CN116360392A - Digital twin model fault prediction method, system and equipment of shearing machine - Google Patents
Digital twin model fault prediction method, system and equipment of shearing machine Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 58
- 238000010008 shearing Methods 0.000 title claims abstract description 43
- 239000002915 spent fuel radioactive waste Substances 0.000 claims abstract description 25
- 238000009776 industrial production Methods 0.000 claims abstract description 19
- 238000004458 analytical method Methods 0.000 claims abstract description 16
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0262—Confirmation of fault detection, e.g. extra checks to confirm that a failure has indeed occurred
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
The invention provides a digital twin model fault prediction method, a system and equipment of a shearing machine, wherein the method comprises the following steps: acquiring global data of an industrial production line in spent fuel post-treatment; obtaining the equipment operation state of the global digital twin model reaction of the shearing equipment according to the global data; the global digital twin model is obtained by simulation according to equipment data of shearing equipment; according to the running state, controlling an analysis and evaluation model to conduct fault prediction processing on the global digital twin model of the shearing machine equipment to obtain a prediction result; the scheme of the invention can improve the global digital twin computing capability and the cutter equipment fault prediction effect of the spent fuel post-treatment industrial demonstration plant.
Description
Technical Field
The invention relates to the technical field of spent fuel aftertreatment, in particular to a digital twin model fault prediction method, a digital twin model fault prediction system and digital twin model fault prediction equipment for a shearing machine.
Background
At present, an industrial demonstration factory for post-treatment of spent fuel is raised, the industrial demonstration factory combines the latest experience on the basis of the design, construction and debugging results of the prior power stack spent fuel post-treatment pilot plant, and adopts a mature, reliable and advanced spent fuel post-treatment technology for the prior scheme optimization design;
the shearing machine equipment is key equipment in the spent fuel post-treatment industrial demonstration factory, and is responsible for extracting the AFA3G spent fuel assembly from the element elevator into a charging barrel in the shearing hot chamber, and sending the charging barrel to the shearing device through the feeding device, and integrally cutting the assembly according to a preset length;
however, the novel industrial demonstration plant has complex spent fuel post-treatment process and strong uncertainty, so that the fault prediction of the shearing machine equipment in the spent fuel post-treatment industrial demonstration plant is difficult.
Disclosure of Invention
The invention aims to solve the technical problem of providing a digital twin model fault prediction method, a digital twin model fault prediction system and a digital twin model fault prediction device for a shearer, and the method, the system and the equipment for predicting the faults of the shearer can improve the global digital twin calculation capacity and the shearer equipment of a spent fuel post-treatment industrial demonstration plant.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a digital twin model fault prediction method of a shearing machine comprises the following steps:
acquiring global data of an industrial production line in spent fuel post-treatment;
obtaining the equipment operation state of the global digital twin model reaction of the shearing equipment according to the global data; the global digital twin model is obtained by simulation according to equipment data of shearing equipment;
and according to the running state, controlling the analysis and evaluation model to conduct fault prediction processing on the global digital twin model of the shearing machine equipment to obtain a prediction result.
Optionally, acquiring global data of an industrial production line in post-treatment of spent fuel includes:
constructing a basic mathematical framework of a process operation mechanism in an industrial production line according to a basic framework of a production process, production elements and expert knowledge;
and collecting data output by the basic mathematical framework to form global data.
Optionally, the global data includes: equipment status data and process flow data upstream and downstream of the shears in the process flow.
Optionally, according to the running state, the control analysis evaluation model performs fault prediction processing on the global digital twin model of the cutter device to obtain a prediction result, including:
acquiring at least one control parameter of the global digital twin model, wherein the control parameter is used for controlling at least one equipment running state and process production flow on the global production line;
evaluating, screening and optimizing a global digital twin model of the shearing machine equipment according to the comprehensive evaluation index and at least one control parameter;
and carrying out online deployment on the final optimized global digital twin model to obtain a prediction result.
Optionally, the method further comprises:
and adjusting control parameters for the global digital twin model according to the operation result fed back by the industrial production line equipment, and outputting an optimization result.
