CN111915026A - Fault processing method and device, electronic equipment and storage medium - Google Patents
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Abstract
The present disclosure provides a fault processing method, a fault processing apparatus, an electronic device and a computer-readable storage medium, which relate to the technical field of vehicle control, and the fault processing method includes: receiving fault sample data sent by a first service terminal and control parameters of a target object; acquiring reference parameters corresponding to the target object according to the control parameters, and determining a fault source by using the reference parameters and the fault sample data; determining a fault reason based on actual parameters in the fault sample data; and determining a maintenance scheme for the target object according to the fault source and the fault reason. The method and the device can accurately determine the maintenance scheme of the target object, and improve the processing efficiency.
Description
Technical Field
The present disclosure relates to the field of vehicle control technologies, and in particular, to a fault handling method, a fault handling apparatus, an electronic device, and a computer-readable storage medium.
Background
The electric wheel dumper is mainly applied to places such as open-pit coal mines and copper mines, and along with the increase of the application range, the maintenance and fault treatment process of the electric wheel dumper is more and more important.
In the related art, the control system of the electric wheel dump truck can only realize state monitoring according to the monitoring signal. When a fault occurs, the fault code can be inquired according to a maintenance manual only, the corresponding component is checked and tested, and finally, the test result and the fault are judged manually. The method greatly depends on the system knowledge and engineering experience of maintenance personnel, is easy to cause low operation and maintenance efficiency or generate wrong decisions, is difficult to judge maintenance time and provide a reasonable maintenance mode, and cannot implement an accurate maintenance scheme on a fault vehicle in time.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The present disclosure is directed to a method, an apparatus, an electronic device, and a computer-readable storage medium for handling a fault, so as to overcome, at least to some extent, a problem that a maintenance solution cannot be determined accurately in time due to limitations and defects of related art.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of the present disclosure, there is provided a fault handling method including: receiving fault sample data sent by a first service terminal and control parameters of a target object; acquiring reference parameters corresponding to the target object according to the control parameters, and determining a fault source by using the reference parameters and the fault sample data; determining a fault reason based on actual parameters in the fault sample data; and determining a maintenance scheme for the target object according to the fault source and the fault reason.
In an exemplary embodiment of the present disclosure, the obtaining a reference parameter corresponding to a target object according to the control parameter, and determining a fault source by using the reference parameter and the fault sample data includes: inputting the control parameters of the target object into a digital twin prediction model to simulate the reference parameters; and comparing the reference parameters with actual parameters in the fault sample data, and determining the fault source according to the comparison result.
In an exemplary embodiment of the present disclosure, determining a cause of a fault based on actual parameters in the fault sample data comprises: and dividing the characteristic parameters of the actual parameters in the fault sample data to obtain fault types, and determining fault reasons according to the fault types.
In an exemplary embodiment of the present disclosure, determining a cause of the fault according to the fault type includes: forming a fault tree according to the fault type; and on the fault tree, obtaining all fault paths from the judged fault types upwards by utilizing a fault tree search algorithm, and obtaining the fault reason according to the fault paths.
In an exemplary embodiment of the present disclosure, determining a repair scenario for the target object according to the failure source and the failure cause includes: adding intensity information to the fault path to obtain the probability of the fault reason; determining the maintenance scheme for the target object according to the fault source and the probability of the fault reason.
In an exemplary embodiment of the present disclosure, determining the repair scenario for the target object according to the failure source and the probability of the failure cause comprises: and determining a maintenance scheme according to the sequence of the probability from large to small and the reliability parameter of the target object.
According to an aspect of the present disclosure, there is provided a fault handling method including: acquiring actual parameters of a preset part of a target object, and forming sample data according to characteristic parameters in the actual parameters; clustering the characteristic parameters in the sample data to judge the use state of the target object; and determining fault sample data according to the use state, and sending the fault sample data and the control parameters of the target object to a second server so that the second server determines a maintenance scheme for the target object.
In an exemplary embodiment of the present disclosure, clustering the characteristic parameters in the sample data to determine the usage status of the target object includes: clustering the characteristic parameters by using a clustering algorithm to obtain distribution points of the characteristic parameters in a characteristic space; comparing the distribution points with envelope domains of characteristic parameters of a plurality of use states of a target object to judge the use state of the target object, and adding a label to the use state; wherein the use state comprises at least one of: a state of health, a state of performance degradation, and a state of failure.
