning and management implementation method can realize adjustment and correction of the fault early warning management frame and the fault early warning model according to specific application environments and data of different lines.
In order to achieve the above purpose, the present invention provides a method for implementing fault early warning and management of electronic related equipment of a railway signal system, which comprises: retrieving delay passenger clearing data and determining a fault mode causing passenger clearing operation delay; establishing a fault logic model, a fault evaluation model and a relation between a fault mode and an operation state and a relation between maintenance measures and the fault mode in a monitoring and early warning system; extracting fault characteristic parameters corresponding to the fault mode according to the fault logic model and the relation between the fault mode and the running state, monitoring the fault characteristic parameters, and judging whether the fault characteristic parameters are in a normal monitoring threshold value or not; and/or according to the fault evaluation model, obtaining the occurrence probability of the fault mode; giving different weights to fault characteristic parameters and occurrence probability corresponding to the fault mode in combination with key factors, and calculating early warning grades of the fault mode; and selecting a method for eliminating the fault early warning level according to the relation between the maintenance measure and the fault mode, and realizing early warning and management of the fault mode.
Preferably, the step of retrieving the deferred passenger data and determining the fault mode causing the passenger operation deferred comprises the following steps: s101, calling a delay clear-to-customer document of each year from each item management department; s102, screening fault influence fields in the deferred visitor document in the step S101; s103, calling a fault mode according to the fault influence field; s104, establishing a fault mode which causes delay of more than M minutes as a research object, wherein M is more than or equal to 5.
Preferably, the fault impact field includes "clear", "drop", "delay"; the fault modes comprise ATP mode loss, incapability of releasing emergency braking, incapability of opening a vehicle door, incapacity of starting a vehicle, vehicle-mounted crash and incapacity of establishing a train mode.
Preferably, the establishing the fault logic model in the monitoring and early warning system includes the following steps: s201, calling a function design document, wherein the design document comprises a system function requirement document, a system architecture design document, a hardware design document and a software design document which are related to a fault mode; s202, analyzing a fault reason for generating a fault mode, and selecting a fault tree or a function-fault flow method for analysis; s203, determining a logic relation between the fault cause and the fault mode, wherein the logic relation comprises OR logic and AND logic; s204, analyzing a failure mechanism of a failure mode, wherein the failure mechanism is specifically analyzed to a module circuit or a key chip; s205, calling design data, wherein the design data comprises a hardware schematic diagram and a software function corresponding to the fault mode.
Preferably, the building the fault logic model in the monitoring and early warning system further includes: s206, determining monitored fault characteristic parameters; s207, judging whether the fault characteristic parameters in the step S206 are monitored by a monitoring and early warning system; if the fault characteristic parameter is monitored, marking the fault characteristic parameter as monitored; if the fault characteristic parameter is not monitored, executing step S208; and S208, incorporating the fault characteristic parameters which are not monitored into the monitoring range of the monitoring and early warning system.
Preferably, the determining the monitored fault characteristic parameter in step S206 includes the following steps: s261, extracting fault characteristic parameters, wherein the fault characteristic parameters comprise impedance, current and voltage; s262, judging whether the fault characteristic parameter extracted in the step S261 is an analog signal, and if the fault characteristic parameter is the analog signal, executing the step S263; otherwise, determining a fault characteristic parameter of the fault mode according to the relation between the fault mode and the running state; s263, selecting an algorithm for extracting fault characteristic parameters; s264, embedding a normal monitoring threshold curve in the monitoring early warning system for judging fault characteristic parameters; s265, if the fault characteristic parameter is in the normal monitoring threshold curve, ending the judgment of the fault characteristic parameter and incorporating the monitoring range of the monitoring early warning system; otherwise, if the normal monitoring threshold curve is exceeded, step S266 is performed; s266, providing an early warning and maintenance strategy; if the fault characteristic parameters exceed the normal range, early warning or maintenance measures are given.
Preferably, if the fault characteristic parameter is irregular and the output is a discrete 0/1 signal, an ID3 algorithm or an Apriori algorithm is selected; and if the fault characteristic parameters are current or voltage signals, selecting a support vector machine algorithm for extracting and monitoring the fault characteristic parameters.