The invention also provides a digital twin model fault prediction system of the shearing machine, which comprises:
the acquisition module is used for acquiring the global data of the industrial production line in the post-treatment of the spent fuel; obtaining the equipment operation state of the global digital twin model reaction of the shearing equipment according to the global data; the global digital twin model is obtained by simulation according to equipment data of shearing equipment;
and the prediction module is used for controlling the analysis and evaluation model to perform fault prediction processing on the global digital twin model of the shearing machine equipment according to the running state to obtain a prediction result and outputting the prediction result.
The present invention also provides a computing device comprising: a processor, a memory storing a computer program which, when executed by the processor, performs the method as described above.
The invention also provides a computer readable storage medium storing instructions that when run on a computer cause the computer to perform a method as described above.
The scheme of the invention at least comprises the following beneficial effects:
according to the scheme, global data of an industrial production line in spent fuel post-treatment are obtained; obtaining the equipment operation state of the global digital twin model reaction of the shearing equipment according to the global data; the global digital twin model is obtained by simulation according to equipment data of shearing equipment; according to the running state, controlling an analysis and evaluation model to conduct fault prediction processing on the global digital twin model of the shearing machine equipment to obtain a prediction result; the global digital twin computing capability and the cutter equipment fault prediction effect of the spent fuel aftertreatment industry demonstration plant can be improved.
Drawings
FIG. 1 is a flow diagram of a digital twin model fault prediction method for a shearer of an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a digital twin model failure prediction system of a shear according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1, an embodiment of the present invention provides a digital twin model fault prediction method for a shearing machine, including:
and step 13, according to the running state, controlling the analysis and evaluation model to conduct fault prediction processing on the global digital twin model of the shearing machine equipment to obtain a prediction result.
In the embodiment, the operation state of the shearer equipment in the spent fuel post-treatment industrial demonstration field is simulated by constructing a global digital twin model of the shearer equipment, so that the fault prediction effect of the shearer equipment is improved.
In an alternative embodiment of the present invention, step 11 may include:
step 111, constructing a basic mathematical framework of a process operation mechanism in an industrial production line according to a basic framework of a production process, production elements and expert knowledge;
and step 112, collecting data output by the basic mathematical framework to form global data.
In this embodiment, the basic mathematical framework is formed by a calculation model of the bottom layer device, and includes data of various sensors, a preprocessing result of the sensor data, a reynolds coefficient Re calculation result of the air flow pipeline section, a dissipation rate, a particle movement speed and the like; for example, calculation of Reynolds number Re for a section of an airflow duct: re=4χ v×l×w/(2χβ×l+w)), where v denotes the average velocity of the airflow, l denotes the length of the rectangular flow cross-section of the airflow duct, w denotes the width of the rectangular flow cross-section of the airflow duct, and β denotes the dynamic viscosity of the airflow; the calculation formula of the Reynolds coefficient Re is a basic mathematical framework; and acquiring data output by at least one basic mathematical framework, namely, the global data according to which the global digital twin model of the shearing equipment is predicted can be formed.
In an alternative embodiment of the present invention, the global data includes: equipment status data and process flow data upstream and downstream of the shears in the process flow.
In this embodiment, the global digital twin model collects data not only for the shearing machine equipment, but also other data related to the upstream and downstream of the equipment, including production, quality, safety and other data throughout the process flow; the prediction is based on the collected global data, so that the prediction result is more accurate.
In an alternative embodiment of the present invention, step 13 may include:
step 131, obtaining at least one control parameter of the global digital twin model, wherein the control parameter is used for controlling at least one equipment running state and process production flow on the global production line;
step 132, evaluating, screening and optimizing the global digital twin model of the shearing machine equipment according to the comprehensive evaluation index and at least one control parameter;
and 133, performing online deployment on the final optimized global digital twin model to obtain a prediction result.
