CN112800668A - Digital twin-based complex product operation, maintenance and manufacturing integrated overall model - Google Patents
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Abstract
The invention provides a digital twin-based complex product operation, maintenance and manufacturing integrated overall model, which comprises an operation and maintenance system module, a virtual maintenance unit module, a maintenance center module, a manufacturing plant module and a data platform module. The invention also provides solutions of operation and maintenance and manufacturing integrated flow operation mode, modeling simulation, data fusion processing and the like based on the digital twin, and discusses key technologies of intelligent diagnosis, fault prediction, data comprehensive perception, knowledge base/instance base construction and the like based on the digital twin. The invention enables the operation and maintenance of the complex product to be deeply fused with the manufacturing information, the operation and maintenance and the manufacturing data to be interactively fed back, the combination of the operation and maintenance of the complex product and the manufacturing of virtual and real products is realized through digital twins, and more real-time, efficient and intelligent integrated service is provided. The operation mode of the complex product operation and maintenance and manufacturing model based on the digital twin realizes the deep integration of the operation and maintenance of the complex product and the manufacturing process, and eliminates the information island phenomenon generated in the operation, maintenance and manufacturing links.
Description
Technical Field
The invention belongs to the scientific field of intelligent operation and maintenance and manufacturing driven by digital twins, and particularly relates to an integrated overall model for operation, maintenance and manufacturing of a complex product based on the digital twins.
Background
In recent years, a great breakthrough is made in the research of the operation, maintenance and manufacturing aspects of complex products, and the operation, maintenance and manufacturing theoretical system of the complex products is integrated with new information technologies such as big data, cloud computing, internet of things, augmented reality, virtual reality, digital twinning and automation, so that the digitization and intelligence degree of the operation, maintenance and manufacturing system of the complex products is higher and higher. A large amount of data with characteristics of multiple sources, isomerism and the like are generated in the operation, maintenance and manufacturing processes of the complex product, and the data become decision-making bases for reducing the operation and maintenance cost of the complex product, realizing perfect butt joint of the operation, maintenance and manufacturing and ensuring safe and reliable operation of the product. However, at present, there is a serious disjunction between the operation and maintenance of complex products and the manufacture, which results in low reusability of operation and maintenance and manufacture information, and the manufacture data can not effectively serve the operation and maintenance, so how to integrate the operation, maintenance and manufacture links on the same industrial chain, and how to form an organic unity still deserves further research.
A series of product operation and manufacturing development modes, strategies and methods are provided by domestic and foreign research scholars, aiming at the dynamicity and the diversity of the complex product manufacturing requirements, and data generated in the product life cycle management process are researched by the aid of the anyhow (mechanical engineering bulletin (22),194 and 203.), a framework for providing services for the intelligent manufacturing process of complex products by taking the data as a driving force is provided, and the application of the data of each stage of the product life cycle is promoted. Lihao et al (computer integrated manufacturing system, 2019,25(06): 1320-. Quintanlila et al (Engineering Applications of scientific Intelligence,2016,55.) propose a customizable product design specification method based on product manufacturing service, construct a product manufacturing service framework aiming at information reusability in different manufacturing processes, and realize the design and manufacturing data integration of products by creating a product service body; the Toyobo et al (China mechanical engineering, 2020,31(07):757-772.) research the digital-driven complex product intelligent service, provide an equipment fault identification and dynamic prediction method and an equipment maintenance and spare part inventory combined multi-objective decision optimization method according to the situation, and provide a new method for the maintenance and guarantee mode of complex products. Frazzon et al (CIRP Annals-Manufacturing Technology, 2018) proposes a data-driven adaptive planning and control method for a complex product dynamic Manufacturing system, aiming at variable production conditions, realizing data integration between a physical Manufacturing system and a simulation model based on simulation optimization, and providing reference for Manufacturing data integration of complex products. However, the research theories of the operation, maintenance and manufacturing links of complex products by domestic and foreign researchers cannot completely meet the integrated requirement of the operation, maintenance and manufacturing of the complex products under the background of a new generation of information technology, and most research processes mutually isolate the operation, maintenance and manufacturing processes. The research on how to realize the operation, maintenance and manufacturing integration of complex products is relatively less. The operation and maintenance and the manufacture of the product can not be mutually effectively supported, and the real-time dynamic feedback capability of data generated in the actual operation, maintenance and manufacture processes of the product is insufficient. The low integration level of the operation, maintenance and manufacturing processes of the complex product causes the data of the complex product to be presented as an information island in the operation, maintenance and manufacturing processes, the integration difficulty is high, and a large amount of cost is required to process the data in the operation, maintenance and manufacturing processes. And because the operation and maintenance of the complex product and the manufacturing process are mutually dispersed, the generated data are also dispersed, the utilization rate of the data is low, the data generated in the operation and maintenance and manufacturing process can not provide a basis for each other, the smoothness rate of the information in the operation and maintenance and manufacturing process is low, and the operation, maintenance and manufacturing cost is increased. Therefore, a new method is urgently needed to support the deep integration and fusion of the operation and maintenance of the complex product and the manufacturing process, so that the operation and maintenance information of the product is effectively fused into the manufacturing link to promote the overall upgrade of the operation, maintenance and manufacturing of the complex product. The digital twin is used as a technology for fully utilizing data to realize virtual-real interactive feedback and iterative optimization, and an effective way is provided for solving the problem.