According to an aspect of the present disclosure, there is provided a fault handling apparatus including: the data receiving module is used for receiving fault sample data sent by the first service end and control parameters of a target object; the source determining module is used for acquiring reference parameters corresponding to the target object according to the control parameters and determining a fault source by using the reference parameters and the fault sample data; the reason judging module is used for determining the fault reason based on the actual parameters in the fault sample data; and the scheme determining module is used for determining a maintenance scheme for the target object according to the fault source and the fault reason.
According to an aspect of the present disclosure, there is provided a fault handling apparatus including: the data acquisition module is used for acquiring actual parameters of a preset part of a target object and forming sample data according to characteristic parameters in the actual parameters; the state judgment module is used for clustering the characteristic parameters in the sample data so as to judge the use state of the target object; and the data sending module is used for determining fault sample data according to the use state and sending the fault sample data and the control parameters of the target object to a second server so that the second server determines a maintenance scheme for the target object.
According to an aspect of the present disclosure, there is provided an electronic device including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform any of the fault handling methods described above via execution of the executable instructions.
According to an aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a fault handling method as described in any one of the above.
In the fault processing method and apparatus, the electronic device, and the computer-readable storage medium provided in the present exemplary embodiment, the reference control parameter of the simulation object corresponding to the target object is obtained according to the control parameter of the target object, the fault source is determined by using the reference control parameter and the fault sample data, the fault cause is determined based on the characteristic parameter in the fault sample data, and the maintenance scheme for the target object is determined according to the fault source and the fault cause. On one hand, the fault source can be accurately determined through the reference control parameters of the simulation object corresponding to the target object and the fault sample data, the fault reason can be accurately determined according to the characteristic acceptance number in the fault sample data, the process of manually detecting a component according to a fault code is avoided, automatic intelligent fault monitoring can be achieved, the efficiency is improved, and the fault detection accuracy is improved. On the other hand, according to the accurate fault source and fault reason, the wrong decision and time delay caused by the knowledge limitation of personnel in the manual detection process can be avoided, and a more accurate and reasonable maintenance scheme can be provided in time; and the dependence on manpower is avoided, and the convenience of fault treatment is improved.
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 disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
Fig. 1 schematically illustrates a flow chart of a fault handling method in an exemplary embodiment of the present disclosure.
Fig. 2 is a block diagram schematically illustrating a structure for implementing a fault handling method in an exemplary embodiment of the present disclosure.
Fig. 3 schematically illustrates a flow chart for determining a fault source in an exemplary embodiment of the disclosure.
Fig. 4 schematically shows a specific flowchart for determining a cause of a fault in an exemplary embodiment of the present disclosure.
Fig. 5 schematically illustrates a flow chart of another fault handling method in an exemplary embodiment of the disclosure.
Fig. 6 schematically shows an overall flow chart of a fault handling method in an exemplary embodiment of the present disclosure.
Fig. 7 schematically illustrates a block diagram of a fault handling apparatus in an exemplary embodiment of the present disclosure.
Fig. 8 schematically illustrates a block diagram of another fault handling apparatus in an exemplary embodiment of the present disclosure.
Fig. 9 schematically illustrates a block diagram of a fault handling system in an exemplary embodiment of the present disclosure.
Fig. 10 schematically illustrates a schematic diagram of a computer-readable storage medium in an exemplary embodiment of the disclosure.
Fig. 11 schematically shows a schematic view of an electronic device in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. 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 devices and/or microcontroller devices.
In the exemplary embodiment, first, a fault handling method is provided, where an execution subject of the fault handling method is a second server. Referring to fig. 1, the fault handling method may include the steps of:
in step S110, receiving fault sample data sent by the first service end and a control parameter of the target object;
in step S120, obtaining a reference parameter corresponding to the target object according to the control parameter, and determining a fault source by using the reference parameter and the fault sample data;
in step S130, determining a failure cause based on actual parameters in the failure sample data;
in step S140, a maintenance plan for the target object is determined according to the failure source and the failure reason.
On one hand, the fault processing method provided in the exemplary embodiment can obtain the reference parameter by processing the control parameter of the target object, can accurately determine the fault source by combining the reference parameter and the fault sample data, and can accurately determine the fault reason according to the actual parameter in the fault sample data, thereby avoiding the process of manually detecting a component according to a fault code, realizing automatic intelligent fault detection, improving the efficiency and improving the accuracy of fault detection. On the other hand, according to the accurate fault source and fault reason, the wrong decision and time delay caused by the knowledge limitation of personnel in the manual detection process can be avoided, and a more accurate and reasonable maintenance scheme can be provided in time; and the dependence on manpower is avoided, and the convenience of fault treatment is improved.
