CN113868599A - Locomotive fault analysis method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application provides a locomotive fault analysis method, a locomotive fault analysis device, electronic equipment and a storage medium, wherein the method comprises the following steps: determining a first failure rate of each part of a target locomotive according to the locomotive operation failure data; obtaining a second failure rate of each part of the target locomotive by performing reliability distribution on the target locomotive; performing accident tree analysis on a target locomotive according to a preset top event to obtain a middle event and a bottom event, determining the fault rate of the bottom event based on the first fault rate of each part, and determining the fault rate of the middle event based on the fault rate of the bottom event; and determining the operation fault condition of each part of the target locomotive according to the first fault rate and the second fault rate of each part of the target locomotive, the fault rate of the bottom event and the fault rate of the middle event. According to the technical scheme of the embodiment of the application, the failure rate of the parts of the locomotive is analyzed from a plurality of angles, so that the weak links of the locomotive are accurately positioned, and reference data are provided for the optimization direction of the locomotive.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a locomotive fault analysis method and device, electronic equipment and a storage medium.
Background
With the rapid development of railroads, the technology of locomotives is also continuously improved. However, as the locomotive operating time passes, the probability of failure greatly increases; therefore, the fault analysis of the locomotive is particularly important, and the purpose of optimizing the locomotive according to the analysis result to make preventive measures and improve the performance of the locomotive is achieved by carrying out the fault analysis of the locomotive in the process of after-sale application of product design.
At present, fault analysis methods in locomotive production are all analyzed by using a single tool, such as a reliability technology widely applied in locomotive production, and in essence, reliability refers to the property that a product does not have a fault during the use, the reliability analysis method provides a basis for fault analysis, correction measures taken by research and judging whether a product meets an index requirement or not by carrying out reliability investigation, analysis and evaluation on a locomotive, but is disjointed from fault data collected in the actual application process of the locomotive, and the reliability analysis mostly depends on test data obtained under different test conditions for analysis, so that the fault of the locomotive in the actual application cannot be truly and accurately reflected, an accurate locomotive fault analysis result cannot be given, weak parts in the locomotive cannot be accurately positioned, and the optimization direction of the locomotive deviates or is wrong, the purpose of improving the reliability of the locomotive cannot be achieved.
Disclosure of Invention
In order to solve the above technical problem, embodiments of the present application provide a method and an apparatus for analyzing a locomotive fault, an electronic device, and a computer-readable storage medium, which perform fault analysis on a locomotive from multiple directions and combine actual fault data of the locomotive to obtain an operation fault condition of each component of the locomotive.
Other features and advantages of the present application will be apparent from the following detailed description, or may be learned by practice of the application.
According to an aspect of an embodiment of the present application, there is provided a locomotive fault analysis method, including: determining a first failure rate of each part of a target locomotive according to the locomotive operation failure data; obtaining a second failure rate of each part of the target locomotive by performing reliability distribution on the target locomotive; performing accident tree analysis on the target locomotive according to a preset top event to obtain a middle event and a bottom event, determining the fault rate of the bottom event based on the first fault rate of each part, and determining the fault rate of the middle event based on the fault rate of the bottom event; and determining the operation fault condition of each part of the target locomotive according to the first fault rate and the second fault rate of each part of the target locomotive, the fault rate of the bottom event and the fault rate of the middle event.
In an embodiment, the method further comprises: extracting the fault mode and the fault reason of each part according to the fault description of the target locomotive; determining a fault influence relation between each part based on the fault mode and the fault reason; and obtaining the fault-prone parts of the target locomotive by comparing the second fault rate of each part of the target locomotive with the fault influence relation.
In one embodiment, after the obtaining of the fault-prone component of the target locomotive by comparing the second fault rate of each component of the target locomotive with the fault influence relationship, the method further includes: acquiring a fault mode and a fault influence relation corresponding to the fault-prone part; and optimizing the fault-prone parts according to the fault mode and the fault influence relation corresponding to the fault-prone parts.
In one embodiment, the determining a first failure rate of each component of the target locomotive based on the locomotive operation failure data includes: acquiring the operation fault data of the target locomotive and the operation fault data of other locomotives of the same type as the target locomotive, and preprocessing the acquired operation fault data; constructing a structure tree for representing the assembly relation among all parts of the target locomotive; and mapping the preprocessed operation fault data to the structure tree, and calculating a first fault rate of each part according to the operation fault data mapped by the structure tree.
In one embodiment, the obtaining a second failure rate of each component of the target locomotive by performing reliability assignment on the target locomotive includes: acquiring a reliability index of the target locomotive; dividing the target locomotive into a plurality of functional systems, and respectively distributing system indexes to the functional systems according to the reliability indexes, wherein each functional system comprises at least one part; and calculating a second failure rate of the parts in the corresponding functional systems according to the system indexes distributed by the functional systems.
In one embodiment, the performing an accident tree analysis on the target locomotive according to a preset top event to obtain a middle event and a bottom event, determining a failure rate of the bottom event based on a first failure rate of each component, and determining a failure rate of the middle event based on the failure rate of the bottom event includes: determining a fault condition of the target locomotive according to the top event, wherein the fault condition comprises that a fault occurs or the fault does not occur; determining the fault occurrence probability of each part of the target locomotive according to the fault condition, and determining the intermediate event and the bottom event according to the fault occurrence probability of each part of the target locomotive.