Preferably, the establishing the fault evaluation model in the monitoring and early warning system includes the following steps: s301, relevant fault data are called; s302, performing data cleaning and processing on the relevant fault data called in the step S301; s303, manufacturing a fault form according to the relevant fault data after the cleaning processing in the step S302; s304, initializing a parameter model; s305, fitting the parameter model selected in the step S304 according to the relevant fault data after the cleaning treatment; s306, comparing the result of the parameter model after fitting in the step S305 with the result of the RAM monitoring platform; if the result of step S305 is close to the RAM monitoring platform result, step S307 is executed; if the result of the step S305 is different from the result of the RAM monitoring platform greatly, returning to the step S304, and reselecting the initialized parameter model; s307, determining the parameter model after fitting in the step S305 as a fault evaluation model; s308, embedding the fault evaluation model determined in the step S307 into a monitoring and early warning system to complete construction of the fault evaluation model in the monitoring and early warning system.
Preferably, the parametric model described in step S304 includes an X-rank regression or maximum likelihood method to build a cumulative fault probability model.
Preferably, the parametric model comprises a 2-parametric model of weibull
Beta is a shape parameter, eta is a scale parameter, and t is working time; weibull's 3 parameter model->
Beta is a shape parameter, eta is a scale parameter, and gamma is a position parameter; exponential distribution model F (t) =1-e -λt Lambda is the failure rate and t is the working time.
Preferably, the building the fault evaluation model in the monitoring and early warning system further includes step S309: manufacturing a repair form according to the maintainability data; the maintainability data includes delay time and maintenance time.
Preferably, a relation flow diagram of fault event and running state is constructed in the monitoring and early warning system, which comprises the following steps: s401, determining an alarm fault mode; s402, acquiring log data and acquiring a relevant state quantity field of an alarm fault mode; s403, combing the link relation between the fault mode and the running state in the log data; the operation state and characteristic parameter change N minutes before the alarm fault mode is searched for, and the selection of the characteristic parameter of the fault is assisted, wherein N is more than or equal to 10.
Preferably, the method further comprises the step of establishing a wear-type component life assessment model in the monitoring and early warning system, and specifically comprises the following steps: s501, screening a wear-out failure mechanism component; s502, determining design parameters by inquiring a design manual; s503, determining actual application parameters; s504, determining application environment factors; s505, determining a wear-type component life assessment model, and establishing a model by combining the specification and actual online operation data; s506, embedding the wear-type component life assessment model determined in the step S505 into a monitoring and early warning system.
Preferably, the construction of the relationship between the maintenance measures and the fault mode in the monitoring and early warning system comprises the following steps: s601, determining a fault mode list of fault early warning; s602, calling a field 'measures taken on site' in a field fault data statistics system and a frequent fault recording system; s603, calling a field 'measures taken on site' in a site fault event reporting platform; s604, retrieving a field 'maintenance suggestion' in the existing alarm system; s605, generating a fault mode and a maintenance measure form of fault early warning; s606, judging whether a monitoring and early warning system exists in the relation between the fault and the maintenance measures, if so, not repeatedly adding the relation to the monitoring and early warning system, and if not, executing a step S607; s607, embedding the generated fault mode and the maintenance measure form into the monitoring and early warning system.
Preferably, the key factors include environmental factors, elapsed time or frequency, fault characteristic values, and alarm numbers.
Preferably, the environmental factors include temperature, humidity, salt fog and dust, and the quality of the environmental factors are determined according to whether the equipment has dust prevention and heat dissipation design data, so that weights of different environmental factors are given.
Preferably, the weight omega of each key factor is determined by using expert experience method and historical data i And correcting and adjusting the weight omega of each key influence factor in the monitoring and early-warning system by combining the actual early-warning data of the monitoring and early-warning system i Values.
In summary, compared with the prior art, the method for realizing the early warning and management of the faults of the electronic related equipment of the railway signal system can early warn the operation delay faults such as passenger clearing or vehicle dropping in advance, avoid the line cascade reaction caused by sudden faults or the occurrence of emergency accidents, reduce the passenger clearing or the delay fault rate of more than 5 minutes, and improve the passenger riding satisfaction;
further, the invention provides an evaluation model based on field fault data, which is used for reflecting the actual availability of the product field, and solves the problem that the existing pure theory calculation is not in line with the actual application;
furthermore, the service life evaluation model of the wear-type component can be used for checking the service life result of the wear-type component, and different statistical charts are generated, so that the model selection efficiency in product design is effectively improved;
furthermore, the invention can give out suggested maintenance measures for early warning faults and possible fault reasons, reduce maintenance and guarantee costs and reduce the strength of maintenance personnel; meanwhile, the method provides thinking for the combination and integration of the monitoring system for the fault early warning and the management of the electronic related equipment of the railway signal system.