Next, a failure processing method in the present exemplary embodiment will be described in detail with reference to the drawings.
In step S110, the failure sample data sent by the first service end and the control parameter of the target object are received.
In the present exemplary embodiment, the target object may be any type of object that needs to be detected, such as a mechanical vehicle or an equipment system, and the target object is exemplified as an electric wheel dump truck. The first server may be a vehicle-mounted server provided on the target object, that is, a vehicle-mounted server of the electric wheel dump truck itself. Referring to fig. 2, the first service end 21 includes an information acquisition terminal 211, a data preprocessing module 212, a CAN bus module 213, a vehicle-mounted end database 214, a vehicle-mounted wireless gateway module 215, a filtering and forwarding module 216, a fault triggering module 217, and a monitoring and displaying module 218. The vehicle-mounted server 21 is mainly used for acquiring parameter information of a preset component of the target object as an actual parameter, extracting fault sample data according to the parameter information, and further sending the fault sample data and the control parameter of the target object to the second server, so that the second server serves as an execution subject to determine a maintenance scheme for the target object. The preset components may be determined according to reliability, maintainability, safety, full life cycle cost, and the like, and may include, but are not limited to, a hydraulic cylinder, a hydraulic valve, and the like of the electric wheel dump truck, for example. The actual parameters of the preset component may be represented by characteristic parameters, and the characteristic parameters corresponding to the actual parameters include, but are not limited to, mathematical statistics characteristics, morphological characteristics, correlation parameters, spectrum information characteristics, and the like. For example, the actual parameters may be a1, B2, B3, B4.
The second server may be a ground server of a software platform connected to the vehicle server and installed on a computer, and with continued reference to fig. 2, the second server 22 may include the following modules: the system comprises a ground wireless gateway module 221, a display module 222, an expert database module 223, a fault statistic analysis module 224, a maintenance decision module 225, a fault prediction module 226 and a ground database module 227.
The following describes the process of implementing the fault handling method in detail with reference to the specific structure of the ground service end. Firstly, a ground wireless gateway module in a ground service end receives fault sample data and control parameters of a target object sent by a vehicle-mounted wireless gateway in a first service end, and sends the fault sample data and the control parameters of the target object to a ground end database in the ground service end. The failure sample data refers to a part of data in which a failure occurs among all sample data. The control parameters of the target object are also acquired by the first service end and sent to the second service end, and the control parameters specifically refer to control instructions and load values of the electric wheel dump truck, wherein the control instructions include but are not limited to forward, parking, backward and neutral control instructions of the electric wheel dump truck, lifting, descending, maintaining and floating control instructions, parking brake, emergency brake and loading brake control instructions; the load value refers to the maximum thermal load value allowed by the electric wheel dump truck.
In step S120, a reference parameter corresponding to the target object is obtained according to the control parameter, and a fault source is determined by using the reference parameter and the fault sample data.
In this exemplary embodiment, after obtaining the fault sample data and the control parameter, the fault prediction module in the second service end may simulate the target object according to the received control parameter and the fault sample data, to obtain a reference parameter of the target object, where the reference parameter may be a simulation parameter of a preset component that simulates the target object to obtain the target object, and the simulated reference parameter is the same as the type of an actual parameter obtained by the first service end, but a specific numerical value may be different or the same.
Further, a fault source corresponding to the fault sample data can be determined according to the reference parameters obtained by simulation and the fault sample data. Fig. 3 schematically shows a schematic diagram of determining a fault source, and referring to fig. 3, the method mainly includes step S310 and step S320, where:
in step S310, the control parameters of the target object are input to a digital twin prediction model to simulate the reference parameters.
In this step, the digital twin prediction model is a mapping of a real object in a virtual space to reflect the running state of the real object in the whole life cycle. The digital twin prediction model can deduce some indexes which cannot be directly measured originally by machine learning by collecting direct data of limited physical sensor indexes and by means of a large sample library. For example, for various fault characteristics occurring in the operation process of the equipment, the historical data of the sensor can be trained into digital characteristic models for different fault phenomena through machine learning, and the digital characteristic models are combined with records processed by experts to form the basis for accurately judging the fault state of the equipment in the future, so that the autonomous intelligent diagnosis and judgment are finally formed. In the exemplary embodiment, the control parameters of the target object may be input into the digital twin prediction model for simulation to obtain the reference parameters of the target object through simulation. For example, the reference parameters of the target object are B1, B2, B3, B4.
In step S320, the reference parameter is compared with the actual parameter in the fault sample data, and the fault source is determined according to the comparison result.