In one embodiment, the determining an operational failure condition of each component of the target locomotive based on the first and second failure rates of each component of the target locomotive, the failure rate of the base event, and the failure rate of the intermediate event includes: respectively sequencing the first fault rate, the second fault rate, the fault rate of the bottom event and the fault rate of the middle event of each part from large to small to correspondingly obtain a first sequence, a second sequence, a bottom event sequence and a middle event sequence; and respectively acquiring fault rate information of preset ranks in the first sequence, the second sequence, the bottom event sequence and the middle event sequence, and determining corresponding parts according to the acquired fault rate information.
According to an aspect of an embodiment of the present application, there is provided a locomotive failure analysis apparatus including: the first fault rate acquisition module is configured to determine a first fault rate of each part of the target locomotive according to the locomotive operation fault data; the second failure rate acquisition module is configured to perform reliability distribution on the target locomotive to obtain a second failure rate of each part of the target locomotive; the fault rate acquisition module of the middle event and the fault rate acquisition module of the bottom event are configured to perform fault tree analysis on the target locomotive according to a preset top event to obtain the middle event and the bottom event, determine the fault rate of the bottom event based on the first fault rate of each part, and determine the fault rate of the middle event based on the fault rate of the bottom event; and the locomotive running condition acquisition module is configured to determine the running fault condition of each part of the target locomotive according to the first fault rate and the second fault rate of each part of the target locomotive, the fault rate of the bottom event and the fault rate of the middle event.
According to an aspect of an embodiment of the present application, there is provided an electronic device including one or more processors; storage means for storing one or more computer programs which, when executed by the one or more processors, cause the electronic device to implement the data processing method as described above.
According to an aspect of an embodiment of the present application, there is provided a computer-readable storage medium having stored thereon computer-readable instructions which, when executed by a processor of a computer, cause the computer to execute a locomotive fault analysis method as described above.
According to an aspect of embodiments herein, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions are read by a processor of the computer device from a computer readable storage medium, and the computer instructions are executed by the processor to cause the computer device to perform the locomotive fault analysis method provided in the various alternative embodiments described above.
In the technical scheme provided by the embodiment of the application, the locomotive operation fault data in practical application is combined, fault analysis is carried out on parts of the locomotive from multiple aspects, so that the operation fault condition of each part of the target locomotive is determined, and the parts with higher fault rates in the actual operation process can be accurately positioned according to the operation fault condition of each part, so that more accurate fault analysis result data can be obtained, and a reference direction can be provided for reliability optimization of the locomotive.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
FIG. 1 is a schematic illustration of an implementation environment to which the present application relates;
FIG. 2 is a flow chart illustrating a method of locomotive fault analysis in accordance with an exemplary embodiment of the present application;
FIG. 3 is a flow chart illustrating a method of locomotive fault analysis in accordance with another exemplary embodiment of the present application;
FIG. 4 is a flowchart of step S210 in an exemplary embodiment of the embodiment shown in FIG. 2;
FIG. 5 is a flowchart of step S230 in an exemplary embodiment of the embodiment shown in FIG. 2;
FIG. 6 is a flowchart of step S270 in an exemplary embodiment of the embodiment shown in FIG. 2;
FIG. 7 is a schematic diagram of a locomotive fault analysis device shown in an exemplary embodiment of the present application;
FIG. 8 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It should also be noted that: reference to "a plurality" in this application means two or more. "and/or" describe the association relationship of the associated objects, meaning that there may be three relationships, e.g., A and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Referring first to fig. 1, fig. 1 is a schematic diagram of an implementation environment related to the present application. The implementation environment includes a terminal 100 and a server 200, and the terminal 100 and the server 200 communicate with each other through a wired or wireless network. The terminal 100 is configured to collect locomotive data, and input the collected locomotive data to the server 200, where the locomotive data includes basic conditions such as a type, a structure, a specific model, an application environment, and the like of a locomotive, and operation fault data of different types of locomotives; after the locomotive data is collected, the server 200 may determine a target locomotive, perform fault analysis on the target locomotive, send a result of the fault analysis to the terminal 100, and visually display the analysis result through a display module of the terminal 100.
For example, after receiving the locomotive data, the terminal 100 sends the locomotive data to the server 200, and the server 200 determines to perform fault analysis on the target locomotive a: determining a first failure rate of each part of the target locomotive according to locomotive operation failure data in the locomotive data, and obtaining a second failure rate of each part of the target locomotive by performing reliability distribution on the target locomotive; performing accident tree analysis on a target locomotive according to a preset top event to obtain a middle event and a bottom event, determining the fault rate of the bottom event based on the first fault rate of each part, and determining the fault rate of the middle event based on the fault rate of the bottom event; and determining the operation fault condition of each part of the target locomotive according to the first fault rate and the second fault rate of each part of the target locomotive, the fault rate of the bottom event and the fault rate of the middle event.
