CN111159893A - Fault recurrence and prediction system based on digital twinning - Google Patents
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Abstract
本发明公开了一种基于数字孪生的故障复现及预测系统。该系统包括：虚拟化模块、OPC UA模型模块、关系模型模块、故障预测模块、虚拟化场景显示模块、历史数据库和故障回放模块；虚拟化模块用于虚拟化实际的设备实体；设备实时的数据可以在虚拟化场景中进行显示；在虚拟化场景中可以回放设备的运行画面，从而追溯设备故障的原因；传入的实际数据信息会被保存到历史数据库中，根据历史数据库中的历史数据信息，得到关系模型，根据关系模型和实时的数据来预测故障的发生。本发明能够显示设备的运行状态，并对设备故障进行预测、显示及重现。
The invention discloses a fault recurrence and prediction system based on digital twin. The system includes: virtualization module, OPC UA model module, relational model module, fault prediction module, virtualized scene display module, historical database and fault playback module; the virtualization module is used to virtualize actual equipment entities; equipment real-time data It can be displayed in a virtualized scenario; in the virtualized scenario, the running screen of the device can be played back, so as to trace the cause of the device failure; the incoming actual data information will be saved in the historical database, according to the historical data information in the historical database , get a relational model, and predict the occurrence of faults according to the relational model and real-time data. The invention can display the running state of the equipment, and predict, display and reproduce the equipment failure.
Description
Technical Field
The invention relates to a fault recurrence and prediction system based on digital twinning.
Background
With the continuous development of mechanization and intellectualization, mechanical equipment is more and more appeared in our lives. Since mechanical devices are inevitably subject to faults, these faults can sometimes be detected when the devices are out of the field, and some are generated during the operation of the devices. Therefore, a system that can visually observe the operation state of the equipment, reproduce the equipment failure and have a certain prediction function on the failure is needed to meet the development requirement.
Disclosure of Invention
The invention aims to provide a fault recurrence and prediction system based on digital twins, which can display the running state of equipment and predict, display and reproduce equipment faults.
In order to achieve the purpose, the invention provides the following scheme:
a digital twin based fault recurrence and prediction system comprising: the system comprises a virtualization module, an OPCUA model module, a relation model module, a fault prediction module, a virtualization scene display module, a history database and a fault playback module;
the virtualization module is used for constructing a virtual model of each device in the workshop;
the OPCUA model module acquires real-time operation data of each device in the workshop by operating the OPCUA information model; the OPCUA information model is a workshop information model established in an OPCUA modeling mode, and the real-time operation data is the real-time operation data corresponding to each characteristic parameter of each device and each sub-component thereof;
the relational model module is used for establishing a relational model among the characteristic parameters;
the fault prediction module predicts whether each characteristic parameter is abnormal according to the real-time operation data or predicts whether each characteristic parameter is abnormal according to the real-time operation data and the relation model;
the virtual scene display module displays a virtual model of each device, displays real-time running data of corresponding characteristic parameters on the virtual model, and displays the device to which the abnormal characteristic parameter belongs as an alarm color when a certain characteristic parameter is abnormal;
the historical database stores the real-time operation data;
and the fault playback module controls the virtual scene display module to play back the pictures according to the running data in the historical database in a time sequence. When a playback operation is performed, data transfer from the history database into the playback screen is called in accordance with the input start time of playback.
Optionally, the virtualization module includes: virtual models of a variety of devices.
Optionally, the virtualization module includes: and the model building unit is used for building a virtual model of the equipment.
Optionally, the failure playback module is further configured to control the failure prediction module to predict whether each feature parameter is abnormal according to the operation data stored in the historical database, and when the feature parameter is abnormal, control the virtualized scene display module to display the device to which the abnormal feature parameter belongs as an alarm color in the playback process.
Optionally, the virtualization module is further configured to set a name and an ID for each device and its sub-component, where the ID of each device and sub-component is unique.
The virtualization module can virtualize an actual device entity; real-time data of the equipment can be displayed in a virtual scene; the running picture of the equipment can be played back in the virtual scene, so that the reason of the equipment fault can be traced; the transmitted actual data information is stored in a historical database, a relational model is obtained according to the historical data information in the historical database, and the occurrence of the fault is predicted according to the model and the real-time data.