In this step, the reference parameters and the actual parameters in the fault sample data may be compared one by one to determine whether the reference parameters and the actual parameters are the same. If the comparison results are different, the fault source corresponding to the different reference parameters can be determined according to the different reference parameters. That is to say, the fault caused by which parameter can be known by comparing the virtual electric-wheel dump truck parameter with the acquired actual electric-wheel dump truck parameter, so that the fault source can be predicted and positioned. For example, if the reference parameter B1 is different from the actual parameter A1, the fault source can be determined to be the fault source 1 corresponding to the two parameters.
With continued reference to fig. 1, in step S130, a failure cause is determined based on actual parameters in the failure sample data.
In this exemplary embodiment, the expert library module of the second server may determine the cause of the fault according to actual parameters included in the fault sample data, and may specifically analyze the characteristic parameters corresponding to the actual parameters to accurately determine the cause of the fault. Specifically, the characteristic parameters of the actual parameters in the fault sample data may be divided to obtain fault types, and the fault cause may be determined according to the fault types. When the classification is carried out, the characteristic parameters in the fault sample data can be classified by utilizing a multivariate decision tree classification algorithm. A decision tree is a tree-like structure in which each internal node represents a test on an attribute, each branch represents a test output, and each leaf node represents a category. The classification using decision trees is divided into two steps: step 1, establishing and refining a decision tree by using a training set, and establishing a decision tree model. This process is actually a process of acquiring knowledge from data and performing machine learning. And 2, classifying the input data (characteristic parameters) by using the generated decision tree. And sequentially testing the attribute values of the characteristic parameters from the root node to a certain leaf node for the input characteristic parameters, thereby finding the class of the characteristic parameters. In this example, the feature parameters corresponding to the fault sample data may be input into a multivariate decision tree classification algorithm to obtain a classification of each feature parameter, so as to determine the fault type. For example, feature parameter 1 and feature parameter 2 belong to fault type 1, feature parameter 3 belongs to fault type 2, and feature parameter 4 belongs to fault type 3.
Further, the cause of the fault may be determined according to the judged fault type. One fault type may correspond to one or more fault causes, and multiple fault types may also correspond to one fault cause. Fig. 4 schematically shows a specific flowchart for determining the cause of the fault, and referring to fig. 4, the method mainly includes step S410 and step S420, where:
in step S410, a fault tree is formed according to the fault type;
in step S420, on the fault tree, all fault paths are obtained upwards from the determined fault types by using a fault tree search algorithm, and the fault cause is obtained according to the fault paths.
In the exemplary embodiment, the multivariate decision tree classification algorithm forms an initial fault tree by applying a recursive manner through a plurality of judgment conditions, for example, the initial fault tree can be obtained by finding out the events of the direct reasons from the top event step by step to the depth to be analyzed and according to the logical relationship. The fault tree can be a decision tree, and then the structure is simplified through a pruning method. The fault tree is a special inverted tree-like logical causal graph that describes causal relationships between various events in the system using event symbols, logic gate symbols, and transition symbols. The input event of a logic gate is the "cause" of the output event and the output event of the logic gate is the "effect" of the input event.
And the expert library module of the ground service terminal utilizes a fault tree search algorithm to summarize all possible fault paths from the currently judged fault types to the top on a fault tree, so that the fault reason is extracted according to the fault paths. For example, if the determined fault type is fault type 1, and a fault path obtained upward from fault type 1 on the fault tree is fault type 1-fault type 2-fault type 5, the fault cause can be obtained according to the fault path. It should be noted that one failure path may correspond to one failure cause.
With continued reference to fig. 1, in step S140, a maintenance plan for the target object is determined according to the failure source and the failure reason.
In the exemplary embodiment, after the fault source is obtained and the fault reason is determined, the maintenance decision module of the ground service terminal may analyze the fault prediction module to predict and locate the fault source, and provide a maintenance scheme in combination with the fault reason given by the expert database module. And the fault statistical analysis module receives the information of the maintenance decision module and counts fault phenomena and fault reasons.
In determining a repair plan for a target object according to a failure source and a failure cause, it is first necessary to calculate probabilities of various failure causes. Specifically, the strength information may be added to the failure path to obtain the probability of the failure cause. The strength information refers to a weight for describing the importance of a certain fault type, and may be represented by a numerical value, such as 0.5 or 0.8, and so on. Furthermore, each fault type included in each fault path can be multiplied by the corresponding strength information, and all fault types are combined to obtain the probability of the fault reason corresponding to the fault path.