In the embodiment, aiming at indexes such as the health state of the shearing machine equipment and equipment fault analysis, an analysis and evaluation model and a supporting environment based on an index system are established, wherein the analysis and evaluation model supports the construction of the index system, the design of an evaluation scheme, the design of an evaluation flow and the display of various evaluation results; including at least one of SWOT analysis, analytic Hierarchy Process (AHP), fuzzy evaluation (FCE), data Envelope Analysis (DEA), gray comprehensive evaluation, comprehensive Index Method (CIM), regression analysis algorithm, cluster analysis, association rule algorithm, deep learning model, or combinations thereof.
Inputting the collected global data into an analysis and evaluation model for evaluating whether the equipment operates normally or not and whether faults occur or not; the supporting environment is a software and hardware environment for analyzing and evaluating the program operation corresponding to the model.
In this embodiment, by adjusting the control parameters of the global digital twin model, the global digital twin model is made to be closer to the running condition of the physical device, so that the simulation is more accurate.
In an alternative embodiment of the present invention, the method further includes:
and adjusting control parameters for the global digital twin model according to the operation result fed back by the industrial production line equipment, and outputting an optimization result.
The model obtained by final optimization is deployed on line, and the global digital twin model can simulate and obtain the optimal solution for controlling the production process and predict faults by combining the input information of the production requirements; the output simulation prediction result is fed back to the equipment control system for guiding production control.
The global digital twin model is also used for repeatedly carrying out iterative training and optimization according to the continuously updated and accumulated data so as to adapt to the continuous change of the real environment; the steps are repeated to form a virtual-real interaction circulating system, so that the global digital twin computing capacity of the shearing machine equipment of the spent fuel after-treatment industrial demonstration plant is improved, and the fault prediction effect of the shearing machine equipment of the spent fuel after-treatment industrial demonstration plant is improved.
By the shear equipment visual management and production control optimization method based on big data analysis, a continuous interaction process can be established between equipment entities and virtual equipment; the virtual digital device continuously collects real-time data of the physical device, and performs model iterative training, model verification, preference and model update release by using the real-time data and the historical data, so that the most effective feedback is continuously provided for production control optimization of the physical device.
As shown in fig. 2, an embodiment of the present invention further provides a digital twin model fault prediction system 20 of a shear, comprising:
an acquisition module 21, configured to acquire global data of an industrial production line in post-processing of spent fuel; obtaining the equipment operation state of the global digital twin model reaction of the shearing equipment according to the global data; the global digital twin model is obtained by simulation according to equipment data of shearing equipment;
and the prediction module 22 is used for controlling the analysis and evaluation model to perform fault prediction processing on the global digital twin model of the cutter equipment according to the running state to obtain a prediction result and outputting the prediction result.
Optionally, acquiring global data of an industrial production line in post-treatment of spent fuel includes:
constructing a basic mathematical framework of a process operation mechanism in an industrial production line according to a basic framework of a production process, production elements and expert knowledge;
and collecting data output by the basic mathematical framework to form global data.
Optionally, the global data includes: equipment status data and process flow data upstream and downstream of the shears in the process flow.
Optionally, according to the running state, the control analysis evaluation model performs fault prediction processing on the global digital twin model of the cutter device to obtain a prediction result, including:
acquiring at least one control parameter of the global digital twin model, wherein the control parameter is used for controlling at least one equipment running state and process production flow on the global production line;
evaluating, screening and optimizing a global digital twin model of the shearing machine equipment according to the comprehensive evaluation index and at least one control parameter;
and carrying out online deployment on the final optimized global digital twin model to obtain a prediction result.
Optionally, the method further comprises:
and adjusting control parameters for the global digital twin model according to the operation result fed back by the industrial production line equipment, and outputting an optimization result.
It should be noted that, the system is a system corresponding to the above method, and all implementation manners in the above method embodiments are applicable to the system embodiment, so that the same technical effects can be achieved.
An embodiment of the invention is a computing device comprising: a processor, a memory storing a computer program which, when executed by the processor, performs the method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.
Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
Furthermore, it should be noted that in the apparatus and method of the present invention, it is apparent that the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. Also, the steps of performing the series of processes described above may naturally be performed in chronological order in the order of description, but are not necessarily performed in chronological order, and some steps may be performed in parallel or independently of each other. It will be appreciated by those of ordinary skill in the art that all or any of the steps or components of the methods and apparatus of the present invention may be implemented in hardware, firmware, software, or a combination thereof in any computing device (including processors, storage media, etc.) or network of computing devices, as would be apparent to one of ordinary skill in the art after reading this description of the invention.
The object of the invention can thus also be achieved by running a program or a set of programs on any computing device. The computing device may be a well-known general purpose device. The object of the invention can thus also be achieved by merely providing a program product containing program code for implementing said method or apparatus. That is, such a program product also constitutes the present invention, and a storage medium storing such a program product also constitutes the present invention. It is apparent that the storage medium may be any known storage medium or any storage medium developed in the future. It should also be noted that in the apparatus and method of the present invention, it is apparent that the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. The steps of executing the series of processes may naturally be executed in chronological order in the order described, but are not necessarily executed in chronological order. Some steps may be performed in parallel or independently of each other.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
Claims (8)
1. A digital twin model fault prediction method for a shearer, comprising:
acquiring global data of an industrial production line in spent fuel post-treatment;
obtaining the equipment operation state of the global digital twin model reaction of the shearing equipment according to the global data; the global digital twin model is obtained by simulation according to equipment data of shearing equipment;
and according to the running state, controlling the analysis and evaluation model to conduct fault prediction processing on the global digital twin model of the shearing machine equipment to obtain a prediction result.
2. The method for predicting faults of a digital twin model of a shearer of claim 1, wherein acquiring global data of an industrial production line in spent fuel post-treatment comprises:
constructing a basic mathematical framework of a process operation mechanism in an industrial production line according to a basic framework of a production process, production elements and expert knowledge;
and collecting data output by the basic mathematical framework to form global data.
3. The method of claim 2, wherein the global data comprises: equipment status data and process flow data upstream and downstream of the shears in the process flow.
4. The method for predicting faults of a digital twin model of a shear according to claim 1, wherein the controlling, analyzing and evaluating model performs fault prediction processing on the global digital twin model of the shear device according to the running state to obtain a prediction result, and the method comprises the following steps:
acquiring at least one control parameter of the global digital twin model, wherein the control parameter is used for controlling at least one equipment running state and process production flow on the global production line;
evaluating, screening and optimizing a global digital twin model of the shearing machine equipment according to the comprehensive evaluation index and at least one control parameter;
and carrying out online deployment on the final optimized global digital twin model to obtain a prediction result.
5. The method for predicting faults in a digital twin model of a shear of claim 1, further comprising:
and adjusting control parameters for the global digital twin model according to the operation result fed back by the industrial production line equipment, and outputting an optimization result.
6. A digital twin model fault prediction system for a shear, comprising:
the acquisition module is used for acquiring the global data of the industrial production line in the post-treatment of the spent fuel; obtaining the equipment operation state of the global digital twin model reaction of the shearing equipment according to the global data; the global digital twin model is obtained by simulation according to equipment data of shearing equipment;
and the prediction module is used for controlling the analysis and evaluation model to perform fault prediction processing on the global digital twin model of the shearing machine equipment according to the running state to obtain a prediction result and outputting the prediction result.
7. A computing device, comprising: a processor, a memory storing a computer program which, when executed by the processor, performs the method of any one of claims 1 to 5.
8. A computer readable storage medium storing instructions which, when run on a computer, cause the computer to perform the method of any one of claims 1 to 5.
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| CN116912444A (en) * | 2023-08-04 | 2023-10-20 | 深圳市固有色数码技术有限公司 | Meta-universe model generation system and method based on artificial intelligence |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116912444A (en) * | 2023-08-04 | 2023-10-20 | 深圳市固有色数码技术有限公司 | Meta-universe model generation system and method based on artificial intelligence |
| CN116912444B (en) * | 2023-08-04 | 2024-02-23 | 深圳市固有色数码技术有限公司 | Meta-universe model generation system and method based on artificial intelligence |
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