Drawings
FIG. 1 is a flow chart of a method for implementing fault early warning and management of electronic related equipment of a railway signal system;
FIG. 2 is a flow chart of a study object such as a determination of a delay in clearing a passenger or getting off the vehicle;
FIG. 3 is a flow chart of a constructed fault logic model of the present invention;
FIG. 4 is a flow chart of the extraction of fault signature parameters of the present invention;
FIG. 5 is a partial input and output of fault data information according to the present invention;
FIG. 6 is a flow chart of a constructed fault assessment model of the present invention;
FIG. 7 is a flow chart of a relationship between build fault events and operating conditions in accordance with the present invention;
FIG. 8 is a flow chart of the wear-type component life assessment model construction of the present invention;
FIG. 9 is a flow chart of the present invention for constructing a repair measure versus failure;
FIG. 10 is a block diagram of the present invention for fault warning and management key factors in a monitoring and warning system.
Detailed Description
The technical scheme, constructional features, achieved objects and effects of the embodiments of the present invention will be described in detail below with reference to fig. 1 to 10 in the embodiments of the present invention.
It should be noted that, the drawings are in very simplified form and all use non-precise proportions, which are only used for the purpose of conveniently and clearly assisting in describing the embodiments of the present invention, and are not intended to limit the implementation conditions of the present invention, so that the present invention has no technical significance, and any modification of structure, change of proportion or adjustment of size, without affecting the efficacy and achievement of the present invention, should still fall within the scope covered by the technical content disclosed by the present invention.
It should be noted that, in the present invention, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention provides a method for realizing fault early warning and management of electronic related equipment of a railway signal system, as shown in figure 1, comprising the following steps: retrieving delay passenger clearing data and determining a fault mode causing passenger clearing operation delay; establishing a fault logic model, a fault evaluation model and a relation between a fault mode and an operation state and a relation between maintenance measures and the fault mode in a monitoring and early warning system; extracting fault characteristic parameters corresponding to the fault mode according to the relation among the fault logic model, the fault mode and the running state, monitoring the fault characteristic parameters, and judging whether the fault characteristic parameters are in a normal monitoring threshold value or not; and/or according to the fault evaluation model, obtaining the occurrence probability of the fault mode; giving different weights to fault characteristic parameters and occurrence probability corresponding to the fault mode in combination with key factors, and calculating early warning grades of the fault mode; and selecting a method for eliminating the fault early warning level according to the relation between the maintenance measure and the fault mode, and realizing early warning and management of the fault mode.
The key factors include environmental factors, elapsed time or frequency, fault characteristic values, alarm quantity and the like, as shown in fig. 10. Further, the environmental factors are that the environment participating in fault early warning management is determined according to the field data and the design data; the environmental factors comprise temperature, humidity, salt fog, dust and the like, and the advantages, the middle and the advantages of the environmental factors are determined according to whether the equipment has design data such as dust prevention, heat dissipation and the likeAnd (3) grading, and giving weights to different environmental factors. Still further, the weight omega of each key factor is determined by using expert experience method and historical data i And correcting and adjusting the weight omega of each key influence factor in the monitoring and early-warning system by combining the actual early-warning data of the monitoring and early-warning system i And the value is increased, so that the accuracy and the effectiveness of fault early warning are improved.
Wherein, as shown in fig. 2, the step of retrieving the delay clear data and determining the fault mode causing clear operation delay includes the following steps: s101, calling a delay clear-to-customer document of each year from each item management department; s102, screening fault influence fields in the delay clear document in S101, such as 'clear', 'drop car', 'delay' and the like; s103, calling a fault mode according to the fault influence field; s104, establishing a fault mode which causes delay of more than 5 minutes as a study object; further, the failure mode includes: ATP mode loss, inability to release emergency braking, inability to open doors, unsuccessful truck loading, vehicle crashes, inability to establish train mode, etc.
As shown in fig. 3, the building of the fault logic model in the monitoring and early warning system includes the following steps: s201, calling function design documents, namely calling the function design documents corresponding to the fault mode, such as a system function requirement document, a system architecture design document, a hardware design document, a software design document and the like which are related to the fault mode; s202, analyzing fault reasons for generating fault modes, and selecting fault trees or function-fault flows and other methods for analysis; s203, determining a logic relation between the fault cause and the fault mode, wherein the logic relation comprises OR logic, AND logic and the like; s204, analyzing a failure mechanism of a failure mode, namely analyzing a root cause and a failure mechanism of the failure in a deeper layer, wherein the step specifically analyzes a module circuit or a key chip; s205, calling design data, wherein the design data comprises a hardware schematic diagram and a software function corresponding to a fault mode; and constructing and completing a fault logic model through steps S201-S205, wherein the fault logic model corresponds each fault mode to the functional design document, the logic relation, the fault mechanism and the design data corresponding to each fault mode one by one so as to facilitate the extraction of the subsequent fault characteristic parameters.