Further, a repair scenario for the target object may be determined based on the source of the failure and the probability of the cause of the failure. Since there may be multiple causes of failure and the probability of each failure cause is different, multiple maintenance schemes may result. Specifically, when providing a maintenance plan, the maintainable decision module may provide a plurality of maintenance plans in order of the probability of the cause of the failure from large to small. Maintenance may be initiated, for example, from a maintenance scenario where the probability of the cause of the failure is large; whether the fault of the electric wheel dump truck disappears or not can be judged through the detection system in the next operation; if the fault is not disappeared, the fault is maintained according to the remaining fault reasons and maintenance schemes arranged at the second place, the third place and the like until the fault disappears, so that the real fault reason can be determined.
In addition, a repair scenario may be determined in conjunction with reliability parameters of the target object. The reliability parameters may include, for example, reliability and low cost, on the basis of which the maintenance schedule may be determined by the following equation (1):
p ═ maxf (E, C) formula (1)
Wherein, P is the benefit of the electric wheel dumper, E is the benefit of the electric wheel dumper for transporting goods, and C is the maintenance cost of the electric wheel dumper.
Based on the method, the curve of the benefit of the electric wheel dump truck changing along with the time can be calculated through the benefit and the maintenance cost generated by the fault operation of the electric wheel dump truck, and therefore the optimal maintenance time can be determined. By combining the reliability parameters and the probability of the fault reason, the maintenance opportunity and a more appropriate maintenance scheme can be accurately determined, the problem of maintenance error caused by manually determining the fault reason is avoided, and the maintenance accuracy and effectiveness are improved.
And then, the fault statistical analysis module receives the information of the maintenance decision module, and statistics fault phenomena and fault reasons are sent to a ground server database. And the fault phenomenon and the fault reason are counted and sent to a ground database, and designers comprehensively analyze the fault phenomenon and the fault reason, so that support is provided for continuous optimization of subsequent products such as equipment design, manufacturing process improvement and the like.
In this exemplary embodiment, another fault handling method is further provided, where an execution subject of the fault handling method is a first server, and referring to fig. 5, the fault handling method mainly includes the following steps:
in step S510, actual parameters of a preset portion of the target object are acquired, and sample data is formed according to the characteristic parameters in the parameter information.
In the exemplary embodiment, the preset components may be determined according to reliability, maintainability, safety, full life cycle cost, etc., and may include, but are not limited to, hydraulic cylinders, hydraulic valves, etc. of the electric wheel dump truck, for example. The actual parameters of the preset component may be represented by characteristic parameters, and the characteristic parameters corresponding to the actual parameters include, but are not limited to, mathematical statistics characteristics, morphological characteristics, correlation parameters, spectrum information characteristics, and the like.
After the actual parameters are obtained, sample data including all the actual parameters can be formed according to the characteristic parameters in the actual parameters. The information acquisition terminal in the first service end acquires actual parameters of components of the electric wheel dump truck in real time, and the data preprocessing module extracts characteristic parameters in the actual parameters to form sample data which are stored in a database of the vehicle-mounted service end in real time.
In step S520, clustering the characteristic parameters in the sample data to determine the usage status of the target object.
In the present exemplary embodiment, the use state of the electric wheel dump truck may be identified by analyzing the sample data. The using state of the electric wheel dumper comprises any one of a healthy state, a performance degradation state and a fault state, and labels representing the healthy state, the performance degradation state and the fault state are added to sample data. Specifically, the method can comprise the following steps: the method comprises the following steps of firstly, clustering the characteristic parameters by using a clustering algorithm to obtain distribution points of the characteristic parameters in a characteristic space. The clustering algorithm can be any one of a partition method, a hierarchical method, a density-based method, a grid-based method, a model-based method and a K-means clustering algorithm. After the clustering is completed, the distribution points of the feature parameters in the feature space can be obtained, for example, the feature parameter 1 is distributed at the point a, and the feature parameter 3 is distributed at the point D. And step two, comparing the distribution points with envelope domains of characteristic parameters of a plurality of using states of the target object to judge the using state of the target object. Wherein the usage state of the target object comprises one of: a state of health, a state of performance degradation, and a state of failure. The envelope domain refers to a range of regions where the distribution points of the characteristic parameters of each usage state are located. For example, the envelope domain of the healthy state is a1, the envelope domain of the performance degradation state is B2, the envelope domain of the fault state is C3, and so on. Each use state can be represented by a corresponding label, the label can be represented by a number, and can also be represented by words or letters and the like. For example, a healthy state is labeled 0, a degraded state is labeled 1, a failed state is labeled 2, and so on.