The digital twin technology is an important means for providing intelligent service for the whole life cycle of products by taking data as a driving force. The initial concept was taught by professor Grieves (Cocoa Beach: Space Coast Press,2011:2-10.) in the 2003 product full life cycle management course of the united states, and with the development of information technology, the digital twin gradually draws high attention from domestic and foreign scholars and introduces new connotation for the scholars. The Todofei and the like (doi: 10.13196/j.cims.2019.01.001) provide ten application fields of a digital twin five-dimensional model and a digital twin, twin data and two new dimensions of service are added on the basis of an original three-dimensional model, and after years of research, basic theory and technical support are formed in product life cycle management and a product manufacturing workshop. Hu et al (Springer, Singapore,2020: 123-. Wang et al (Zhengli Wu (2020), Model construction of planning and scheduling system based on digital twin.109(7-8),2189-2203.) combine digital twin technology and planning scheduling to manage and control uncertain factors in workshop production.
The traditional complex product operation and maintenance links are separated from the manufacturing links, after a manufacturing plant delivers products to users, tasks of the manufacturing plant are completed, and operation and maintenance personnel rarely consider manufacturing information of the manufacturing plant in the product operation and maintenance process, so that the operation and maintenance information cannot be communicated with the manufacturing information in time, and an information isolated island is formed.
Disclosure of Invention
One purpose of the invention is to provide a digital twin-based integrated overall model of operation, maintenance and manufacturing of a complex product, so as to realize data information intercommunication of the operation, maintenance and manufacturing processes of the complex product.
In order to achieve the purpose, the digital twin-based complex product operation, maintenance and manufacturing integrated overall model provided by the invention adopts the following technical scheme:
the integrated overall model comprises an operation and maintenance system module, a virtual maintenance unit module, a maintenance center module, a manufacturing plant module and a data platform module, wherein the manufacturing plant module and the maintenance center module form a physical space of a twin system, the virtual maintenance unit module forms a virtual space of the twin system, and the operation and maintenance system module, the virtual maintenance unit module, the manufacturing plant module and the maintenance center module respectively transmit service data, simulation data, manufacturing data and maintenance data to the data platform module and receive feedback of the data platform module; the data platform module performs data fusion processing on the acquired data information through the data fusion processing unit and then performs data interaction with other modules, so as to provide data support for normal operation of other modules;
the operation and maintenance system module monitors the normal operation of the complex product, not only provides operation and maintenance service data for the data platform module, but also adjusts the operation and maintenance plan in time after receiving feedback information of the data platform to ensure the normal operation of the product; the service data provided by the operation and maintenance system comprises a product running state, part state information, fault reason types, fault classification standards, field spare part inventory and operation and maintenance plan adjustment data;
the virtual maintenance unit module simulates a part maintenance task by simulating a part to be maintained according to maintenance data acquired from the data platform module, and prepares a preliminary maintenance scheme, and an optimal maintenance scheme is prepared according to rules in a data platform knowledge base, the virtual maintenance unit module guides a maintenance unit module which bears a corresponding maintenance task in a physical space to play a role, and physical maintenance of the part is implemented according to the optimal maintenance scheme;
the maintenance center module carries out physical maintenance on parts of complex products according to an optimal maintenance scheme formulated by the virtual maintenance unit module, and the maintenance is divided into operation field maintenance and maintenance base maintenance;
the manufacturing plant module undertakes the tasks of product manufacturing, part manufacturing and part maintenance needing to be returned to a factory, when the operation and maintenance system uploads abnormal part information to the data platform, the maintenance state of the part is obtained through fault diagnosis, if the manufacturing plant is required to supply spare parts, the manufacturing plant arranges a spare part inventory scheduling plan, if the factory returning maintenance is required, after the manufacturing plant module receives the maintenance information, the maintenance information is taken as an uncertain factor obtained in the production process and is introduced into a daily production plan management and control system, a production plan scheduling scheme of the manufacturing plant is adjusted, and after the rearrangement is carried out in the production plan system, the production plan is formulated; the manufacturing plant arranges the production of spare parts according to the production plan, and finally ensures the normal operation and maintenance of complex products;
the data platform module is used as a data carrier in the operation, maintenance and manufacturing integration process of the complex product, twin data generated by interaction of all modules and physical data related to the operation, maintenance and manufacturing integration are stored, and a knowledge base and an instance base are established and updated by using data acquired from other modules, so that data are provided for the operation, maintenance and manufacturing integration of the complex product; the data platform module is divided into four processes of data acquisition, storage, processing and application.
The complex product operation, maintenance and manufacturing integrated overall model based on the digital twin takes data generated in the operation, maintenance and manufacturing processes as driving, and by means of the digital twin technology, the problems possibly existing in the product operation, maintenance and production plan can be found in time by performing real simulation of the whole information intercommunication process with a maintenance center and a manufacturing plant after the product breaks down in a virtual space. On the basis of integration of operation, maintenance and manufacturing, by means of fusion of twin data depth of a maintenance workshop and a manufacturing workshop and timely updating of data in a data platform, part faults and maintenance plans of future products in the operation process are predicted, normal operation of the products is guaranteed, and intelligent operation, maintenance, manufacturing transformation and upgrading are achieved.