The terminal 100 may be any electronic device capable of implementing data visualization, such as a smart phone, a tablet, a notebook, and a computer, and is not limited in this respect. The server 200 may be an independent physical server, or may be a server cluster or a distributed system formed by a plurality of physical servers, where the plurality of servers may form a block chain, and the server is a node on the block chain, and the server 200 may also be a cloud server providing basic cloud computing services such as cloud service, cloud database, cloud computing, cloud function, cloud storage, Network service, cloud communication, middleware service, domain name service, security service, CDN (Content Delivery Network ), big data, and artificial intelligence platform, which is not limited herein.
FIG. 2 is a flow chart illustrating a locomotive fault analysis method according to an exemplary embodiment. As shown in fig. 2, in an exemplary embodiment, the method may include steps S210 to S270, which are described in detail as follows:
step S210: a first failure rate of each component of the target locomotive is determined based on the locomotive operational failure data.
In this embodiment, a target locomotive that needs to be subjected to fault analysis is determined, and if a new eight-axle locomotive of the HXD2 series needs to be optimized, the new eight-axle locomotive is set as the target locomotive to be subjected to fault analysis. Specifically, firstly, a large amount of locomotive operation fault data is acquired, where the fault data may be operation fault data generated by a fault occurring in a certain part of a target locomotive, or operation fault data generated by a fault occurring in some functional systems (a plurality of parts constitute functional systems, such as an engine system and a brake system), or fault data generated by a fault occurring in a whole locomotive, and the operation fault data is subjected to fault rate analysis and calculation to acquire a first fault rate of each part and functional system of the target locomotive.
In some exemplary embodiments, the acquired locomotive operation fault data may further include operation fault data of a locomotive of the same type as the target locomotive, and a type and a reason of the locomotive of the same type as the target locomotive are similar to those of the target locomotive when a fault occurs, so that the operation fault data of the locomotive of the same type as the target locomotive may be acquired, and of course, operation fault data of other types of locomotives related to a certain type of fault may also be acquired (for example, the target locomotive is prone to a braking fault, and data related to a braking fault in the operation fault data of other types of locomotives may be acquired), and the operation fault data acquired in this case may be acquired according to actual needs, which is not limited in this embodiment.
Step S230: and obtaining a second failure rate of each part of the target locomotive by performing reliability distribution on the target locomotive.
Reliability allocation means a process of reasonably allocating a predetermined reliability index to each component constituting a system in a product design stage, and as a result, a basis and a goal of reliability prediction, and a reliability prediction relative result is a basis of reliability allocation and index adjustment.
In this embodiment, performing reliability allocation on the target locomotive refers to obtaining a second failure rate of each component according to a known failure rate related index, for example, the reliability allocation may be a failure rate provided by each component supplier, and the provided failure rate is used as the second failure rate of the corresponding component; or for a reliability index of the whole target locomotive, distributing the reliability index of the target locomotive to a functional system or each part of the target locomotive to obtain a second failure rate of the functional system or each part, wherein the second failure rate can finally meet the reliability index of the target locomotive; of course, in other methods, the second failure rate may not be fully assigned to all of the components of the target locomotive when the second failure rate assignment is made based on the reliability indicator, and the second failure rate for components or functional systems not assigned to the second failure rate may be obtained from a failure rate provided by the supplier or may be predicted from data of the components or functional systems in actual operation.
Step S250: and performing accident tree analysis on the target locomotive according to a preset top event to obtain a middle event and a bottom event, determining the fault rate of the bottom event based on the first fault rate of each part, and determining the fault rate of the middle event based on the fault rate of the bottom event.
The accident tree analysis is to identify and evaluate the dangerousness of various systems by using logical reasoning, and not only can analyze the direct cause of an accident, but also can deeply reveal the potential cause of the accident. In the accident tree analysis, an analysis object, namely a top event, is firstly determined, then the top event is used as a result, the accident tree is compiled according to the analysis purpose from the result to the cause until the cause event is inseparable, so that a bottom event is determined, and an intermediate event can link the bottom event and the top event in the accident.
In this step, a top event may be preset for the target locomotive, where the top event may be a fault determined according to the type of the target locomotive, for example, when the target locomotive is an internal combustion locomotive, the top event may be set as a fire; when the target locomotive is a floating train, the top event of the target locomotive can be set to be derailment and the like; in addition, the top event may also be a failure determination based on differences in functional systems within the target locomotive, such as for the engine of the target locomotive, the top event may be a shutdown; for the braking system of the target locomotive, the top event may be a brake failure, etc., and is not limited herein; the specific top event can be determined according to different needs, and the top event is not unique.
After the top event is determined, performing accident tree analysis on the target locomotive according to the top event to obtain a middle event and a bottom event under the top event, and specifically, determining the fault condition of the target locomotive or a functional system in the target locomotive according to the top event, wherein the fault condition comprises the occurrence of a fault or the non-occurrence of a fault; and the uncertain events are not considered temporarily, the probability of various faults is analyzed according to the logic sequence of the fault situation, and the middle event and the bottom event are determined according to the probability of the fault of each part of the target locomotive.