For example, if the point a of the distribution of the feature parameter 1 is within the range of the envelope domain C3, the target object corresponding to the feature parameter 1 may be considered to be in a fault state, and the label is 2. In this example, clustering can be accurately performed through a clustering algorithm, and then the use state of the target object can be accurately judged, so as to accurately monitor the target object.
In the exemplary embodiment, a clustering algorithm is adopted to analyze various characteristic parameters in sample data, obtain distribution points of the characteristic parameters in a multi-dimensional characteristic space, and compare the distribution points with characteristic parameter envelope domains of the health state and the fault state of the electric wheel dump truck to judge the health state, the performance degradation state and the fault state of the electric wheel dump truck. Due to the multi-dimensional parameter characteristics, the traditional electric wheel dump truck monitoring system is prevented from adopting the threshold value of a single sensor parameter as a fault alarm condition, the fault false alarm rate is reduced, and the accuracy rate is improved.
In step S530, determining fault sample data according to the usage status, and sending the fault sample data and the control parameter of the target object to a second server, so that the second server determines a maintenance scheme for the target object.
In the present exemplary embodiment, since the failure processing is performed, sample data belonging to the use state of the failure tag may be used as failure sample data to be processed. Further, a CAN bus module of the vehicle-mounted service end acquires control parameters of a target object in real time, and specifically, a gear shifting handle 235 signal of the electric wheel dump truck determines control instructions of forward movement, parking, backward movement and neutral position of the electric wheel dump truck; collecting signals of a lifting handle 236 of the electric wheel dump truck to determine lifting, descending, keeping and floating control instructions of the electric wheel dump truck; the acquisition brake button 233 determines the parking brake, emergency brake, and loading brake control commands of the electric wheel dump truck; the load value of the electric wheel dump truck is acquired by the electric wheel dump truck weighing system 234, and the control instruction and the load value of the electric wheel dump truck are stored in the vehicle-mounted end database. Next, the filtering and forwarding module in the vehicle-mounted server is responsible for compressing and encrypting the fault sample data added with the fault state label in the vehicle-mounted database, the fault sample data, the control instruction and the load value of the electric wheel dump truck in the same time period, and then sending the compressed and encrypted data to the vehicle-mounted wireless gateway module, and sending the data to the ground server through the vehicle-mounted wireless gateway module. In the process that the vehicle-mounted wireless gateway module sends the control instruction to the ground server, the filtering and forwarding module can compress sample data in a vehicle-mounted database, encrypt the control instruction by adopting an MD5 algorithm or other algorithms, send the control instruction to the vehicle-mounted wireless gateway module by adopting an MQTT (Message Queuing Telemetry Transport) protocol, and send the control instruction to the ground server through the vehicle-mounted wireless gateway, so that the data transmission efficiency is improved through data compression, and the security of data transmission is realized through encryption. In the process, the monitoring display module can read out the current data from the vehicle-mounted end database in real time to display the running state of the electric wheel dumper, can also read out fault data in the vehicle-mounted end database, and selects multivariate query, display and playback.
The ground wireless gateway module receives fault sample data sent by the vehicle-mounted wireless gateway and control instructions and load values of the electric wheel dumper in the same time period and sends the fault sample data and the control instructions and the load values to the ground end database, so that the ground service end can determine a maintenance scheme for a target object according to the fault sample data and the control parameters.
In the fault processing method shown in fig. 5, the first server acquires actual parameters of a preset part of the target object in real time, forms sample data according to characteristic parameters of the actual parameters, then clusters the sample data, can accurately classify all the sample data, determines the use state of the target object, and obtains more accurate fault sample data according to the use state. Furthermore, the fault sample data and the control parameters of the target object can be sent to the second server side, so that the second server side can timely and accurately determine a maintenance scheme for the target object, and efficiency and convenience are improved. Moreover, the method in fig. 5 avoids the problem that the detection system of the electric-wheel dump truck in the related art cannot timely acquire the related information and the operation data of the electric-wheel dump truck, so that the field data support cannot be provided for the development and the improvement of the subsequent vehicle.