Constructing a three-dimensional model of the equipment in a virtualization module, which comprises the following specific steps: a device will contain many sub-components, all of which will have physical dimensions. Here, the virtual model of each subcomponent is obtained by inputting the size of each subcomponent, and a virtual model of the entire device is obtained. The virtual module provides a virtual model of the commonly used equipment and its subcomponents, and the size of each subcomponent can be further adjusted according to the size of the actually used equipment. If the virtual model of the device to be used is not found in the virtual module, the virtual model can be automatically created in the virtual module, and the created virtual model is saved in the virtual module and can be directly used later. Meanwhile, the display names and ID values of all the sub-components of the model, the parameter interval in normal operation and the alarm color in fault occurrence are set. These settings are used in the virtualized scene display module.
Optionally, the system further includes:
the OPCUA server stores an OPCUA information model, communicates with each device by operating the OPCUA information model, and acquires real-time operation data of each device, wherein the OPCUA information model is a workshop information model established by adopting an OPCUA modeling mode; and the OPCUA model module acquires real-time operation data of each device in the workshop from the OPCUA server.
And adopting the OPCUA to collect the data information of the equipment, and transmitting the collected operation data of each sub-component of the equipment to the ID value of each component. Wherein the acquisition time interval of the real-time operational data can be set.
According to the method, equipment of the whole production workshop is firstly modeled, an OPCUA modeling mode is adopted, and meanwhile, the established OPCUA model is given to the established three-dimensional model, namely, the model information of the OPCUA corresponds to each part of the three-dimensional model. The method comprises the following specific steps: and establishing an OPC UA information model according to an actual production workshop, and importing the model information into an OPCUA model module in a virtual platform. In the OPCUA model module, the variable nodes of each sub-component in the established model are connected with the IDs of each sub-component of the virtual equipment established in the virtual module, so that after the operation data of the actual equipment is read through the OPCUA, the actual operation data of each sub-component can be correspondingly displayed in each sub-component of the virtual equipment.
Optionally, the relational model module determines the relational model between the characteristic parameters in a data mining manner according to the operation data in the historical database.
Optionally, the relationship model module updates the relationship model between the characteristic parameters once every set period.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the fault recurrence and prediction system based on the digital twin constructs a virtual model of each device in a workshop, correspondingly displays real-time operation data of each device and each subcomponent in the virtual model, and realizes visual display of the operation state of the device. And determining or predicting the fault according to the real-time operation data, and giving out a warning, so that the fault prediction and warning of the equipment are realized. The invention also stores the real-time operation data of each device and each sub-component, and can reproduce the operation state and the fault of each device and each sub-component by adopting the stored operation data through the fault playback module, thereby realizing the fault reproduction function.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic diagram of a digital twin based fault recovery and prediction system according to an embodiment of the present invention;
FIG. 2 is a flow chart of data transfer in an embodiment of the present invention;
fig. 3 is a schematic diagram of automatic correction of a relationship model in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The present invention provides a fault recurrence and prediction system based on digital twins, as shown in fig. 1, the system includes: the system comprises a virtualization module 1, an OPCUA model module 5, a relation model module 6, a fault prediction module 4, a virtualization scene display module 2, a historical database 7 and a fault playback module 3;
the virtualization module 1 is used for constructing a virtual model of each device in the workshop;
the OPCUA model module 5 acquires real-time operation data of each device in the workshop by operating the OPCUA information model; the OPCUA information model is a workshop information model established in an OPCUA modeling mode, and the real-time operation data is the real-time operation data corresponding to each characteristic parameter of each device and each sub-component thereof;
the relational model module 6 is used for establishing a relational model among the characteristic parameters;
the fault prediction module 4 predicts whether each characteristic parameter is abnormal according to the real-time operation data or predicts whether each characteristic parameter is abnormal according to the real-time operation data and the relation model;
the virtual scene display module 2 displays a virtual model of each device, displays real-time running data of corresponding characteristic parameters on the virtual model, and displays devices to which abnormal characteristic parameters belong as alarm colors when a certain characteristic parameter is abnormal;
the historical database 7 is used for storing the real-time operation data;
and the fault playback module 3 controls the virtualized scene display module 2 to play back according to the running data in the historical database 7 in a time sequence.