Another object of the present invention is to provide an operation mode of an integrated operation, maintenance and manufacturing global model of a complex product based on digital twinning, comprising:
1) when a complex product is abnormal in the operation process, operation and maintenance personnel need to analyze the reason of the abnormality in an operation and maintenance system, obtain part maintenance information data needing to be maintained through fault diagnosis, and input the part maintenance information data to a data platform module;
2) the data platform module prepares a preliminary maintenance scheme according to historical maintenance data, a knowledge base, an instance base and other data, carries out simulation on the preliminary maintenance scheme in a virtual space, calls the knowledge base in the data platform to judge the preliminary maintenance scheme, continues simulation in the virtual space if the preliminary maintenance scheme is not the optimal maintenance scheme, and corrects the maintenance scheme until the optimal maintenance scheme is formed;
3) implementing the maintenance of the parts according to the optimal maintenance scheme;
4) after the maintenance task of the part to be maintained is completed, the part to be maintained is returned to the product operation site, so that the normal operation of the product is ensured; in the process of processing product abnormity, after a manufacturing plant, an operation site and a maintenance base complete maintenance tasks, updated maintenance data and manufacturing data are input into the data platform, data support is provided for the next maintenance scheme formulation, and a knowledge base and an instance base in the data platform are updated by using related maintenance data. In the data platform, on one hand, a fault prediction mechanism is established according to the processed fault data and the data generated in the maintenance process, and the result of the fault prediction is fed back to the fault diagnosis process, so that the diagnosis efficiency is improved. On the other hand, according to the change of the inventory data of the parts in the fault part maintenance process and the manufacturing data of the parts in the manufacturing factory, an inventory prediction mechanism is established, and inventory part information is provided for the preparation of the optimal maintenance scheme.
Further, in the step 3), the parts are maintained according to the maintenance scheme, the parts are transported to a manufacturing plant for maintenance when needing to be returned to the manufacturing plant, the parts are maintained on a product operation site when needing to be maintained on the site, and the parts are transported to a maintenance base for maintenance when needing to be maintained on the maintenance base.
How to accurately, efficiently and quickly acquire the data related to the operation, maintenance and manufacturing integration from each module of the complex product to generate higher value, and better serve the operation, maintenance and manufacturing integration process is an important hand in the operation, maintenance and manufacturing integration process of the complex product. The data fusion processing unit framework in the application is divided into a data source layer, a data preprocessing layer, a data screening layer and a data application layer:
the data in the data source layer is derived from data collected from an operation and maintenance system module, a manufacturing plant module and a maintenance unit module which are stored in a data platform, wherein the data comprises product state data, part fault data, part repair cycle data, part inventory data, spare inventory data, part fault reason data, part production surrounding data, part maintenance data and the like;
the data preprocessing layer processes data according to the algorithm model, the acquired data are subjected to data processing such as data cleaning, data integration, data conversion and data summarization to obtain preliminary data, the preliminary data are classified into maintenance data and manufacturing data according to the operation, maintenance and manufacturing integration requirements of complex products, and a basis is provided for making maintenance and manufacturing schemes; the algorithm model comprises a neural network, cluster analysis, association analysis and deep learning;
the data screening layer establishes a twin body of a physical maintenance-manufacturing space, namely a virtual maintenance-manufacturing unit, by means of a digital twin technology, a virtual simulation model is established in the unit, a maintenance-manufacturing task is decomposed and evolved, different virtual maintenance and manufacturing schemes are formulated, then maintenance-manufacturing task virtual simulation is performed according to different schemes, and an optimal scheme is decided according to a simulation result; finally, data corresponding to the optimal scheme is obtained and stored in a data platform;
the data application layer feeds data back to the operation and maintenance system module to optimize the operation and maintenance process of the complex product; the data and the optimal scheme are fed back to the maintenance unit module, the physical maintenance unit guides physical maintenance and implements physical part maintenance according to the formulated maintenance scheme, and product failure prediction can be carried out according to the fed-back maintenance data, so that the smooth integration of product operation, maintenance and manufacturing is guaranteed; and feeding back the data to a manufacturer module, and performing part maintenance and product manufacturing of parts needing to be returned to the manufacturer for maintenance according to the prepared maintenance and manufacturing scheme by the manufacturer module.
According to the data fusion processing unit based on the digital twin, the layers are based on the digital twin technology, are driven by data streams, are mutually related and closely matched, the fusion processing of operation and maintenance and manufacturing data of complex products is realized, and important data bases are provided for integration of operation and maintenance and manufacturing.
The fault diagnosis of the parts is a main scientific means for ensuring the safe and reliable operation of the complex products and improving the operation and maintenance efficiency, in order to accurately identify whether the health state of the parts of the complex products meets the operation and maintenance requirements of the products, the intelligent diagnosis gradually replaces the traditional diagnosis method depending on the experience of technicians, and the digital twin technology can well utilize data generated in the operation process of the products and realize the intelligent fault diagnosis through the real-time interaction and feedback of a physical space and a virtual space. The intelligent diagnosis technology based on the digital twin is a diagnosis method for mutually mapping dynamic information in the running process of a product driven by a part fault diagnosis mechanism and data, and a knowledge base and an intelligent diagnosis algorithm in a data platform are used for identifying part faults, so that the intelligent diagnosis model based on the digital twin is further provided, the maintenance cost is reduced, and the running stability of the product is improved.
A intelligent diagnosis model based on digital twins is characterized by firstly, collecting online measured data and historical operating data of key parts of various fault products in a physical space; then, constructing a mapping relation model between the virtual space and the physical space product, performing mechanism analysis on a part model in the virtual space, and extracting corresponding fault characteristics according to dynamic data; finally, analyzing the product fault reason by combining the influence factors through a self-learning optimization process based on an intelligent diagnosis algorithm, and realizing intelligent fault diagnosis of the product; and feeding back the final fault diagnosis result to a physical space for product operation, and providing scientific theoretical guidance for normal operation of complex products.