The method comprises the steps that a plurality of top events obtain middle events and bottom events under the corresponding top events, and the obtained bottom events are failure rates of a target locomotive or a minimum part in a functional system corresponding to one top event; if the fire of the engine of the target locomotive is taken as a top event, the accident tree analysis is carried out to obtain a bottom event and a middle event, and which bottom event is the set of the failure rates of all parts in the engine system.
Since the bottom event obtained in this embodiment is a failure rate set of each component in the target locomotive, in this step, the first failure rate of each component in step S210 is assigned to a corresponding bottom event as an occurrence probability corresponding to the bottom event (failure rate of the bottom event), that is, the first failure rates of each component are weighted and compared to obtain a failure rate of the bottom event corresponding to each component, and if the first failure rate of a certain component a in the target locomotive is obtained as a without performing S210, and the bottom event B obtained after performing fault tree analysis on a certain top event in step S250 (occurrence probability of the bottom event B is that of the component a), the first failure rate of the component a is assigned to the bottom event B, and the first failure rate is a as the occurrence probability of the bottom event B.
Through the method, the failure rate corresponding to the bottom event is determined through the first failure rate of each part, and since the bottom event is obtained through the analysis of the accident tree, the middle events and the top events corresponding to the bottom event (one top event can correspond to a plurality of middle events, and one middle event can correspond to a plurality of bottom events) can be obtained by upward presumption according to the accident trees corresponding to different bottom events, so that the failure rate of the middle events in the accident tree can be obtained according to the failure rates of all the bottom events in the accident tree, and the failure rate of the top event in the accident tree can be obtained through the probability of the middle events.
Step S270: and determining the operation fault condition of each part of the target locomotive according to the first fault rate and the second fault rate of each part of the target locomotive, the fault rate of the bottom event and the fault rate of the middle event.
In this embodiment, after the first failure rate and the second failure rate of each component of the target locomotive, the failure rate of the bottom event, and the failure rate of the middle event are obtained, analysis and comparison can be performed from each failure rate data, for example, the component which is easy to fail is obtained from the failure rate data, or the failure condition of each component which is easy to fail, and the like, and the target locomotive can be optimized according to the obtained failure condition data, for example, for the component with the higher failure rate, the reliability of the component is improved by performing improved design on the component, the failure rate is reduced, and the like; further, as for a component which is likely to have a failure, a replacement of a component having the same function is performed, and the like, and the method of performing optimization based on the failure is not particularly limited.
The order between steps S210-S250 in this embodiment may be exchanged, but step S210 needs to precede step S250.
The embodiment provides a locomotive fault analysis method, which analyzes the fault rate of a target locomotive from a plurality of angles, obtains a first fault rate by using actual operation data, obtains a second fault rate by using reliability analysis, obtains the fault rate of a bottom event and the fault rate of an intermediate event by using accident tree analysis, on the basis of analyzing the failure rate of the target locomotive parts by the failure rate test, the failure analysis of the target locomotive is assisted by the actual failure data, thereby more accurately determining the components which are easy to fail and the possible failure conditions among the components of the target locomotive, the failure condition of each part obtained by the method can also provide reference data for the optimized direction of the target locomotive, for example, the plurality of fault rate data are integrated, the fault-prone parts are analyzed, the parts are optimized, the reliability of the parts is improved, the fault rate is reduced, and the like.
FIG. 3 is a flow chart illustrating a locomotive fault analysis method according to another exemplary embodiment. As shown in fig. 3, in an exemplary embodiment, the method may include steps S310 to S350, which are described in detail as follows:
step S310: and extracting the fault mode and the fault reason of each part according to the fault description of the target locomotive.
In this embodiment, the fault description is a name of a fault that may occur in the target locomotive, such as a fault description of the target locomotive when the fault occurs, for example, a fire, a steering failure, etc., and the fault description may be determined according to a fault that is likely to be caused in actual operation of the target locomotive.
After determining the fault description, decomposing the functional system related to the fault description into a plurality of parts or one part according to the fault description, wherein the fault of the decomposed plurality of parts or one part can cause the fault description to occur; if the fault description of the steering failure indicates that the system causing the steering failure can be many, determining a functional system related to the steering failure, namely a steering system and the like according to the analysis of the steering failure, then decomposing the steering system into one or more parts, namely a rotating shaft, a universal joint and the like in the steering system, and decomposing the steering system into the parts related to the steering failure according to the fault description of the steering failure, namely that the rotating shaft breaks down to cause the steering failure or the universal joint breaks down to cause the steering failure and the like; of course, this is only a single example, and other fault descriptions and other fault-described functional systems may also be used, and are not limited specifically here.
In this embodiment, after the functional system is decomposed into one or more components according to the fault description, the fault mode and the fault cause of the corresponding one or more components are analyzed according to the fault description information; specifically, as described for the failure of steering failure, the corresponding component may be a universal joint, a steering shaft, a bending or sinking of the universal joint, a fracture of the steering shaft, etc., which may cause the steering failure, and for the component of the universal joint, the bending or sinking may be a failure mode of the universal joint, and the fracture may be a failure mode of the steering shaft, and meanwhile, for each of the disassembled components, the cause of the failure mode is analyzed, such as the bending of the universal joint is caused by collision or friction of the rotating shaft, etc.