In an embodiment, the failure playback module 3 further controls the failure prediction module 4 to predict whether each characteristic parameter is abnormal according to the operation data stored in the history database 7, and when the characteristic parameter is abnormal, controls the virtualized scene display module 2 to display the device to which the abnormal characteristic parameter belongs as an alarm color in the playback process. That is, during the playback process, not only the historical operation data of each device is displayed, but also whether each characteristic parameter is abnormal or not is predicted according to the historical operation data, and when the characteristic parameter is determined to be abnormal, a fault display is sent out, so that the fault display is reproduced.
In an embodiment, the virtualization module 1 may include: virtual models of various devices and a model building unit. The model building unit is used for building a virtual model.
In the embodiment, a virtual model of the equipment is built in the virtualization module 1, the actual equipment comprises a plurality of sub-components, and the invention adopts the idea of modularization to build a three-dimensional virtual model of the equipment, which is similar to the mode of stacking wood. The virtualization module 1 stores three-dimensional virtual models of some of the more common industrial devices, and virtual models of new devices may be added as needed. The physical dimensions of its sub-components are entered in the virtualization module 1 according to the size of the actual device and its sub-components, and the physical dimensions of the virtual device follow the input values to change the physical dimensions. If the virtual model of the device to be used is not found in the virtualization module 1, the model can be created by itself, and then the created model is saved in the virtualization module, and can be directly used later as the virtual model stored in the virtualization module 1. The virtual model of each device stored in the virtualization module 1 has its own name, and the name rule of the name is the name of the corresponding actual device, so that when the number of virtual models is large, the model can be quickly found by inputting the name of the virtual model to be used. When the newly added virtual model is stored in the module, the name of the actual device corresponding to the model also needs to be input.
The virtualized device and its subcomponents all have a display name for displaying in the virtualized scene display module 2; meanwhile, each device has a fixed ID, and the sub-components of the device also have sub-ID names subordinate to the device ID, and the display name and the ID can be set, but the ID is ensured to be unique. The device display name can adopt a Chinese name mode or an English display mode, and the ID can only be in the form of capital and lower case of English letters and digital underline. The purpose of this is to accurately and correspondingly display the real-time data information of each device in the virtualized scene display module 2, which is convenient for data storage and display. Therefore, it is necessary to set a display name and an ID value to the virtual device and its subcomponents, respectively, in the virtualization module 1.
For each sub-component of the apparatus, a value interval of their normal operation state, and a color at the normal operation state and an alarm color during the period of exceeding the normal value are set. In this way, it is helpful to clearly see the location of the fault in the display module. Moreover, when a sub-component fails, the overall color of the device to which the sub-component belongs may blink, preventing the failed component from being located inside the device, and the failed color may not be easily observable.
The system provided by the embodiment may further include: the OPCUA server stores an OPCUA information model, and obtains real-time operation data of each device and each sub-component in the device by operating the OPCUA information model, wherein the OPCUA information model is a workshop information model established by adopting an OPCUA modeling mode; the opuca model module 5 obtains real-time operation data of each device and each sub-component in the device in the plant from the opuca server.
Since a production plant comprises a large number of devices, each device has a plurality of sub-components, so much data information needs to establish a clear data model to ensure the friendliness in data browsing and the high efficiency of subsequent operations. The invention adopts the modeling mode of the OPCUA to establish the information model of the production workshop.
The information model for establishing the OPCUA is imported into the OPCUA server, so that an address space is established in the server, nodes in the address space are connected with a data source of an actual workshop, wherein the data source refers to equipment containing operation data or states of each component of the equipment, such as: temperature sensors, pressure sensors, etc. The characteristic parameters of the present invention refer to these temperature parameters, pressure parameters, etc.
The established information model of the OPCUA is imported into the OPCUA model module 5, and further, variable nodes storing numerical values in the established OPCUA information model are established to correspond to corresponding device IDs in the virtualization module 1, and the information model is realized in a key-value pair mode. Because each node has a NodeId attribute to uniquely identify each node, we can use the NodeId of the node as a key and the device ID in the virtual module as a value.
