WO2022257925A1 - Procédé et appareil de prédiction de défaillance basés sur un jumeau numérique, serveur et support de stockage - Google Patents

Procédé et appareil de prédiction de défaillance basés sur un jumeau numérique, serveur et support de stockage Download PDF

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WO2022257925A1
WO2022257925A1 PCT/CN2022/097396 CN2022097396W WO2022257925A1 WO 2022257925 A1 WO2022257925 A1 WO 2022257925A1 CN 2022097396 W CN2022097396 W CN 2022097396W WO 2022257925 A1 WO2022257925 A1 WO 2022257925A1
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data
fault prediction
fault
real
trained
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PCT/CN2022/097396
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English (en)
Chinese (zh)
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崔岩
常青玲
侯宇灿
揭英达
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五邑大学
广东四维看看智能设备有限公司
中德（珠海）人工智能研究院有限公司
珠海市四维时代网络科技有限公司
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Publication of WO2022257925A1 publication Critical patent/WO2022257925A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent

Definitions

  • the present application belongs to the technical field of data mapping, and in particular relates to a digital twin-based fault prediction method, device, server and storage medium.
  • the embodiment of the present application provides a fault prediction method, device, server, and storage medium based on digital twins to realize early prediction of production line faults.
  • the embodiment of the present application provides a fault prediction method based on digital twins, including:
  • the simulation data is input into a pre-trained fault prediction model, and before the fault prediction result is obtained, the method further includes:
  • extracting the fault data in the real-time data includes:
  • the fault data in the real-time training data is extracted according to a preset fault data extraction model.
  • the pre-trained fault prediction model includes a generation network and a discrimination network
  • Import the fault data into the preset fault prediction model for training to obtain a pre-trained fault prediction model including:
  • the fault data is used as real data, the real data and the fake sample data are used as input, and the discrimination result is used as the output, and the discrimination result is used as the feedback input of the generation network, and according to the preset objective function
  • the discrimination network and the generation network are trained to obtain a pre-trained fault prediction model.
  • the preset objective function is:
  • D represents the generation network
  • G represents the confrontation network
  • E represents the expected function
  • x represents the fault data
  • x ⁇ pdata(x) represents the fault data obeys the pdata(x) distribution
  • z represents the noise data
  • z ⁇ p (z) indicates that the noise data obeys the p (z) distribution
  • D(x) indicates the output data after the fault data is imported into the generation network
  • G(z) indicates the output data after the noise data is imported into the judgment network.
  • the fault prediction result is obtained according to the simulation data and the pre-trained fault prediction model, including:
  • the simulation data and the data generated by the generating network according to the noise data are imported into the discriminant network, and the fault prediction result is output.
  • the method further includes:
  • the embodiment of the present application provides a digital twin-based fault prediction device, including:
  • the first acquisition module is used to acquire the real-time data of the target real object
  • a generating module configured to generate a digital twin virtual object according to the real-time data
  • the second acquisition module the simulation data in the digital twin virtual model
  • the fault prediction module is used to obtain the fault prediction result according to the simulation data and the pre-trained fault prediction model.
  • the device further includes:
  • the third obtaining module is used to obtain the real-time training data of the target real object
  • An extraction module configured to extract fault data in the real-time data samples
  • the training module is used to import fault data into a preset fault prediction model for training to obtain a pre-trained fault prediction model.
  • the extraction module includes:
  • the extraction unit is configured to extract fault data in the real-time training data according to a preset fault data extraction model.
  • the pre-trained fault prediction model includes a generation network and a discrimination network
  • the training modules include:
  • the fault data is used as real data, the real data and the fake sample data are used as input, and the discrimination result is used as the output, and the discrimination result is used as the feedback input of the generation network, and according to the preset objective function
  • the discrimination network and the generation network are trained to obtain a pre-trained fault prediction model.
  • the preset objective function is:
  • D represents the generation network
  • G represents the confrontation network
  • E represents the expected function
  • x represents the fault data
  • x ⁇ pdata(x) represents the fault data obeys the pdata(x) distribution
  • z represents the noise data
  • z ⁇ p (z) indicates that the noise data obeys the p (z) distribution
  • D(x) indicates the output data after the fault data is imported into the generation network
  • G(z) indicates the output data after the noise data is imported into the judgment network.
  • the fault prediction module includes:
  • the fault prediction unit is used to import the simulation data and the data generated by the generation network according to the noise data into the discrimination network, and output a fault prediction result.
  • the digital twin-based fault diagnosis equipment further includes:
  • the registration module is used to register simulation data and fault prediction results.
  • a sending module configured to send the registered simulation data and fault prediction results to the user terminal to instruct the user terminal to display a monitoring picture to the user, the monitoring picture including the operating status of the target real object and the marked fault prediction analyze.