Through the method, the fault descriptions are decomposed and analyzed, and finally the fault description and the fault reason of each part of the target locomotive can be obtained; of course, for the parts that are not analyzed, the failure mode and the failure cause of the corresponding part may be obtained through a failure mode library of FEMA (failure mode and effect analysis), in other embodiments, the failure mode and the failure cause of each part may also be obtained through the failure mode library instead of the failure description, and the obtaining method is not particularly limited herein.
Step S330: and determining a fault influence relation between the parts based on the fault mode and the fault reason.
In this embodiment, after the failure mode and the failure cause of each component are obtained, the failure influence relationship of each component is analyzed according to the failure mode and the failure cause of each component, where the failure influence relationship includes the failure impression of each component on itself and the failure influence on the component of the previous or next stage, if the failure mode on a certain component a is a break, and the failure cause is a collision between a component B and a component a, the failure influence on the component on itself can be obtained as a break, and the influence on the next stage may be that the component a breaks to cause a certain functional system or the target locomotive to fail to operate normally, and the influence of the component a on the component B may cause the component B not to be transmitted to other components, and so on, which is only an example, but also may be other components according to the failure mode and the failure cause of the component a, The influence of the fault cause obtained by the component a on some components, the previous process and the subsequent process is not specifically limited here.
Step S350: and obtaining the fault-prone parts of the target locomotive by comparing the second fault rate of each part of the target locomotive with the fault influence relation.
In this embodiment, after the second failure rate and the failure influence relationship of each component are obtained, which component is likely to fail may be determined according to the second failure rate and the failure influence relationship, or the component is likely to have a large-scale influence on other components or functional systems after the failure occurs, and the component that is likely to fail may be determined according to the data, so as to provide reference data for the optimization direction of the target locomotive.
For example, the fault rate of the parts can be determined to be higher according to the magnitude of the second fault rate, the fault influence relationship is determined to determine that the faults of the parts easily cause the faults of other parts, and then the parts which are easy to fault of the target locomotive can be optimized by combining the second fault rate and the fault influence relationship, for example, the parts with the higher second fault rate are optimized or replaced, the reliability is improved, or the parts with the larger fault influence are structurally improved, so that the fault influence is reduced while the fault rate is reduced.
In this embodiment, after obtaining the component easy to fail, the component easy to fail may be optimized according to the failure mode, the failure influence relationship, and the failure cause corresponding to the component easy to fail, for example, for the component easy to fail a, the failure mode is concave, the failure cause is friction with the component B, and the failure influence relationship is that the component B cannot transmit, an optimization scheme is formulated according to the failure mode, the failure influence relationship, and the failure cause of the component easy to fail a, for example, a scheme such as changing a material of the component easy to fail A, B to reduce friction between two components is changed, so as to improve reliability of the component easy to fail a. Of course, only one optimization scheme is mentioned above, and in practical application, an optimization scheme meeting requirements can be formulated for the fault-prone component according to a fault mode corresponding to the fault-prone component, a fault influence relationship, and a fault reason, and no specific limitation is made here.
In the embodiment, the fault description of the target locomotive is analyzed, the easily-faulted parts are accurately positioned, reference data are provided for the optimization direction of the target locomotive, meanwhile, the optimization direction of the easily-faulted parts can be accurately and efficiently obtained according to the fault mode, fault reason and fault influence relation of each part extracted from the fault description, and the direction is provided for the optimization scheme of the easily-faulted parts, so that the purpose of reducing and increasing the fault rate and fault influence of the easily-faulted parts is achieved, and the effect of improving the reliability of the target locomotive is finally achieved.
Fig. 4 is a flowchart of step S210 in an exemplary embodiment in the embodiment shown in fig. 2. As shown in FIG. 4, in an exemplary embodiment, the process of determining a first failure rate of each component of the target locomotive based on the locomotive operation failure data in step S210 may include steps S410 to S450, as detailed below:
step S410: the method comprises the steps of obtaining operation fault data of a target locomotive and operation fault data of other locomotives of the same type as the target locomotive, and preprocessing the obtained operation fault data.
In this embodiment, the category of the target locomotive is determined, and the operation fault data is different for different categories of locomotives, so that the operation fault data may be collected according to the category of the target locomotive, such as collecting actual operation fault data of the target locomotive and operation fault data of the same category as the target locomotive, and then preprocessing the collected operation fault data.
In this embodiment, preprocessing the operation failure data must respect the original data, and the data processing is performed according to the requirements of normalization, correctness and completeness instead of directly modifying and deleting the original data, so as to remove the repeated data, correct the error data, complement the missing data and normalize the messy data; for example: the specification of the train number, the specification of the failure date, the reason category of the system and the like.
Step S430: and constructing a structure tree for representing the assembly relation among the parts of the target locomotive.
In this embodiment, after the operation fault data is normalized, the structure tree of the functional system and the component of the target locomotive is determined, and the structure tree is obtained by analyzing the target locomotive to the minimum replaceable unit according to the assembly relationship between the functional system and the component.
Step S450: and mapping the preprocessed operation fault data to the structure tree, and calculating a first fault rate of each part according to the operation fault data mapped by the structure tree.