Specifically, taking an example that the emergency braking can not release the fault mode, searching the occurrence reason and the fault mechanism by a fault tree mode, and obtaining the relation between an emergency braking function and a speed sensor, a backboard of a cabinet, a board card PSB for controlling the emergency braking, a safety input/output control board, a channel interface, a relay and the like based on a design document of the emergency braking, so that a fault logic model is established according to the function association relation.
Further, the building the fault logic model in the monitoring and early warning system further includes: step S206, determining monitored fault characteristic parameters such as impedance, current, voltage and the like; step S207, judging whether the fault characteristic parameters in the step S206 are monitored by a monitoring and early warning system; if the fault characteristic parameter is monitored, marking the fault characteristic parameter as monitored; if the fault characteristic parameter is not monitored, executing step S208; and step S208, the fault characteristic parameters which are not monitored are included in the monitoring range of the monitoring and early warning system.
Further, as shown in fig. 4, the step S206 of determining the monitored fault characteristic parameter includes the following steps:
s261, extracting fault characteristic parameters, including impedance, current, voltage and the like, as described in the step S206;
s262, judging whether the fault characteristic parameter extracted in the step S261 is an analog signal, and if the fault characteristic parameter is the analog signal, executing the step S263; otherwise, determining a fault characteristic parameter of the fault mode according to the relation between the fault mode and the running state; wherein the information represented by the continuously changing physical quantity is judged as an analog quantity; judging the output signal to be a digital signal when the output signal is 0/1;
s263, selecting an algorithm for extracting fault characteristic parameters; in an embodiment, if the fault characteristic parameter is irregular and the output is a discrete 0/1 signal, an ID3 algorithm or an Apriori algorithm may be selected; if the fault characteristic parameter is a current or voltage signal, a support vector machine algorithm can be selected for extracting and monitoring the fault characteristic parameter;
s264, embedding a normal monitoring threshold curve in the monitoring early warning system for judging fault characteristic parameters;
s265, if the fault characteristic parameter is in the normal monitoring threshold curve, ending the judgment of the fault characteristic parameter and incorporating the monitoring range of the monitoring early warning system; otherwise, if the normal monitoring threshold curve is exceeded, step S266 is performed;
s266, providing an early warning and maintenance strategy; if the fault characteristic parameters exceed the normal range, early warning or maintenance measures are given. The fault characteristic parameter is corrected by continuously learning through the algorithm selected in the step S263 according to the field application, so that the purpose of correcting the fault characteristic parameter model is achieved, and the purpose of improving early warning accuracy is achieved.
Further, as shown in fig. 6, the building of the fault evaluation model in the monitoring and early warning system includes the following steps:
s301, relevant fault data are called; the relevant fault data comprise field fault data obtained from CRM (field fault data statistics system), K2 (frequent fault record system), field fault event reporting platform, monitoring data, alarm data and maintenance data which are fetched from MSS (operation and maintenance system);
specifically, fig. 5 is a schematic diagram of partial input and output of relevant fault data information in the present invention, where fields included in the field fault data include a fault component, a fault phenomenon, a fault date, a fault number, an emergency braking amount, whether to cause ATP downtime of a train, an operation delay time (minutes), an environmental condition, and the like; the maintenance data comprises fault components, maintenance time (minutes) and maintenance measures; the monitoring data comprise the existing objects, fault codes, fault descriptions and the like monitored on the maintenance platform and are used for distinguishing whether the monitored objects are analog signals or digital signals; the alarm data comprises the contents of an object to be alarm, fault alarm contents, alarm reasons and the like on the maintenance platform.