It is added that the data preprocessing module in the vehicle-mounted server can preprocess the sample data. The preprocessing process includes at least one of deleting extraneous or duplicate data in the original data set, smoothing noise data, filtering out data that is not relevant to subsequent applications, processing missing or outliers, and the like. The data loss mainly includes loss of recorded sample data and loss of certain field information in the recorded sample data. The method for processing the missing value comprises the following steps: deleting the record with missing value, interpolating possible value and not processing 3 modes. The interpolation method comprises any one of the following methods: (1) mean/median/mode interpolation; (2) using a fixed value; (3) nearest neighbor interpolation; (4) a regression method; (5) interpolation. Outliers are values that deviate significantly from the rest of the observations. The method for processing the abnormal value comprises two steps of abnormal value analysis and processing, wherein the abnormal value analysis comprises the following steps: (1) simple statistic analysis; (2)3 sigma principle; (3) and (5) analyzing the box type graph. The abnormal value processing comprises the following steps: (1) deleting records containing outliers; (2) the value is regarded as the missing value; (3) and (6) correcting the average value. By preprocessing the sample data, the problem of inaccurate sample data caused by data loss, data abnormity and other conditions is avoided, the quality of the sample data can be improved, and an accurate maintenance scheme can be obtained based on the high-quality sample data.
Fig. 6 schematically illustrates an overall flow chart of the fault handling method in the exemplary embodiment of the present disclosure, and referring to fig. 6, the method mainly includes the following steps S601 to S609, where:
in step S601, the system is powered on.
In step S602, the control command is stored in the vehicle-mounted database.
In step S603, it is determined whether or not there is a failure. If yes, go to step S604; if not, go to step S602.
In step S604, the fault sample data is sent to the ground server database.
In step S605, the fault source is located according to the digital twin fault prediction module.
In step S606, the cause of the failure is determined according to the type of the failure.
In step S607, a repair scenario is determined based on the source and cause of the fault.
In step S608, it is determined whether the failure is eliminated. If yes, go to step S609; if not, go to step S602.
In step S609, the fault phenomenon and the fault cause are counted and sent to the ground server database.
Through the steps in fig. 6, the cause of the fault can be accurately determined, and a relatively accurate maintenance scheme can be determined.
In the present exemplary embodiment, there is provided a fault handling apparatus, as shown in fig. 7, the fault handling apparatus 700 including:
a data receiving module 701, configured to receive fault sample data sent by a first service end and a control parameter of a target object;
a source determining module 702, configured to obtain a reference parameter corresponding to a target object according to the control parameter, and determine a fault source by using the reference parameter and the fault sample data;
a cause determining module 703, configured to determine a cause of the fault based on actual parameters in the fault sample data;
a scheme determining module 704, configured to determine a maintenance scheme for the target object according to the failure source and the failure cause.
In the present exemplary embodiment, there is also provided another fault handling apparatus, and referring to fig. 8, the fault handling apparatus 800 includes:
the data acquisition module 801 may be configured to acquire actual parameters of a preset portion of a target object, and form sample data according to characteristic parameters in the actual parameters;
the state determining module 802 may be configured to cluster the characteristic parameters in the sample data to determine the use state of the target object;
the data sending module 803 may be configured to determine fault sample data according to the usage status, and send the fault sample data and the control parameter of the target object to a second server, so that the second server determines a maintenance scheme for the target object.
It should be noted that the specific details of each module in the fault handling apparatus have been described in detail in the corresponding fault handling method, and therefore are not described herein again.
In the present exemplary embodiment, there is also provided a fault handling system, and referring to fig. 9, the fault handling system 900 may include:
the first server 901 is configured to collect actual parameters of a preset part of a target object, determine a use state of the target object according to the actual parameters, and send fault sample data determined by the use state and control parameters of the target object to a second server;
and a second service end 902, communicatively connected to the first service end, configured to determine a maintenance scheme for the target object according to the fault sample data and the control parameter of the target object.
Specific components of the first server and the second server are shown in fig. 2, and functions of the components have been described in detail in the fault handling method, so that details are not described here.
In the present exemplary embodiment, it should be noted that although several modules or units of the apparatus for action execution are mentioned in the above detailed description, such division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above section "exemplary methods" of the present description, when said program product is run on the terminal device.
Referring to fig. 10, a program product 1000 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a 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.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or 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.
A computer readable signal medium may include a propagated data signal with readable program code 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 readable signal medium may also be any readable medium that is not a 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.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 1100 according to this embodiment of the invention is described below with reference to fig. 11. The electronic device 1100 shown in fig. 11 is only an example and should not bring any limitations to the function and the scope of use of the embodiments of the present invention.
As shown in fig. 11, electronic device 1100 is embodied in the form of a general purpose computing device. The components of the electronic device 1100 may include, but are not limited to: the at least one processing unit 1110, the at least one memory unit 1120, and a bus 1130 that couples various system components including the memory unit 1120 and the processing unit 1110.