The influencing factors comprise different products, operating environments, maintenance levels and the like.
In the operation and maintenance process of complex products, the mutual influence and constraint between the fault prediction of parts and the maintenance scheme are one of important restriction factors in the decision of the maintenance scheme. The fault prediction of the complex product is based on the state detection and fault diagnosis of product parts, statistical analysis is carried out on historical data and online monitoring data, and the fault prediction is carried out by adopting a model. The fault prediction is an important guarantee for daily operation, maintenance and emergency treatment of complex products, and is an important factor for ensuring the reliability and the rationality of a complex product maintenance scheme. Therefore, how to effectively predict the failure is a difficulty in the integrated process of operation, maintenance and manufacture of complex products. The method further provides a fault prediction model based on the digital twin, fault state decomposition, fault mode reasoning and fault reason analysis are carried out in a virtual space through fault part identification, prediction results and spare part inventory are jointly optimized, accurate prediction of faults is achieved, timely repair of abnormal parts is guaranteed, and normal operation of products is guaranteed.
Firstly, sensing fault data in a physical space, including historical product operation data and real-time monitoring data, based on a digital twin fault prediction model; secondly, obtaining data with strong correlation with fault prediction by processing the data, establishing a simulation model of the fault part in a virtual space, and performing fault state decomposition, fault mode reasoning and fault reason analysis; then, establishing a corresponding fault prediction model to predict the possible future occurrence times of the fault part; and finally, making a maintenance scheme and scheduling an inventory plan according to the prediction result, performing multi-objective combined virtual implementation optimization on spare part inventory requirements in combination with complex product maintenance, and feeding back the optimized result to a physical space to realize accurate fault prediction based on digital twinning.
The fault prediction model based on the digital twin, which is established by the application, can evolve, evaluate and predict the future fault state of the part in advance under the virtual environment according to the historical fault data of the part, so that the part inventory can be scheduled in time, and the normal operation and maintenance of complex products can be met.
The comprehensive perception of operation, maintenance and manufacturing data is the first premise for realizing the integration of operation, maintenance and manufacturing of complex products, the data perception is the basis for providing services for the integration of operation, maintenance and manufacturing, and intelligent decision is an important means for realizing the value of operation, maintenance and manufacturing data. The data perception network based on the operation and maintenance and manufacturing integration is expected to realize the comprehensive application of data in the operation and maintenance and manufacturing integration process of complex products. Therefore, the invention also provides a complex product operation and maintenance and manufacturing integrated data comprehensive sensing system, which comprises: firstly, the resource sharing standard among a product operation site, a maintenance center and a manufacturing plant is established; secondly, online monitoring is carried out on a product operation site, a maintenance center, a manufacturing plant and the like through a sensor, multi-scale and multi-angle observation is realized, and a complex product sudden abnormity sensing network is constructed; carrying out multi-source heterogeneous data fusion, and establishing a complex product multi-source heterogeneous data comprehensive perception index, a common technology and a standard system; dimension reduction processing and storage of structured and unstructured data.
The complex product operation and maintenance and manufacturing integrated data comprehensive sensing system has the following 5 basic characteristics: the management method can meet the requirements of different place data management in the operation, maintenance and manufacturing processes of various products. Secondly, the dynamic state data of the product can be efficiently managed. And the real-time observation data of the sensor network can be efficiently managed. And fourthly, the real-time dynamic simulation of the maintenance and manufacturing process can be supported. The interrelationship of various products, objects, states, time, processes and other elements can be effectively established.
The complex product operation and maintenance and manufacturing integrated data comprehensive sensing system fully utilizes the integrated sensing architecture to acquire data generated in the complex product operation and maintenance and manufacturing process in real time, and extracts dynamic changes in the operation, maintenance and manufacturing process timely, comprehensively and accurately, so that the data becomes a driving force of the product in the operation and maintenance process from manufacturing to manufacturing, and a data basis is provided for realizing the operation, maintenance and manufacturing integrated operation of the complex product.
The modeling and simulation of the virtual maintenance unit are supported by a digital twin technology, and the virtual-real synchronization between a physical maintenance space and the virtual maintenance unit in the operation, maintenance and manufacturing integration process of the complex product is realized through the organic fusion of data, so that accurate service is provided for the operation, maintenance and manufacturing integration of the complex product. The virtual maintenance unit is an important component of the maintenance process of the complex product, is not only a digital mirror image of the physical maintenance unit, but also an important reference basis for the processes of making a virtual maintenance scheme, data processing, data interaction and the like, so that the coupling degree of the constructed virtual maintenance unit and the physical maintenance unit directly determines the operation, maintenance and manufacturing integration performance of the complex product. The modeling of the virtual maintenance unit and the maintenance operation of the simulated complex product comprise a workflow model of manufacturing, maintenance and operating personnel, a model and inventory data of spare parts, a virtual prototype model and a part fault model of a maintained part, intelligent analysis of a maintenance data processing result based on a digital twin, a simulation knowledge base model and the like. The modeling and simulation of the virtual maintenance unit not only comprises two-dimensional data and a model, but also comprises a three-dimensional model and a simulation process, and is complete information of the part maintenance process. The method can effectively serve the rapid modeling and maintenance of the abnormal parts of the complex products only by establishing data and a simulation model for maintaining the parts of the complex products and realizing the modeling and simulation of the virtual maintenance unit in the digital twin process. The modeling and simulation of the virtual maintenance unit of the complex product provided by the application comprise three aspects: the method comprises the steps of modeling simulation of a physical maintenance unit of the complex product, construction of a simulation knowledge base of a virtual maintenance unit of the complex product and modeling of a key module of the virtual maintenance unit of the complex product.