S302, performing data cleaning and processing on the relevant fault data called in the step S301; the specific steps of data cleaning and processing include: s321, preparing a data format required by a preparation form; s322, eliminating missing values or invalid values in the field fault data; s323, supplementing data to form cleaned field fault data;
s303, manufacturing a fault form according to the relevant fault data after the cleaning processing in the step S302; the fault form comprises information such as fault time, fault sample size, environmental conditions, fault state failure or success and the like;
s304, initializing a parameter model; an accumulated fault probability model, such as weibull's 2-parameter model, can be established by using X-Rank Regression (RRX) or maximum likelihood method
Beta is a shape parameter, eta is a scale parameter, t is a working time), a 3-parameter model of weibull (a depictingthe model)>
Beta is a shape parameter, eta is a scale parameter, gamma is a position parameter), an exponential distribution model (F (t) =1-e -λt Lambda is failure rate, t is working time);
s305, fitting the parameter model selected in the step S304 according to the field fault data after the cleaning treatment; in one embodiment, 78 samples of a line collected by a fault assessment model of critical equipment are analyzed for 45048 hours of fault data
The occurrence probability of the key equipment at the time t can be predicted according to the time t of operation, a threshold probability of occurrence of faults can be set as an active maintenance condition, and when the threshold probability is lower than the threshold probability, the monitoring and early warning system sends an alarm signal to prompt operation and maintenance staff to prevent the occurrence of the faults;
s306, comparing the result of the parameter model after fitting in the step S305 with the result of a RAM monitoring platform (reliability, availability and maintainability platform); because the RAM monitoring platform is sample data obtained by monitoring the actual data, if the result of step S305 is close to the RAM monitoring platform result, step S307 is executed; if the result of the step S305 is different from the result of the RAM monitoring platform greatly, returning to the step S304, and reselecting the initialized parameter model;
s307, determining the parameter model after fitting in the step S305 as a fault evaluation model;
s308, embedding the fault evaluation model determined in the step S307 into a monitoring and early warning system to complete construction of the fault evaluation model in the monitoring and early warning system.
Further, the building the fault evaluation model in the monitoring and early warning system further includes step S309: manufacturing a repair form according to the maintainability data; the maintainability data comprises delay time, maintenance time and the like; based on the repair form, the corresponding delay time and the time required for maintenance can be directly obtained according to the fault mode, so that operation and maintenance personnel can make a maintenance plan in advance, and the maintenance efficiency is improved.
Fig. 7 is a schematic flow chart of a relationship between a fault event and an operation state constructed in a monitoring and early warning system, which includes the following steps:
s401, determining an alarm fault mode A;
s402, acquiring log data and acquiring a relevant state quantity field of an alarm failure mode A; because the log data contains relevant state quantity fields of possible fault information, the relevant state quantity fields can describe train running states at fault moment and some action state information related to train protection; for example, if a fault event is definitely abnormal in a certain characteristic parameter, but the fault event at the moment is insufficient to trigger an alarm, then at a certain moment, the fault event triggers another fault event, so that the alarm is caused, and ATP downtime of a train is possibly caused, and the train operation is influenced; for example, when the alarm failure mode is that the emergency brake cannot be released, the relevant state quantity fields which cannot be released by the emergency brake include the running state of a speed sensor in a system, the running state of a board card for controlling the emergency brake, the state of a safety input/output control board, the state of a backboard and the like;
s403, combing the link relation between the fault mode and the running state in the log data; specifically, the running state and characteristic parameter change of N minutes (N is more than or equal to 10) before the alarm fault mode A are searched, and the purpose is to search what characteristic parameter change causes the alarm fault mode A, so that the selection of the characteristic parameter of the fault is assisted.
In order to enable the online failure mechanism to be that the wear-out type equipment carries out the assessment and the early warning of the service life, the method for realizing the early warning and the management of the faults of the electronic related equipment of the railway signal system, provided by the invention, further comprises the following steps: and establishing a wear-type component life evaluation model in the monitoring and early warning system. Specifically, as shown in fig. 8, the method for establishing the wear-type component life assessment model includes the following steps:
s501, screening loss type failure mechanism components, such as a switch, a relay, an LED, a liquid aluminum electrolyte capacitor, an optocoupler, a flash, a connector, a battery and the like;
s502, determining design parameters such as durability indexes of the liquid aluminum electrolyte, rated erasing times of flash and the like by inquiring a design manual;
s503, determining practical application parameters, such as the number of plugging times of practical application, the current of an input end of an optocoupler, the current of an input end of an LED, the design erasing period of flash and the like;
s504, determining application environment factors such as temperature and the like;
s505, determining a life assessment model of the wear-out component, combining the specification and the actual online operation data to establish a model thereof, wherein Table 1 is a usable life assessment model of a part of the wear-out component:
TABLE 1 service life evaluation model of partially worn component
S506, embedding the wear-type component life assessment model determined in the step S505 into a monitoring and early warning system. The life of the loss type component is evaluated through the life evaluation model of the loss type component, early warning management can be carried out on the loss type component in advance, the problem that the loss type component fails when in use is effectively avoided, the failure rate is greatly reduced, and the operation delay is avoided.