Wherein the storage unit stores program code that is executable by the processing unit 1110 to cause the processing unit 1110 to perform steps according to various exemplary embodiments of the present invention as described in the above section "exemplary methods" of the present specification. For example, the processing unit 1110 may perform the steps as shown in fig. 1 or fig. 5.
The storage unit 1120 may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM)11201 and/or a cache memory unit 11202, and may further include a read only memory unit (ROM) 11203.
The display unit 1140 may be a display having a display function to show a processing result obtained by the processing unit 1110 performing the method in the present exemplary embodiment through the display. The display includes, but is not limited to, a liquid crystal display or other display.
The electronic device 1100 may also communicate with one or more external devices 1200 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 1100, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 1100 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 1150. Also, the electronic device 1100 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet) via the network adapter 1160. As shown, the network adapter 1160 communicates with the other modules of the electronic device 1100 over the bus 1130. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 1100, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is to be limited only by the terms of the appended claims.
Claims (12)
1. A method of fault handling, comprising:
receiving fault sample data sent by a first service terminal and control parameters of a target object;
acquiring reference parameters corresponding to the target object according to the control parameters, and determining a fault source by using the reference parameters and the fault sample data;
determining a fault reason based on actual parameters in the fault sample data;
and determining a maintenance scheme for the target object according to the fault source and the fault reason.
2. The method according to claim 1, wherein obtaining a reference parameter corresponding to a target object according to the control parameter, and determining a fault source by using the reference parameter and the fault sample data comprises:
inputting the control parameters of the target object into a digital twin prediction model to simulate the reference parameters;
and comparing the reference parameters with actual parameters in the fault sample data, and determining the fault source according to the comparison result.
3. The method of claim 1, wherein determining a cause of the fault based on the actual parameters in the fault sample data comprises:
and dividing the characteristic parameters of the actual parameters in the fault sample data to obtain fault types, and determining fault reasons according to the fault types.
4. The fault handling method of claim 1, wherein determining a cause of the fault based on the fault type comprises:
forming a fault tree according to the fault type;
and on the fault tree, upwards obtaining all fault paths from the judged fault types by utilizing a fault tree search algorithm, and obtaining the fault reasons according to the fault paths.
5. The fault handling method of claim 1, wherein determining a repair scenario for the target object based on the source of the fault and the cause of the fault comprises:
adding intensity information to the fault path to obtain the probability of the fault reason;
determining the maintenance scheme for the target object according to the fault source and the probability of the fault reason.
6. The fault handling method of claim 5, wherein determining the repair scenario for the target object based on the fault source and the probability of the fault cause comprises:
and determining a maintenance scheme according to the sequence of the probability from large to small and the reliability parameter of the target object.
7. A method of fault handling, comprising:
acquiring actual parameters of a preset part of a target object, and forming sample data according to characteristic parameters in the actual parameters;
clustering the characteristic parameters in the sample data to judge the use state of the target object;
and determining fault sample data according to the use state, and sending the fault sample data and the control parameters of the target object to a second server so that the second server determines a maintenance scheme for the target object.
8. The fault handling method according to claim 7, wherein clustering the characteristic parameters in the sample data to determine the usage status of the target object comprises:
clustering the characteristic parameters by using a clustering algorithm to obtain distribution points of the characteristic parameters in a characteristic space;
comparing the distribution points with envelope domains of characteristic parameters of a plurality of use states of a target object to judge the use state of the target object, and adding a label to the use state; wherein the use state comprises at least one of: a state of health, a state of performance degradation, and a state of failure.
9. A fault handling device, comprising:
the data receiving module is used for receiving fault sample data sent by the first service end and control parameters of a target object;
the source determining module is used for acquiring reference parameters corresponding to the target object according to the control parameters and determining a fault source by using the reference parameters and the fault sample data;
the reason judging module is used for determining the fault reason based on the actual parameters in the fault sample data;
and the scheme determining module is used for determining a maintenance scheme for the target object according to the fault source and the fault reason.
10. A fault handling device, comprising:
the data acquisition module is used for acquiring actual parameters of a preset part of a target object and forming sample data according to characteristic parameters in the actual parameters;
the state judgment module is used for clustering the characteristic parameters in the sample data so as to judge the use state of the target object;
and the data sending module is used for determining fault sample data according to the use state and sending the fault sample data and the control parameters of the target object to a second server so that the second server determines a maintenance scheme for the target object.
11. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the fault handling method of any of claims 1-8 via execution of the executable instructions.
12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the fault handling method according to any one of claims 1 to 8.
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