The specific flow of data transmission is shown in fig. 2:
(1) the OPCUA server communicates with on-site equipment to obtain real-time operation data of the equipment;
(2) the method comprises the steps that an OPCUA client side is connected with an OPCUA server, and then data in the server are read;
(3) the obtained data is then transmitted to the virtual model for display, and before we assign a unique ID to each device, the operation data of the device can be stored and displayed in a key-value pair manner. The method comprises the following specific operations: when the storage is carried out: after the OPCUA client side obtains the equipment operation data, the ID of the equipment corresponding to the node is inquired, and then the data is transmitted to the value corresponding to the equipment ID key for storage. When displaying: and finding the value corresponding to the equipment ID key according to the equipment ID, and then displaying.
The OPCUA information model module of the invention is used as an OPCUA client to read the equipment in the OPCUA server, and then the obtained data is transmitted to the virtual model for displaying. Therefore, after the data of the field devices are collected through the OPCUA, the numerical information of each component of each device can be accurately and efficiently transmitted to the ID of the corresponding virtual model. In the virtual scene display module, each sub-component of the device displays the actual operation value at the moment. Further, it is judged whether the value is within a normal interval, thereby displaying a normal color or an alarm color.
In an embodiment, the virtual scene display module is configured to virtually display an actual production workshop, and specifically includes:
in the virtualized scene display module, a simulated production plant is constructed from the actual production plant. And placing the virtual equipment constructed in the virtualization module in the virtualization scene display module according to the relative setting position of each equipment in the actual production workshop. In this way, the current device operating conditions can be seen in the display module. Further, the display of fault recurrence and fault prediction is also displayed in the display module.
In an embodiment, the historical database is used to store historical data for the entire production plant. The method comprises the following specific steps:
the opuua module transmits new data each time, and except for displaying in the virtualized scene display module, the new data is stored in the historical database at the same time, and the data stored in the historical database each time includes all numerical information displayed in the virtualized scene display module, that is, the operation information of all components of the device, and also includes the time when the data is transmitted. Therefore, when a fault occurs and the fault playback is required, data can be extracted from the historical database according to time and displayed in the virtual scene display module.
In an embodiment, the failure playback module is configured to play back the running data information before the failure occurs. The method comprises the following specific steps:
entering the fault playback module, the starting time for starting playback can be selected, then the playback mode is selected, and the virtual scene display module is switched from the real-time display state to the fault playback state. The method specifically comprises the following steps: and searching the starting time or the time closest to the starting time in the historical database according to the set starting time of the playback, and sequentially importing the searched data into the virtual scene display module for display.
The data information before the fault is searched compared with browsing the historical data in a historical database. By adopting the fault recovery mode, the operation data information of each part before the fault can be observed more visually, so that the fault reason can be obtained more clearly.
In an embodiment, the relational model module may determine the relational model between the feature parameters by data mining according to the feature parameter operation data in the historical database. And the relation model between the characteristic parameters can be updated once every set period.
The relational model is established according to the data information in the historical database. The relational model is established by first determining independent variables and dependent variables, otherwise referred to as input variables and output variables. And obtaining a relation model between the input quantity and the output quantity by a data mining mode according to a large amount of historical data in a historical database. The updating of the relational model is performed at regular intervals. For example, the relationship model is updated every week, in the week, the historical database obtains new operation data of the equipment, the part of the data which is used as the input quantity of the relationship model is input into the relationship model, the predicted output quantity can be obtained through the relationship model, the difference between the actual output and the predicted output is obtained to obtain a deviation value, the deviation value in the week is transmitted to the relationship model, and the parameters in the relationship model are corrected to enable the data model to be closer to the corresponding relationship of the actual input and output. As shown in fig. 3.
In an embodiment, the fault prediction module is configured to predict an occurrence time and an occurrence location of the fault. The failure prediction module mainly utilizes the established relation model and the variation trend of the device number acquired in a period of time, such as the variation trend of the operation data of the device in an hour. Furthermore, the operation data of each device in a future period of time is deduced in advance, the operation data is displayed in a virtual scene display module, whether a fault occurs in the future period of time or not is observed, and if the fault occurs, a part corresponding to the fault part is changed into an alarm color, so that a specific fault position is obtained. Furthermore, corresponding countermeasures can be made in advance. For example, the relevant parts are prepared in advance, or the parts of the equipment are replaced in advance at a proper time before the failure.