  • an embodiment of the present application provides a server, including a memory, a processor, and a computer program stored in the memory and operable on the processor, and the computer program is implemented when the processor executes the computer program.
  • a server including a memory, a processor, and a computer program stored in the memory and operable on the processor, and the computer program is implemented when the processor executes the computer program. The method as described in the first aspect above.
  • an embodiment of the present application provides a storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method as described in the first aspect above is implemented.
  • a digital twin virtual object is generated according to the real-time data, the simulation data in the digital twin virtual model is obtained, and the fault prediction result is obtained according to the simulation data and the pre-trained fault prediction model . It can be seen that the embodiment of the present application can achieve the effect of accurate fault prediction for real objects (such as production lines or workshops, etc.) by combining the digital twin technology with the unsupervised learning characteristics of the pre-trained fault prediction model.
  • FIG. 1 is a schematic flow diagram of a digital twin-based fault prediction method provided in an embodiment of the present application
  • Fig. 2 is a schematic flow chart before step S108 in Fig. 1 of the digital twin-based fault prediction method provided by the embodiment of the present application;
  • Fig. 3 is a specific implementation flowchart of step S206 in Fig. 2 of the digital twin-based fault prediction method provided by the embodiment of the present application;
  • Fig. 4 is a schematic flow chart after step S204 in Fig. 2 of the digital twin-based fault prediction method provided by the embodiment of the present application;
  • FIG. 5 is a schematic structural diagram of a digital twin-based fault prediction device provided by an embodiment of the present application.
  • FIG. 6 is a schematic structural diagram of a server provided by an embodiment of the present application.
  • Fig. 7 is the training process of the fault prediction model of the digital twin-based fault prediction method provided by the embodiment of the present application.
  • the term “if” may be construed, depending on the context, as “when” or “once” or “in response to determining” or “in response to detecting “.
  • the phrase “if determined” or “if [the described condition or event] is detected” may be construed, depending on the context, to mean “once determined” or “in response to the determination” or “once detected [the described condition or event] ]” or “in response to detection of [described condition or event]”.
  • references to "one embodiment” or “some embodiments” or the like in the specification of the present application means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application.
  • appearances of the phrases “in one embodiment,” “in some embodiments,” “in other embodiments,” “in other embodiments,” etc. in various places in this specification are not necessarily All refer to the same embodiment, but mean “one or more but not all embodiments” unless specifically stated otherwise.
  • the terms “including”, “comprising”, “having” and variations thereof mean “including but not limited to”, unless specifically stated otherwise.
  • Digital twin is a simulation process that makes full use of data such as physical models, sensor updates, and operating history, integrates multi-disciplinary, multi-physical quantities, multi-scale, and multi-probability, and completes the mapping in the virtual space to reflect the corresponding physical equipment. whole life cycle process.
  • a digital twin is a concept beyond reality that can be viewed as a digital twin of one or more important, interdependent equipment systems.
  • FIG. 1 it is a schematic flow diagram of a digital twin-based fault prediction method provided by the embodiment of the present application.
  • the method can be applied to a server connected to a sensor set on a target real object.
  • the server may be a computing device such as a cloud server, and the method may include the following steps:
  • Step S102 acquiring real-time data of the target real object.
  • the target real object refers to a real-world production line or workshop, etc.
  • real-time data includes static data (such as equipment parameters) and dynamic data (such as equipment operating status).
  • the real-time data of the target real object is collected through sensors installed on various devices in the production line or workshop.
  • Step S104 generating a digital twin virtual object according to the real-time data.
  • the digital twin virtual object refers to the digital twin corresponding to the target real object.
  • an editable logic controller is used to generate digital twin virtual objects based on real-time data.
  • Step S106 acquiring simulation data in the digital twin virtual model.
  • the simulation data refers to the data generated by the digital twin virtual model to dynamically map the operating conditions of the target real object according to the real-time data.
  • Step S108 according to the simulation data and the pre-trained fault prediction model, a fault prediction result is obtained.
  • the pre-trained fault prediction model includes a generative network and a discriminative network.
  • the fault prediction result is obtained according to the simulation data and the pre-trained fault prediction model, including: importing the simulation data and the data generated by the generation network based on the noise data into the discriminant network, and outputting the fault prediction result.
  • the fault prediction result is the fault equipment number, fault type, fault cause and fault time.
  • the simulation data and the data generated by the generation network based on the noise data are judged as real data or generated samples according to the discrimination network, so as to determine the fault data in the simulation data as the fault prediction result.
  • the embodiment of the present application provides a schematic flow chart before step S108 in Figure 1 of the digital twin-based fault prediction method, based on simulation data and pre-trained fault prediction
  • the model, before getting the fault prediction results also includes:
  • Step S202 acquiring real-time training data of the target real object.
  • real-time training data is obtained from open source datasets.
  • Step S204 extracting fault data in the real-time training data.