The virtual maintenance unit module is composed of a geometric model and maintenance data of each maintenance element, and the modeling simulation of the physical maintenance unit is realized by modeling simulation among factors such as people, parts, supporting resources and the like in the maintenance process under the support of a digital twin technology, so that the maintenance task is provided with low cost and high efficiency; the physical objects of the maintenance process modeling simulation are a manufacturing plant, an operation site and a maintenance base for completing the maintenance task of the parts of the complex product, main elements comprise personnel, environment and data modeling of different stages of maintenance task requests, maintenance plans, operation and maintenance scheduling, maintenance execution, completion confirmation and the like, and the mutual behavior relation among the elements is established through different behaviors of the elements, wherein the mutual behavior relation comprises transportation of spare parts, various personnel activities, operation of various workshops and maintenance activities of different parts.
The complex product virtual maintenance unit simulation knowledge base is constructed in order to ensure that the virtual maintenance unit can truly reflect part maintenance, personnel activity and task allocation of the physical maintenance unit, and the complex product virtual maintenance unit simulation knowledge base is constructed and comprises the steps of simulation time step rule determination, maintenance unit division, determination of different-layer modeling tools and calculation of maintenance costs of different maintenance units. And establishing a multilayer structure capable of truly depicting the complex product maintenance unit, thereby effectively describing the structure from the maintenance unit subsystem to the maintenance unit at the component level and forming a full-element simulation knowledge base for the complex product maintenance unit.
The complex product virtual maintenance unit modeling and simulation process relates to people, environment, materials, core equipment and auxiliary equipment, and is a complex system with coupled multiple elements such as equipment, personnel and environment. Through the combination of the dynamic models of the complex product virtual maintenance unit system, the whole maintenance process of the part is analyzed and simulated, the dynamic process of the part maintenance and the evolution rule of the maintenance state are revealed, and the part maintenance efficiency is improved. The complex product virtual maintenance unit key module modeling comprises the steps of conversion and modeling of geometric data of parts to be maintained, modeling of a key part digital prototype, modeling of part maintenance environment and conversion and modeling of data generated in the part maintenance process, and provides a dynamic, programmed and visual simulation analysis platform for performance analysis of complex product part maintenance.
Through the virtual maintenance unit module constructed in the above 3 aspects, the behavior, process, operation and result of the virtual maintenance unit for the maintenance of the physical maintenance unit can be truly simulated, and an important support is provided for the operation and maintenance and manufacturing integration process of complex products.
Because knowledge utilized by the complex product in the operation, maintenance and manufacturing integration process has the characteristics of diversity, complexity, ambiguity and the like, the establishment of the knowledge base/instance base plays an important role in the maintenance and manufacturing of the product. By establishing the knowledge base/the instance base, operation and maintenance personnel can accurately judge and timely process the faults and hidden dangers of the product operation system in the first time. The construction of the knowledge base/the example base is a very complex dynamic, multivariate, nonlinear and huge data volume process, and the knowledge base/the example base is constructed by means of a digital twin technology, so that the knowledge base/the example base plays a role in a virtual space and a physical space, the iteration optimization is carried out in time, the knowledge base/the example base is updated, and the service is better provided for the integration of operation, maintenance and manufacturing of complex products. Therefore, the application also provides a knowledge base/instance base construction method based on the digital twin.
A knowledge base/instance base construction method based on digital twins comprises the steps of data acquisition and evaluation, data mode and classification, data storage and release, data calculation, data sharing and application and six-level realization of virtual realization of the knowledge base/instance base;
the data of the data acquisition and evaluation are derived from simulation data generated by virtual spaces of a product operation site, a maintenance center, a manufacturing plant and a twin system; carrying out data mode and classification on the acquired data; data storage and release are carried out by means of a data platform, and data calculation is carried out by means of a data calculation method; data sharing and application are realized; the expert system, the visual analysis and recommendation system and the historical search module jointly form a knowledge base/instance base.
And applying the formed knowledge base/example base in a virtual space for scheme decision, fault diagnosis, fault prediction, inventory prediction, plan prediction and the like, and feeding back the formed knowledge base/example base to a physical space corresponding to the operation, maintenance and manufacturing of the complex product through iterative update to realize the fusion application of data in the integrated process of the operation, maintenance and manufacturing of the complex product.
The data calculation method comprises data ontology reasoning, rule reasoning, similarity calculation, inconsistency detection and the like.