Further, as shown in fig. 9, the construction of the relationship between the maintenance measure and the failure mode in the monitoring and early warning system includes the following steps:
s601, determining a fault mode list of fault early warning; namely, the fault mode list is obtained through the steps S101 to S104;
s602, retrieving a field 'field taken measure' in CRM and K2;
s603, a field 'field adopted measures' in a field fault event reporting platform is called, namely, the adopted field maintenance measures recorded in the current historical fault data are called;
s604, retrieving a field 'maintenance suggestion' in the alarm system, namely, some maintenance suggestions given to the early-warning fault mode, including but not limited to 'on-site measures' mentioned in the step S602 and the step S603;
s605, generating a fault mode and a maintenance measure form of fault early warning;
s606, judging whether a monitoring and early warning system exists in the relation between the fault and the maintenance measures, if so, not repeatedly adding the relation to the monitoring and early warning system, and if not, executing a step S607;
s607, embedding the generated fault mode and the maintenance measure form into the monitoring and early warning system. By inquiring the fault mode and the maintenance measure form in the monitoring and early warning system, maintenance measures can be quickly obtained when faults are early warned or occur, the fault processing speed is improved, the maintenance time is shortened, and the influence of overlong maintenance time on operation delay is avoided.
In summary, the invention provides a fault early warning and management construction method and a fault early warning and management construction process suitable for electronic related equipment of a railway signal system, and the method can early warn operation delay faults such as passenger clearing or vehicle dropping in advance, avoid line chain reaction caused by sudden faults or emergency accidents, reduce passenger clearing or delay fault rate more than 5 minutes, and improve passenger riding satisfaction;
further, the invention provides an evaluation model based on field fault data, which is used for reflecting the actual availability of the product field, and solves the problem that the existing pure theory calculation is not in line with the actual application;
furthermore, the service life evaluation model of the wear-type component can be used for checking the service life result of the wear-type component, and different statistical charts are generated, so that the model selection efficiency in product design is effectively improved;
furthermore, the invention can give out suggested maintenance measures for early warning faults and possible fault reasons, reduce maintenance and guarantee costs and reduce the strength of maintenance personnel; meanwhile, the method provides thinking for the combination and integration of the monitoring system for the fault early warning and the management of the electronic related equipment of the railway signal system.
While the present invention has been described in detail through the foregoing description of the preferred embodiment, it should be understood that the foregoing description is not to be considered as limiting the invention. Many modifications and substitutions of the present invention will become apparent to those of ordinary skill in the art upon reading the foregoing. Accordingly, the scope of the invention should be limited only by the attached claims.
Claims (17)
1. The method for realizing fault early warning and management of the electronic related equipment of the railway signal system is characterized by comprising the following steps:
retrieving delay passenger clearing data and determining a fault mode causing passenger clearing operation delay;
establishing a fault logic model, a fault evaluation model and a relation between a fault mode and an operation state and a relation between maintenance measures and the fault mode in a monitoring and early warning system;
extracting fault characteristic parameters corresponding to the fault mode according to the fault logic model and the relation between the fault mode and the running state, monitoring the fault characteristic parameters, and judging whether the fault characteristic parameters are in a normal monitoring threshold value or not;
and/or according to the fault evaluation model, obtaining the occurrence probability of the fault mode;
giving different weights to fault characteristic parameters and occurrence probability corresponding to the fault mode in combination with key factors, and calculating early warning grades of the fault mode;
and selecting a method for eliminating the fault early warning level according to the relation between the maintenance measure and the fault mode, and realizing early warning and management of the fault mode.
2. The method for implementing fault early warning and management of electronic related equipment in railway signal system as claimed in claim 1, wherein said retrieving delay clearing data, determining the fault mode causing clearing delay comprises the steps of:
s101, calling a delay clear-to-customer document of each year from each item management department;
s102, screening fault influence fields in the deferred visitor document in the step S101;
s103, calling a fault mode according to the fault influence field;
s104, establishing a fault mode which causes delay of more than M minutes as a research object, wherein M is more than or equal to 5.
3. The method for implementing fault early warning and management of electronic related equipment of railway signal system as claimed in claim 2, wherein,
the fault impact fields include "clear", "drop", "delay";
the fault modes comprise ATP mode loss, incapability of releasing emergency braking, incapability of opening a vehicle door, incapacity of starting a vehicle, vehicle-mounted crash and incapacity of establishing a train mode.