  • the fault data includes fault equipment number, fault type, fault content and fault time.
  • the named entity recognition is directly carried out on the real-time data, and the fault equipment number, fault type, fault content and fault time in the entity data are extracted.
  • the fault data extraction model can be trained on the basis of Bi-LSTM+CRF neural network.
  • the process of training the fault data extraction model on the Bi-LSTM+CRF neural network can be:
  • Step S206 importing the fault data into a preset fault prediction model for training to obtain a pre-trained fault prediction model.
  • the fault data is imported into the preset fault prediction model for training to obtain a pre-trained fault prediction model, including: importing the fault data and the data generated by the generation network based on the noise data to the discriminant network, and outputting the fault prediction result.
  • FIG. 3 it is a specific implementation flow chart of step S206 in FIG. 2 of the digital twin-based fault prediction method provided by the embodiment of the present application.
  • the fault data is imported into the preset fault prediction model for training, and the obtained Pre-trained failure prediction models, including:
  • Step S302 generating noise data.
  • the noise data is randomly generated variables, which are used to train the generation network to generate fake sample data.
  • Step S304 importing the noise data into the generation network to obtain fake sample data.
  • the generating network is preferably a naive Bayesian model.
  • Step S306 using the fault data as the real data, the real data and the fake sample data as the input, the discrimination result as the output, and the discrimination result as the feedback input of the generation network, and training the discrimination network and the generation network according to the preset objective function , to get the pre-trained fault prediction model.
  • the discriminant network is preferably a decision tree.
  • the preset objective function is:
  • D represents the generation network
  • G represents the confrontation network
  • E represents the expected function
  • X represents the fault data
  • x ⁇ pdata(x) represents the fault data obeys the pdata(x) distribution
  • z represents the noise data
  • z ⁇ p (z) indicates that the noise data obeys the p (z) distribution
  • D(x) indicates the output data after the fault data is imported into the generation network
  • G(z) indicates the output data after the noise data is imported into the judgment network.
  • pdata(x) distribution and p (z) distribution may respectively refer to Gaussian distribution, uniform distribution and the like.
  • steps S302 to S306 reference may be made to the training process of the fault prediction model as shown in FIG. 7 .
  • the named entity recognition technology is used to directly extract the fault data to train the fault prediction data, without additional manual data labeling, which reduces the difficulty of training the fault prediction model.
  • FIG. 4 it is a schematic flow chart after step S204 in FIG. 2 of the digital twin-based fault prediction method provided by the embodiment of the present application.
  • the trained fault prediction model after obtaining the fault prediction results, also includes:
  • Step S402 registering the simulation data and the fault prediction result.
  • Step S404 sending the registered simulation data and fault prediction results to the user terminal to instruct the user terminal to generate a monitoring screen according to the registered simulation data and fault prediction results, and display the monitoring screen to the user.
  • the monitoring screen includes the equipment generation of each equipment in the digital twin virtual model, the equipment operation status and the fault prediction results of the faulty equipment.
  • the simulation data and fault prediction results are obtained by combining the digital twin technology with the pre-trained fault prediction model, and a monitoring screen is generated according to the simulation data and fault prediction results for customers to view, so as to realize the user through monitoring.
  • the screen can check the running status of the target real object and the prediction and analysis of the faulty equipment in the target real object in real time, and detect the faulty equipment in time.
  • a digital twin virtual object is generated according to the real-time data, the simulation data in the digital twin virtual model is obtained, and the fault prediction result is obtained according to the simulation data and the pre-trained fault prediction model .
  • the effect of accurate fault prediction on real objects can be achieved by combining the digital twin technology with the unsupervised learning characteristics of the pre-trained fault prediction model.
  • Fig. 5 shows a structural block diagram of the digital twin-based fault prediction device provided by the embodiment of the present application. For the convenience of description, only the parts related to the embodiment of the present application are shown.
  • the digital twin-based fault prediction device includes:
  • the first obtaining module 51 is used to obtain the real-time data of the target real object
  • a generating module 52 configured to generate a digital twin virtual object according to the real-time data
  • the second acquisition module 53 the simulation data in the digital twin virtual model
  • the fault prediction module 54 is configured to obtain a fault prediction result according to the simulation data and the pre-trained fault prediction model.
  • the device further includes:
  • the third obtaining module is used to obtain the real-time training data of the target real object
  • An extraction module configured to extract fault data in the real-time data samples
  • the training module is used to import the fault data into a preset fault prediction model for training to obtain a pre-trained fault prediction model.
  • the extraction module includes:
  • the extraction unit is configured to extract fault data in the real-time training data according to a preset fault data extraction model.
  • the pre-trained fault prediction model includes a generation network and a discrimination network
  • the training modules include:
  • the fault data is used as real data, the real data and the fake sample data are used as input, and the discrimination result is used as the output, and the discrimination result is used as the feedback input of the generation network, and according to the preset objective function