The digital twin-based complex product operation and maintenance and manufacturing integrated overall model provided by the application provides a digital twin-based complex product operation and maintenance and manufacturing integrated overall framework and a virtual-real integrated system integration model by means of a digital twin technology. Solutions such as operation and maintenance and manufacturing integrated flow operation modes, modeling simulation, data fusion processing and the like based on the digital twin are analyzed, and key technologies such as intelligent diagnosis, fault prediction, data comprehensive perception, knowledge base/instance base construction and the like based on the digital twin are discussed. The invention enables the operation and maintenance of the complex product to be deeply fused with the manufacturing information, the operation and maintenance and the manufacturing data to be interactively fed back, the combination of the operation and maintenance of the complex product and the manufacturing of virtual and real products is realized through digital twins, and more real-time, efficient and intelligent integrated service is provided. The method for integrating the operation, the maintenance and the manufacture of the complex product based on the digital twin realizes the deep integration of the operation, the maintenance and the manufacture of the complex product and eliminates the information island phenomenon generated in the operation, the maintenance and the manufacture links.
Drawings
FIG. 1 is a diagram of an integrated overall model of operation, maintenance and manufacturing of a complex product based on digital twinning;
FIG. 2 is a diagram of an operation mode of a digital twin-based integrated overall model of operation, maintenance and manufacture of a complex product;
FIG. 3 is a diagram of a data fusion processing unit architecture;
FIG. 4 is a diagram of a digital twin-based intelligent diagnostic model;
FIG. 5 is a diagram of a digital twin based fault prediction model;
FIG. 6 is a data comprehensive perception system diagram of an integrated overall model of operation, maintenance and manufacturing of a complex product based on digital twinning;
FIG. 7 is a modeling simulation build diagram of a virtual repair unit module;
FIG. 8 is a diagram of a knowledge base/example base construction method of a digital twin-based complex product operation and maintenance and manufacturing integrated overall model.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications can be made by persons skilled in the art without departing from the spirit of the invention. All falling within the scope of the present invention.
Example 1
As shown in fig. 1, the digital twin-based integrated overall model for operation, maintenance and manufacturing of a complex product comprises an operation and maintenance system module, a virtual maintenance unit module, a maintenance center module, a manufacturing plant module and a data platform module, wherein the manufacturing plant module and the maintenance center module form a physical space of a twin system, the virtual maintenance unit module forms a virtual space of the twin system, and the operation and maintenance system module, the virtual maintenance unit module, the manufacturing plant module and the maintenance center module respectively transmit service data, simulation data, manufacturing data and maintenance data to the data platform module and receive feedback of the data platform module; the data platform module performs data fusion processing on the acquired data information through the data fusion processing unit and then performs data interaction with other modules, so as to provide data support for normal operation of other modules;
the operation and maintenance system module monitors the normal operation of the complex product, not only provides operation and maintenance service data for the data platform module, but also adjusts the operation and maintenance plan in time after receiving feedback information of the data platform to ensure the normal operation of the product; the service data provided by the operation and maintenance system comprises data such as product running state, part state information, fault reason types, fault classification standards, field spare part inventory, operation and maintenance plan adjustment and the like;
the product running state shows whether the product running field has a fault or not, the part state information is important data for tracing the fault reason of the product, and the fault reason type, the fault grade standard and the field spare part inventory data provide important basis for the virtual maintenance unit to formulate a maintenance scheme. The operation and maintenance system module provides original data information for the operation, maintenance and manufacturing integration process timely and efficiently, and data requirements of the operation, maintenance and manufacturing integration process are guaranteed;
the virtual maintenance unit module simulates a part maintenance task to make different maintenance schemes by simulating a part to be maintained according to maintenance data acquired from the data platform module, and makes an optimal maintenance scheme including the maintenance level, the maintenance mode, the maintenance place and the like of the part according to rules in a data platform knowledge base by taking time cost, maintenance cost, process optimization, maintenance route and the like as decision-making bases. The maintenance grades and the maintenance places of different parts are different, some parts need daily maintenance and repair, and some parts need disassembly and repair; some need to be returned to the factory for maintenance, some need to be maintained on the operation site, and some need to be maintained at a maintenance base. After an optimal maintenance scheme is formulated, the virtual maintenance unit module guides a maintenance unit module which bears a corresponding maintenance task in a physical space to play a role, and physical maintenance of parts is implemented according to the optimal maintenance scheme;
the virtual maintenance unit module provides a simulated maintenance place for performing task virtual implementation and maintenance scheme decision to obtain an optimal maintenance scheme, and seamless butt joint, real-time interaction and fusion of a virtual space and a physical space in the product operation and maintenance and manufacturing integration process are achieved. In the decision making process, on one hand, a virtual maintenance scheme decision model is established in the virtual maintenance unit according to maintenance data, on the other hand, the visual result of the virtual maintenance scheme is combined with the physical environment of the physical maintenance unit, and organized and logical interaction is carried out through a virtual-real combination and binary decision main body. A maintenance scheme decision based on digital twin forms a continuous improvement mode of mutual adjustment, mutual learning and mutual adaptation through the virtual maintenance unit module and the maintenance center module, the scientificity and objectivity of a decision result are ensured by mutually connecting the virtual world and the physical world, a 'virtual-real' cooperative decision is formed, and the maintenance efficiency and the maintenance quality are improved. The virtual maintenance unit module is used for making a preliminary virtual maintenance scheme according to maintenance and manufacturing data information acquired from the data platform, and then deciding an optimal maintenance scheme according to rules in a data platform knowledge base so as to guide a physical maintenance process.
The maintenance center module performs physical maintenance on parts of complex products according to maintenance schemes formulated by the virtual maintenance unit module, and the maintenance is divided into operation site maintenance and maintenance base maintenance. And conveying the parts to the maintenance base for maintenance, such as regular overhaul, equipment maintenance, part replacement, flaw detection overhaul, system overhaul and the like.