In this embodiment, the preprocessed operation fault data is mapped to the structure tree, and since the structure tree is analyzed to the minimum replaceable unit of the target locomotive (i.e., the component of the target locomotive), the first fault rate of each component can be calculated according to the structure tree and the operation fault data.
In the embodiment, the actual operation fault data related to the target locomotive is obtained, the structure tree is constructed, and the first fault rate of each part of the target locomotive can be accurately obtained through the actual operation fault data.
Fig. 5 is a flowchart of step S230 in an exemplary embodiment in the embodiment shown in fig. 2. As shown in fig. 5, in an exemplary embodiment, the step S230 of obtaining the second failure rate of each component of the target locomotive by performing reliability assignment on the target locomotive may include steps S510 to S550, which are described in detail as follows:
step S510: and acquiring the reliability index of the target locomotive.
In this embodiment, a reliability index of the target locomotive is first obtained, where the reliability index may be a target reliability when the target locomotive is optimized, may also be a reliability index provided by a supplier, and may also be an index obtained by analyzing reliability data of the same category of the target locomotive, and the reliability index may be set according to an actual requirement, and is not specifically limited herein.
Step S530: and dividing the target locomotive into a plurality of functional systems, and respectively distributing system indexes to the functional systems according to the reliability indexes, wherein each functional system comprises at least one part.
In this embodiment, the target locomotive is divided into a plurality of functional modules according to different functions, for example, the target locomotive is divided into a braking module, a steering module, a braking module, and the like according to functions. And then distributing the reliability indexes to different functional modules, wherein the reliability indexes can be distributed according to the importance weights of the functional modules in the distribution process.
Step S550: and calculating a second failure rate of the parts in the corresponding functional systems according to the system indexes distributed by the functional systems.
In this embodiment, the different functional systems generally include one or more components, and after the functional systems obtain the allocated system index, the second failure rates corresponding to the components in the functional systems are predicted according to the system index, so that the second failure rates of the components in one functional system together satisfy the system index of the functional system.
In the embodiment, the second fault rate of each part in the target locomotive is calculated and obtained according to the triggering of the reliability index of the target locomotive, the fault rate of the part of the target locomotive can be obtained under the condition that the target locomotive meets the reliability requirement, the fault analysis of the target locomotive is realized, and the part with higher fault rate can be positioned according to the second fault rate to be optimized, so that the reliability of the target locomotive is improved.
Fig. 6 is a flowchart of step S270 in an exemplary embodiment in the embodiment shown in fig. 2. As shown in fig. 6, in an exemplary embodiment, the step S270 of determining the operation failure condition of each component of the target locomotive according to the first failure rate and the second failure rate of each component of the target locomotive, the failure rate of the bottom event and the failure rate of the middle event may include steps S610 to S650, which are described in detail as follows:
step S610: and respectively sequencing the first fault rate, the second fault rate, the fault rate of the bottom event and the fault rate of the middle event of each part from large to small to correspondingly obtain a first sequence, a second sequence, a bottom event sequence and a middle event sequence.
In this embodiment, the first failure rate, the second failure rate, the failure rate of the bottom event, and the failure rate of the middle event of each component are sorted from large to small to obtain a first sequence, a second sequence, a bottom event sequence, and a middle event sequence, where the failure rates in all the sequences correspond to a certain component.
Step S630: respectively acquiring fault rate information of preset ranks in the first sequence, the second sequence, the bottom event sequence and the middle event sequence, and determining corresponding parts according to the acquired fault rate information.
In this embodiment, data in the first sequence, the second sequence, the bottom event sequence, and the middle event sequence are compared, and failure rate information of a preset rank in each sequence or failure rate information of a failure rate greater than a certain value in each sequence, for example, failure rate information of top 5 of the rank in each sequence, may be obtained, and according to failure rate information of preset ranks in different sequences, corresponding parts may be obtained, and the parts may have higher failure rates, and may be optimized.
When the obtained fault rate information of the preset rank in each sequence has corresponding same parts, for example, the fault rate of the part a in the preset rank in the first sequence and the fault rate of the part a in the preset rank in the second sequence are also high, the fault rate and the frequency of the fault of the part a are high, the part a can be optimized in a key mode when the target locomotive is optimized, and the reliability of the part a is improved, of course, in other embodiments, when the fault rate information obtained in each sequence is different, the fault condition of the target locomotive can be determined by adopting other analysis modes; the optimization method can refer to fig. 3 to fig. 4, and specifically, the parts corresponding to the fault rate information of the preset rank can be optimized through the fault mode, the fault influence relationship and the fault reason.
In the embodiment, the fault rates obtained by different methods are sorted from large to small, the fault rate information with higher fault rate obtained by the fault rates under different analysis results is synthesized, and the parts corresponding to the fault information with higher fault rate are optimized, so that the reliability of the target locomotive is improved.
Fig. 7 is a schematic structural diagram illustrating a locomotive failure analysis device according to an exemplary embodiment.