  • the discrimination network and the generation network are trained to obtain a pre-trained fault prediction model.
  • the preset objective function is:
  • D represents the generation network
  • G represents the confrontation network
  • E represents the expected function
  • X represents the fault data
  • x ⁇ pdata(x) represents the fault data obeys the pdata(x) distribution
  • z represents the noise data
  • z ⁇ p (z) indicates that the noise data obeys the p (z) distribution
  • D(x) indicates the output data after the fault data is imported into the generation network
  • G(z) indicates the output data after the noise data is imported into the judgment network.
  • the fault prediction module includes:
  • the fault prediction unit is used to import the simulation data and the data generated by the generation network according to the noise data into the discrimination network, and output a fault prediction result.
  • the digital twin-based fault diagnosis equipment further includes:
  • the registration module is used to register simulation data and fault prediction results.
  • a sending module configured to send the registered simulation data and fault prediction results to the user terminal to instruct the user terminal to display a monitoring picture to the user, the monitoring picture including the operating status of the target real object and the marked fault prediction analyze.
  • FIG. 6 is a schematic structural diagram of a server provided by an embodiment of the present application.
  • the server 6 of this embodiment includes: at least one processor 60, a memory 61, and a computer program 62 stored in the memory 61 and operable on the at least one processor 60, so When the processor 60 executes the computer program 62, the steps in any of the foregoing method embodiments are implemented.
  • the server 6 may be a computing device such as a cloud server.
  • the server may include, but not limited to, a processor 60 and a memory 61 .
  • Fig. 6 is only an example of server 6, and does not constitute a limitation to server 6, and may include more or less components than those shown in the figure, or combine some components, or different components, for example It may also include input and output devices, network access devices, etc.
  • the so-called processor 60 can be a central processing unit (Central Processing Unit, CPU), and the processor 60 can also be other general processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit) , ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • a general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.
  • the storage 61 may be an internal storage unit of the server 6 in some embodiments, such as a hard disk or memory of the server 6 .
  • the memory 61 can also be an external storage device of the server 6 in other embodiments, such as a plug-in hard disk equipped on the server 6, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, flash memory card (Flash Card), etc.
  • the storage 61 may also include both an internal storage unit of the server 6 and an external storage device.
  • the memory 61 is used to store operating system, application program, boot loader (BootLoader), data and other programs, such as the program code of the computer program.
  • the memory 61 can also be used to temporarily store data that has been output or will be output.
  • the embodiment of the present application also provides a storage medium, the storage medium is a computer-readable storage medium, and the readable storage medium stores a computer program, and when the computer program is executed by a processor, it can realize the above-mentioned various method embodiments. in the steps.
  • the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • all or part of the processes in the methods of the above embodiments in the present application can be completed by instructing related hardware through computer programs, and the computer programs can be stored in a computer-readable storage medium.
  • the computer program When executed by a processor, the steps in the above-mentioned various method embodiments can be realized.
  • the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form.
  • the computer-readable medium may at least include: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random-access memory (RAM, Random Access Memory), electrical carrier signals, telecommunication signals, and software distribution media.
  • ROM read-only memory
  • RAM random-access memory
  • electrical carrier signals telecommunication signals
  • software distribution media Such as U disk, mobile hard disk, magnetic disk or CD, etc.
  • computer readable media may not be electrical carrier signals and telecommunication signals under legislation and patent practice.
  • the disclosed device/network device and method may be implemented in other ways.
  • the device/network device embodiments described above are only illustrative.
  • the division of the modules or units is only a logical function division.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

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Abstract

La présente invention est applicable au domaine technique du mappage de données, et concerne un procédé et un appareil de prédiction de défaillance basés sur un jumeau numérique, un serveur et un support de stockage. Le procédé consiste à : obtenir des données en temps réel d'un objet réel cible ; générer un objet virtuel jumeau numérique selon les données en temps réel ; obtenir des données de simulation dans un modèle virtuel jumeau numérique ; et obtenir un résultat de prédiction de défaillance selon les données de simulation et un modèle de prédiction de défaillance pré-entraîné. Ainsi, dans des modes de réalisation de la présente invention, la prédiction précise des défaillances d'un objet réel (tel qu'une chaîne de production ou un atelier) peut être réalisée par une combinaison de la technologie de jumeau numérique et des caractéristiques d'apprentissage non supervisé du modèle de prédiction de défaillance pré-entraîné.
PCT/CN2022/097396 2021-06-09 2022-06-07 Procédé et appareil de prédiction de défaillance basés sur un jumeau numérique, serveur et support de stockage WO2022257925A1 (fr)

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CN116319260A (zh) * 2023-05-09 2023-06-23 新华三技术有限公司 一种网络故障诊断方法、装置、设备及存储介质
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