The maintenance center module is an important place for realizing the physical maintenance task of the parts, so the maintenance efficiency of the maintenance center directly influences the operation and maintenance efficiency of the product. The maintenance center module carries out physical maintenance according to a maintenance scheme formulated by the virtual maintenance module on the basis of considering spare part inventory, and the on-time requirement of parts in the product operation and maintenance process is guaranteed.
The manufacturing plant module undertakes the tasks of product manufacturing, part manufacturing and part maintenance needing to be returned to the factory for maintenance, when the operation and maintenance system uploads abnormal part information to the data platform, the maintenance state of the part is obtained through fault diagnosis, if the manufacturing plant is required to supply spare parts, the manufacturing plant arranges a spare part inventory scheduling plan, if the factory returning maintenance is required, the manufacturing plant module takes the maintenance information as uncertain factors obtained in the production process and introduces the uncertain factors into a daily production plan management and control system after receiving the maintenance information, the production plan scheduling scheme of the manufacturing plant is adjusted, and the production plan is formulated after the rearrangement is carried out in the production plan system; the manufacturing plant arranges the production of spare parts according to the production plan, and finally ensures the normal operation and maintenance of complex products; the manufacturing plant realizes the information interaction with the operation and maintenance system through the data platform, and provides more real-time, efficient and intelligent service for the smooth operation of the operation and maintenance and manufacturing integration of complex products. The manufacturer module implements manufacture of other parts of the parts returned to the manufacturer according to the manufacture data information acquired from the data platform and the maintenance scheme formulated by the virtual maintenance module, so that the requirement of product spare part inventory is met, corresponding product manufacture is arranged according to the requirement information of the products on the market, and the normal requirement of the market is met.
The data platform module is used as a data carrier in the operation, maintenance and manufacturing integration process of the complex product, twin data generated by interaction of all modules and physical data related to the operation, maintenance and manufacturing integration are stored, and a knowledge base and an instance base are established by using data acquired from other modules, so that data are provided for the operation, maintenance and manufacturing integration of the complex product.
The data platform module comprises four processes of data acquisition, storage, processing and application, corresponding maintenance and manufacturing data are acquired from the operation and maintenance system module, the virtual maintenance unit module, the maintenance center module and the manufacturing plant module, product state data and part state data are acquired from the operation and maintenance system module, factory return maintenance cycle data and part production cycle data are acquired from the manufacturing plant, inventory data of an operation site and a maintenance base are acquired from the maintenance center, and simulation data are acquired from the virtual maintenance unit. The data such as the product state, spare part inventory, fault reason, fault classification, repair cycle, production cycle and the like are stored in the data platform and are fed back to the corresponding modules after the data fusion processing, so that the operation efficiency of each module is improved. The data platform is further used for comprehensively applying various collected data, a knowledge base can be established by using the data collected from other modules, decision rule service is provided for decision of a maintenance scheme in the virtual maintenance unit, a historical example base and a maintenance knowledge base can be established by using twin data generated in the maintenance and manufacturing processes, and professional guidance is provided for later similar part maintenance tasks. And the collected data can be used for carrying out inventory prediction, fault prediction and plan prediction, and timely handling the abnormity generated in the operation, maintenance and manufacturing integration process. In a word, the data platform provides a full-element, full-flow and full-service data fusion processing and sharing platform for the operation, maintenance and manufacturing integration of products, eliminates an information isolated island generated in the process of the operation, maintenance and manufacturing integration of complex products, and realizes perfect butt joint of the operation, maintenance and manufacturing.
Example 2
As shown in fig. 2, an operation mode of an integrated overall model of operation, maintenance and manufacturing of complex products based on digital twin includes:
1) when a complex product is abnormal in the operation process, operation and maintenance personnel need to analyze the reason of the abnormality in an operation and maintenance system, obtain part maintenance information data needing to be maintained through fault diagnosis, and input the part maintenance information data to a data platform module;
2) the data platform module prepares a preliminary maintenance scheme according to historical maintenance data, a knowledge base, an instance base and other data, carries out simulation on the preliminary maintenance scheme in a virtual space, calls the knowledge base in the data platform to judge the preliminary maintenance scheme, continues simulation in the virtual space if the preliminary maintenance scheme is not the optimal maintenance scheme, and corrects the maintenance scheme until the optimal maintenance scheme is formed;
3) implementing the maintenance of the parts according to the optimal maintenance scheme;
4) after the maintenance task of the part to be maintained is completed, the part to be maintained is returned to the product operation site, so that the normal operation of the product is ensured; in the process of processing product abnormity, after a manufacturing plant, an operation site and a maintenance base complete maintenance tasks, updated maintenance data and manufacturing data are input into the data platform, data support is provided for the next maintenance scheme formulation, and a knowledge base and an instance base in the data platform are updated by using related maintenance data. In the data platform, on one hand, a fault prediction mechanism is established according to the processed fault data and the data generated in the maintenance process, and the result of the fault prediction is fed back to the fault diagnosis process, so that the diagnosis efficiency is improved. On the other hand, according to the change of the inventory data of the parts in the fault part maintenance process and the manufacturing data of the parts in the manufacturing factory, an inventory prediction mechanism is established, and inventory part information is provided for the preparation of the optimal maintenance scheme.