As shown in fig. 7, in an exemplary embodiment, the locomotive failure analysis device includes:
a first failure rate obtaining module 710 configured to determine a first failure rate of each component of the target locomotive according to the locomotive operation failure data;
the second failure rate obtaining module 730 is configured to obtain a second failure rate of each part of the target locomotive by performing reliability distribution on the target locomotive;
the middle event fault rate and bottom event fault rate obtaining module 750 is configured to perform fault tree analysis on the target locomotive according to a preset top event to obtain a middle event and a bottom event, determine a fault rate of the bottom event based on a first fault rate of each component, and determine a fault rate of the middle event based on the fault rate of the bottom event;
the locomotive operation condition obtaining module 770 is configured to determine an operation fault condition of each component of the target locomotive according to the first fault rate and the second fault rate of each component of the target locomotive, the fault rate of the bottom event, and the fault rate of the middle event.
In this embodiment, through the locomotive fault analysis device with the above structure, the operation fault of the target locomotive can be analyzed, the fault rate of each part of the target locomotive is obtained from a plurality of different modules, and the accuracy of the fault analysis of the target locomotive is improved.
In one embodiment, the locomotive fault analysis device further comprises:
the fault mode and fault reason extraction module is configured to extract the fault mode and fault reason of each part according to the fault description of the target locomotive;
the fault influence relation acquisition module is configured to determine a fault influence relation between each part based on a fault mode and a fault reason;
and the fault-prone part acquisition module is configured to obtain the fault-prone parts of the target locomotive by comparing the second fault rates of the parts of the target locomotive with the fault influence relationship.
In one embodiment, the locomotive fault analysis device further comprises:
the fault mode and fault influence relation acquisition module is configured to acquire a fault mode and fault influence relation corresponding to the fault-prone part;
and the fault-prone part optimizing module is configured to optimize the fault-prone part according to the fault mode and the fault influence relation corresponding to the fault-prone part.
In one embodiment, the first failure rate obtaining module 710 includes:
the preprocessing unit is configured to acquire the operation fault data of the target locomotive and the operation fault data of other locomotives of the same type as the target locomotive, and preprocess the acquired operation fault data;
the structure tree construction unit is configured to construct a structure tree for representing the assembly relation among all parts of the target locomotive;
and the first fault rate acquisition unit is configured to map the preprocessed operation fault data to the structure tree and calculate the first fault rate of each part according to the operation fault data mapped by the structure tree.
In an embodiment, the second failure rate obtaining module 730 includes:
a reliability index acquisition unit configured to acquire a reliability index of a target locomotive;
the system comprises an index distribution unit, a reliability index distribution unit and a reliability index distribution unit, wherein the index distribution unit is configured to divide a target locomotive into a plurality of functional systems and distribute system indexes for the functional systems respectively according to the reliability index, and each functional system comprises at least one part;
and the second failure rate acquisition unit is configured to calculate a second failure rate of the parts in the corresponding functional system according to the system indexes distributed by the functional systems.
In one embodiment, the intermediate event failure rate and bottom event failure rate obtaining module 750 includes:
the fault condition determining unit is configured to determine a fault condition of the target locomotive according to the top event, wherein the fault condition comprises the occurrence of the fault or the non-occurrence of the fault;
and the middle event fault rate and bottom event fault rate acquisition unit is configured to determine the fault occurrence probability of each part of the target locomotive according to the fault condition, and determine a middle event and a bottom event according to the fault occurrence probability of each part of the target locomotive.
In one embodiment, the locomotive operating condition acquisition module 770 includes:
the sequencing unit is configured to sequence the first fault rate, the second fault rate, the fault rate of the bottom event and the fault rate of the middle event of each part from large to small respectively to obtain a first sequence, a second sequence, a bottom event sequence and a middle event sequence correspondingly;
the locomotive running condition acquisition unit is configured to respectively acquire fault rate information of preset ranks in the first sequence, the second sequence, the bottom event sequence and the middle event sequence, and determine corresponding parts according to the acquired fault rate information.
FIG. 8 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.
It should be noted that the computer system 1600 of the electronic device shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 8, the computer system 1600 includes a Central Processing Unit (CPU)1601 which can perform various appropriate actions and processes, such as executing the methods in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 1602 or a program loaded from a storage portion 1608 into a Random Access Memory (RAM) 1603. In the RAM 1603, various programs and data necessary for system operation are also stored. The CPU 1601, ROM 1602, and RAM 1603 are connected to each other via a bus 1604. An Input/Output (I/O) interface 1605 is also connected to the bus 1604.
The following components are connected to the I/O interface 1605: an input portion 1606 including a keyboard, a mouse, and the like; an output portion 1607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage portion 1608 including a hard disk and the like; and a communication section 1609 including a Network interface card such as a LAN (local area Network) card, a modem, or the like. The communication section 1609 performs communication processing via a network such as the internet. The driver 1610 is also connected to the I/O interface 1605 as needed. A removable medium 1611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 1610 as necessary, so that a computer program read out therefrom is mounted in the storage portion 1608 as necessary.
In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method illustrated by the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via the communication portion 1609, and/or installed from the removable media 1611. When the computer program is executed by a Central Processing Unit (CPU)1601, various functions defined in the system of the present application are executed.