4. The method for implementing fault early warning and management of electronic related equipment in railway signal system according to claim 1, wherein the step of establishing a fault logic model in the monitoring early warning system comprises the following steps:
s201, retrieving a function design document, wherein the design document comprises a system related to a fault mode
System function requirements documents, system architecture design documents, hardware design documents, and software design documents;
s202, analyzing a fault reason for generating a fault mode, and selecting a fault tree or a function-fault flow method for analysis;
s203, determining a logic relation between the fault cause and the fault mode, wherein the logic relation comprises OR logic and AND logic;
s204, analyzing a failure mechanism of a failure mode, wherein the failure mechanism is specifically analyzed to a module circuit or a key chip;
s205, calling design data, wherein the design data comprises a hardware schematic diagram and a software function corresponding to the fault mode.
5. The method for implementing fault early warning and management of electronic related equipment in railway signal system according to claim 4, wherein the building of the fault logic model in the monitoring and early warning system further comprises:
s206, determining monitored fault characteristic parameters;
s207, judging whether the fault characteristic parameters in the step S206 are monitored by a monitoring and early warning system;
if the fault characteristic parameter is monitored, marking the fault characteristic parameter as monitored; if the fault characteristic parameter is not monitored, executing step S208;
and S208, incorporating the fault characteristic parameters which are not monitored into the monitoring range of the monitoring and early warning system.
6. The method for implementing fault early warning and management of electronic related devices in a railway signal system according to claim 5, wherein the determining monitored fault characteristic parameters in step S206 includes the following steps:
s261, extracting fault characteristic parameters, wherein the fault characteristic parameters comprise impedance, current and voltage;
s262, judging whether the fault characteristic parameter extracted in the step S261 is an analog signal, and if the fault characteristic parameter is the analog signal, executing the step S263; otherwise, determining a fault characteristic parameter of the fault mode according to the relation between the fault mode and the running state;
s263, selecting an algorithm for extracting fault characteristic parameters;
s264, embedding a normal monitoring threshold curve in the monitoring early warning system for judging fault characteristic parameters;
s265, if the fault characteristic parameter is in the normal monitoring threshold curve, ending the judgment of the fault characteristic parameter and incorporating the monitoring range of the monitoring early warning system; otherwise, if the normal monitoring threshold curve is exceeded, step S266 is performed;
s266, providing an early warning and maintenance strategy; if the fault characteristic parameters exceed the normal range, early warning or maintenance measures are given.
7. The method for implementing fault early warning and management of electronic related equipment of railway signal system according to claim 6, wherein if the fault characteristic parameter is irregular and the output is a discrete 0/1 signal, an ID3 algorithm or an Apriori algorithm is selected; and if the fault characteristic parameters are current or voltage signals, selecting a support vector machine algorithm for extracting and monitoring the fault characteristic parameters.
8. The method for implementing fault early warning and management of electronic related equipment in railway signal system according to claim 1, wherein the step of establishing a fault evaluation model in the monitoring early warning system comprises the following steps:
s301, relevant fault data are called;
s302, performing data cleaning and processing on the relevant fault data called in the step S301;
s303, manufacturing a fault form according to the relevant fault data after the cleaning processing in the step S302;
s304, initializing a parameter model;
s305, fitting the parameter model selected in the step S304 according to the relevant fault data after the cleaning treatment;
s306, comparing the result of the parameter model after fitting in the step S305 with the result of the RAM monitoring platform; if the result of step S305 is close to the RAM monitoring platform result, step S307 is executed; if the result of the step S305 is different from the result of the RAM monitoring platform greatly, returning to the step S304, and reselecting the initialized parameter model;
s307, determining the parameter model after fitting in the step S305 as a fault evaluation model;
s308, embedding the fault evaluation model determined in the step S307 into a monitoring and early warning system to complete construction of the fault evaluation model in the monitoring and early warning system.
9. The method for implementing fault early warning and management of electronic related equipment in railway signal system as claimed in claim 8, wherein the parameter model in step S304 includes an X-rank regression or maximum likelihood method to build up a cumulative fault probability model.
10. The method for implementing fault early warning and management of electronic related equipment in railway signal system as claimed in claim 8, wherein the parameter model comprises a weibull 2 parameter model
Beta is a shape parameter, eta is a scale parameter, and t is working time; weibull's 3 parameter model->
Beta is a shape parameter, eta is a scale parameter, and gamma is a position parameter; exponential distribution model F (t) =1-e -λt Lambda is the failure rate and t is the working time.