Such as: taking a bearing as an example, data such as the rotating speed, the vibration amplitude, the bearing temperature, the inlet temperature of cooling liquid, the outlet temperature of the cooling liquid and the like of the bearing are collected. During normal operation, the value of the bearing temperature is slowly varied over a range. And taking field data acquired in the past hour as basic data, and obtaining equipment operation data after one hour according to the change trend of the data in the one hour. If the data of a certain device in the past hour is basically unchanged, the value of the data is basically unchanged in the next prediction; if a certain value is increased all the time in the past hour, the amplitude and the trend of the data change in the previous hour are increased continuously in the next hour, for example, the bearing temperature value in the previous hour is increased slowly, the bearing temperature value is increased continuously in the next hour according to the change trend in the previous hour, and if the bearing temperature value is increased to exceed the normal range, an alarm is triggered. Or obtaining an output value of the relation model according to the obtained relation model and taking the rotating speed of the bearing, the vibration amplitude, the inlet temperature of the cooling liquid and the like as input values of the relation model, wherein the output value is the temperature of the bearing, the difference is made according to the actual temperature of the bearing and the temperature of the bearing obtained through the relation model, when the difference value of the two exceeds a certain amplitude and the amplitude tends to increase continuously, an alarm can be triggered even though the temperature value does not reach a threshold value, because the actual temperature value at the moment is deviated from the normal relation model continuously, the problem possibly occurs in the equipment part is shown.
The digital twin-based fault recurrence and prediction system provided by the invention has the following advantages:
(1) the virtual model corresponds to the OPCUA model, and data can be effectively displayed and stored. The device and the sub-components of the virtual model are respectively labeled with ID, and the ID is corresponding to the model information in the OPCUA, so that the data value can be rapidly transmitted to the corresponding ID, and the data value can be displayed in the display screen.
(2) Through the fault recovery mode, the running state of each device before the fault occurs can be observed more visually and vividly.
(3) A normal parameter interval is set for each component, when the normal parameter interval exceeds the normal parameter interval, the failed component changes into a failure color, and meanwhile, the color of equipment to which the failed component belongs also begins to flicker, so that strong visual impact can be brought to people.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107678306A (en) * | 2017-10-09 | 2018-02-09 | 驭势（上海）汽车科技有限公司 | Dynamic scene information is recorded and emulation back method, device, equipment and medium |
| CN107832497A (en) * | 2017-10-17 | 2018-03-23 | 广东工业大学 | A kind of intelligent workshop fast custom design method and system |
| CN108107841A (en) * | 2017-12-26 | 2018-06-01 | 山东大学 | A kind of twin modeling method of numerically-controlled machine tool number |
| CN110187686A (en) * | 2019-06-03 | 2019-08-30 | 济南浪潮高新科技投资发展有限公司 | A kind of Internet of Things industrial processes monitoring method based on the twin technology of number |
-
2019
- 2019-12-30 CN CN201911390190.1A patent/CN111159893A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107678306A (en) * | 2017-10-09 | 2018-02-09 | 驭势（上海）汽车科技有限公司 | Dynamic scene information is recorded and emulation back method, device, equipment and medium |
| CN107832497A (en) * | 2017-10-17 | 2018-03-23 | 广东工业大学 | A kind of intelligent workshop fast custom design method and system |
| CN108107841A (en) * | 2017-12-26 | 2018-06-01 | 山东大学 | A kind of twin modeling method of numerically-controlled machine tool number |
| CN110187686A (en) * | 2019-06-03 | 2019-08-30 | 济南浪潮高新科技投资发展有限公司 | A kind of Internet of Things industrial processes monitoring method based on the twin technology of number |
Non-Patent Citations (5)
| Title |
|---|
| 孟光主编: "《 航天航空能制造技术与装备发展战略研究》", 31 October 2017, pages: 207 * |
| 缪远明等: "硬X射线调制望远镜卫星数字伴飞系统设计与应用", 《航天器工程》 * |
| 缪远明等: "硬X射线调制望远镜卫星数字伴飞系统设计与应用", 《航天器工程》, vol. 27, no. 5, 15 October 2018 (2018-10-15), pages 46 - 49 * |
| 赵浩然等: "面向数字孪生车间的三维可视化实时监控方法", 《计算机集成制造系统》 * |
| 赵浩然等: "面向数字孪生车间的三维可视化实时监控方法", 《计算机集成制造系统》, vol. 25, no. 6, 15 June 2019 (2019-06-15), pages 1432 - 1443 * |
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