It should be noted that the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM), a flash Memory, an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with a computer program embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
Yet another aspect of the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a locomotive fault analysis method as before. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist separately without being incorporated in the electronic device.
Another aspect of the application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions are read by a processor of the computer device from a computer-readable storage medium, and the computer instructions are executed by the processor to cause the computer device to perform the locomotive fault analysis method provided in the various embodiments described above.
The above description is only a preferred exemplary embodiment of the present application, and is not intended to limit the embodiments of the present application, and those skilled in the art can easily make various changes and modifications according to the main concept and spirit of the present application, so that the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A locomotive fault analysis method is characterized by comprising the following steps:
determining a first failure rate of each part of a target locomotive according to the locomotive operation failure data;
obtaining a second failure rate of each part of the target locomotive by performing reliability distribution on the target locomotive;
performing accident tree analysis on the target locomotive according to a preset top event to obtain a middle event and a bottom event, determining the fault rate of the bottom event based on the first fault rate of each part, and determining the fault rate of the middle event based on the fault rate of the bottom event;
and determining the operation fault condition of each part of the target locomotive according to the first fault rate and the second fault rate of each part of the target locomotive, the fault rate of the bottom event and the fault rate of the middle event.
2. The method of claim 1, further comprising:
extracting the fault mode and the fault reason of each part according to the fault description of the target locomotive;
determining a fault influence relation between each part based on the fault mode and the fault reason;
and obtaining the fault-prone parts of the target locomotive by comparing the second fault rate of each part of the target locomotive with the fault influence relation.
3. The method of claim 2, wherein after the obtaining of the fault-prone component of the target locomotive by comparing the second fault rate of the component of the target locomotive with the fault impact relationship, the method further comprises:
acquiring a fault mode and a fault influence relation corresponding to the fault-prone part;
and optimizing the fault-prone parts according to the fault mode and the fault influence relation corresponding to the fault-prone parts.
4. The method of claim 1, wherein determining a first failure rate for each component of the target locomotive based on the locomotive operational failure data comprises:
acquiring the operation fault data of the target locomotive and the operation fault data of other locomotives of the same type as the target locomotive, and preprocessing the acquired operation fault data;
constructing a structure tree for representing the assembly relation among all parts of the target locomotive;
and mapping the preprocessed operation fault data to the structure tree, and calculating a first fault rate of each part according to the operation fault data mapped by the structure tree.
5. The method of claim 1, wherein said deriving a second failure rate for each component of the target locomotive by reliability assignment for the target locomotive comprises:
acquiring a reliability index of the target locomotive;
dividing the target locomotive into a plurality of functional systems, and respectively distributing system indexes to the functional systems according to the reliability indexes, wherein each functional system comprises at least one part;
and calculating a second failure rate of the parts in the corresponding functional systems according to the system indexes distributed by the functional systems.
6. The method of claim 1, wherein performing an accident tree analysis on the target locomotive according to a preset top event to obtain a middle event and a bottom event, determining a failure rate of the bottom event based on a first failure rate of each component, and determining a failure rate of the middle event based on the failure rate of the bottom event comprises:
determining a fault condition of the target locomotive according to the top event, wherein the fault condition comprises that a fault occurs or the fault does not occur;
determining the fault occurrence probability of each part of the target locomotive according to the fault condition, and determining the intermediate event and the bottom event according to the fault occurrence probability of each part of the target locomotive.
7. The method of claim 1, wherein determining an operational fault condition for each component of the target locomotive based on the first and second fault rates for each component of the target locomotive, the fault rate for the base event, and the fault rate for the intermediate event comprises:
respectively sequencing the first fault rate, the second fault rate, the fault rate of the bottom event and the fault rate of the middle event of each part from large to small to correspondingly obtain a first sequence, a second sequence, a bottom event sequence and a middle event sequence;
and respectively acquiring fault rate information of preset ranks in the first sequence, the second sequence, the bottom event sequence and the middle event sequence, and determining corresponding parts according to the acquired fault rate information.
8. A locomotive failure analysis device, comprising:
the first fault rate acquisition module is configured to determine a first fault rate of each part of the target locomotive according to the locomotive operation fault data;
the second failure rate acquisition module is configured to perform reliability distribution on the target locomotive to obtain a second failure rate of each part of the target locomotive;
the fault rate acquisition module of the middle event and the fault rate acquisition module of the bottom event are configured to perform fault tree analysis on the target locomotive according to a preset top event to obtain the middle event and the bottom event, determine the fault rate of the bottom event based on the first fault rate of each part, and determine the fault rate of the middle event based on the fault rate of the bottom event;
and the locomotive running condition acquisition module is configured to determine the running fault condition of each part of the target locomotive according to the first fault rate and the second fault rate of each part of the target locomotive, the fault rate of the bottom event and the fault rate of the middle event.
9. An electronic device, comprising:
one or more processors;
storage means for storing one or more computer programs that, when executed by the one or more processors, cause the electronic device to implement the method of any of claims 1-7.
10. A computer-readable storage medium having computer-readable instructions stored thereon, which, when executed by a processor of a computer, cause the computer to perform the method of any one of claims 1-7.
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