11. The method for implementing fault early warning and management of electronic related devices in a railway signal system according to claim 8, wherein the building of the fault evaluation model in the monitoring and early warning system further comprises step S309: manufacturing a repair form according to the maintainability data; the maintainability data includes delay time and maintenance time.
12. The method for implementing fault early warning and management of electronic related equipment in railway signal system according to claim 1, wherein the method for implementing the fault early warning and management of electronic related equipment in railway signal system is characterized in that a relation flow diagram of fault event and running state is constructed in a monitoring early warning system, and the method comprises the following steps:
s401, determining an alarm fault mode;
s402, acquiring log data and acquiring a relevant state quantity field of an alarm fault mode;
s403, combing the link relation between the fault mode and the running state in the log data; the operation state and characteristic parameter change N minutes before the alarm fault mode is searched for, and the selection of the characteristic parameter of the fault is assisted, wherein N is more than or equal to 10.
13. The method for implementing fault early warning and management of electronic related equipment in railway signal system according to claim 1, further comprising the steps of establishing a wear-out component life assessment model in a monitoring early warning system, and specifically comprising the following steps:
s501, screening a wear-out failure mechanism component;
s502, determining design parameters by inquiring a design manual;
s503, determining actual application parameters;
s504, determining application environment factors;
s505, determining a wear-type component life assessment model, and establishing a model by combining the specification and actual online operation data;
s506, embedding the wear-type component life assessment model determined in the step S505 into a monitoring and early warning system.
14. The method for implementing fault early warning and management of electronic related equipment of railway signal system according to claim 1, wherein the construction of the relationship between the maintenance measures and the fault mode in the monitoring early warning system comprises the following steps:
s601, determining a fault mode list of fault early warning;
s602, calling a field 'measures taken on site' in a field fault data statistics system and a frequent fault recording system;
s603, calling a field 'measures taken on site' in a site fault event reporting platform;
s604, retrieving a field 'maintenance suggestion' in the existing alarm system;
s605, generating a fault mode and a maintenance measure form of fault early warning;
s606, judging whether a monitoring and early warning system exists in the relation between the fault and the maintenance measures, if so, not repeatedly adding the relation to the monitoring and early warning system, and if not, executing a step S607;
s607, embedding the generated fault mode and the maintenance measure form into the monitoring and early warning system.
15. The method for implementing fault early warning and management of electronic related equipment of railway signal system according to claim 1, wherein the key factors comprise environmental factors, elapsed time or frequency, fault characteristic values and alarm quantity.
16. The method for implementing fault early warning and management of electronic related equipment of railway signal system according to claim 15, wherein the environmental factors include temperature, humidity, salt fog and dust, and the quality and quality of the environmental factors are determined according to whether the equipment has dust prevention and heat dissipation design data, and weights of different environmental factors are given.
17. The method for implementing fault early warning and management of electronic related equipment in railway signal system as claimed in claim 1, wherein the weight ω of each key factor is determined by expert experience method and historical data i And correcting and adjusting the weight omega of each key influence factor in the monitoring and early-warning system by combining the actual early-warning data of the monitoring and early-warning system i Values.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116934142A (en) * | 2023-07-14 | 2023-10-24 | 箭头租赁(广州)有限公司 | Comprehensive management method, system, equipment and medium for maintenance of leased IT equipment assets |
| CN117369392A (en) * | 2023-11-17 | 2024-01-09 | 岳阳长炼机电工程技术有限公司 | Equipment fault intelligent early warning method based on multiparameter logic relation |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116934142A (en) * | 2023-07-14 | 2023-10-24 | 箭头租赁(广州)有限公司 | Comprehensive management method, system, equipment and medium for maintenance of leased IT equipment assets |
| CN116934142B (en) * | 2023-07-14 | 2024-04-02 | 广州箭头信息科技有限公司 | Comprehensive management method, system, equipment and medium for maintenance of leased IT equipment assets |
| CN117369392A (en) * | 2023-11-17 | 2024-01-09 | 岳阳长炼机电工程技术有限公司 | Equipment fault intelligent early warning method based on multiparameter logic relation |
| CN117369392B (en) * | 2023-11-17 | 2024-04-16 | 岳阳长炼机电工程技术有限公司 | Equipment fault intelligent early warning method based on multiparameter logic relation |
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