How to accurately, efficiently and quickly acquire the data related to the operation, maintenance and manufacturing integration from each module of the complex product to generate higher value, and better serve the operation, maintenance and manufacturing integration process is an important hand in the operation, maintenance and manufacturing integration process of the complex product. Data based on digital twinning, as shown in FIG. 3Fusion processThe unit architecture is divided into a data source layer, a data preprocessing layer, a data screening layer and a data application layer, digital twin technology is used as a basis among the layers, data streams are used as driving, the data streams are mutually related and closely matched, the fusion processing of operation and maintenance and manufacturing data of complex products is realized, and important data basis is provided for operation and maintenance and manufacturing integration.
(1) A data source layer. The data in the data source layer is derived from data collected from an operation and maintenance system module, a manufacturing plant module and a maintenance unit module which are stored in a data platform, and comprises product state data, part fault data, part repair cycle data, part inventory data, spare inventory data, part fault reason data, part production surrounding data, part maintenance data and the like;
(2) and a data preprocessing layer. Because data generated in the operation, maintenance and manufacturing processes of complex products have characteristics of multiple elements, isomerism and the like, part of the data cannot serve the operation, maintenance and manufacturing integration, and corresponding data needs to be preprocessed; the data are processed according to algorithm models such as neural networks, cluster analysis, association analysis and deep learning, the collected data are subjected to data processing such as data cleaning, data integration, data conversion and data summarization to obtain preliminary data, the preliminary data are classified into maintenance data and manufacturing data according to the operation and maintenance and manufacturing integration requirements of complex products, and a basis is provided for making maintenance and manufacturing schemes.
(3) And (4) a data screening layer. The preprocessed data can be applied to the complex product operation, maintenance and manufacturing integration process, but different data have different schemes formulated in the virtual maintenance-manufacturing unit, so that the data need to be screened in order to obtain the optimal maintenance and manufacturing scheme. A physical maintenance-manufacturing space twin body, namely a virtual maintenance-manufacturing unit, is established by means of a digital twin technology, a virtual simulation model is established in the unit, a maintenance-manufacturing task is decomposed and evolved, different virtual maintenance and manufacturing schemes are formulated, then maintenance-manufacturing task virtual implementation (simulation) is carried out according to the different schemes, and an optimal scheme is decided according to a simulation result. And finally, obtaining data corresponding to the optimal scheme and storing the data in a data platform. Through the process, the most useful data in the operation, maintenance and manufacturing integration process is obtained.
(4) And a data application layer. The source data generated in the operation, maintenance and manufacturing processes of the complex product are preprocessed and screened to obtain data corresponding to the optimal scheme, and the data are fed back to different modules, so that the implementation of the operation, maintenance and manufacturing processes is guaranteed. The data are fed back to the operation and maintenance system module, so that the operation and maintenance process of the complex product is optimized, the operation and maintenance cost is reduced, and the service level is improved; the data and the optimal scheme are fed back to the maintenance unit module, the physical maintenance unit guides physical maintenance and implements physical part maintenance according to the formulated maintenance scheme, and product failure prediction can be carried out according to the fed-back maintenance data, so that the smooth integration of product operation, maintenance and manufacturing is guaranteed; and feeding back the data to a manufacturer module, and performing part maintenance and product (part) manufacturing of parts needing to be returned to the manufacturer for maintenance according to the prepared maintenance and manufacturing scheme by the manufacturer module.
The fault diagnosis of the parts is a main scientific means for ensuring the safe and reliable operation of the complex products and improving the operation and maintenance efficiency, in order to accurately identify whether the health state of the parts of the complex products meets the operation and maintenance requirements of the products, the intelligent diagnosis gradually replaces the traditional diagnosis method depending on the experience of technicians, and the digital twin technology can well utilize data generated in the operation process of the products and realize the intelligent fault diagnosis through the real-time interaction and feedback of a physical space and a virtual space. The intelligent diagnosis technology based on the digital twin is a diagnosis method for mutual mapping of dynamic information in a product operation process driven by a part fault diagnosis mechanism and data, a knowledge base in a data platform and an intelligent diagnosis algorithm are utilized to identify part faults, and an intelligent diagnosis model based on the digital twin is established, as shown in fig. 4:
firstly, collecting online measured data and historical operating data of various fault product key parts in a physical space (including a maintenance site and an operating site); then, constructing a mapping relation model (part physical entity → part virtual model) between the virtual space and the physical space product, performing mechanism analysis on the part model in the virtual space, and extracting corresponding fault features according to dynamic data; finally, a self-learning optimization process based on a (complex product) intelligent diagnosis algorithm is performed, wherein the complex product intelligent fault diagnosis algorithm comprises the following steps: the method comprises the following steps of analyzing product fault reasons by combining different products, operation environments, maintenance levels and other influence factors, and realizing intelligent fault diagnosis of the products, wherein the deep confidence neural network (DBN), the Deep Convolution Neural Network (DCNN), the Artificial Neural Network (ANN), the Support Vector Machine (SVM) and the like; and feeding back the final fault diagnosis result to a physical space for product operation, and providing scientific theoretical guidance for normal operation of complex products.
In the operation and maintenance process of complex products, the mutual influence and constraint between the fault prediction of parts and the maintenance scheme are one of important restriction factors in the decision of the maintenance scheme. The fault prediction of the complex product is based on the state detection and fault diagnosis of product parts, statistical analysis is carried out on historical data and online monitoring data, and the fault prediction is carried out by adopting a model. The fault prediction is an important guarantee for daily operation, maintenance and emergency treatment of complex products, and is an important factor for ensuring the reliability and the rationality of a complex product maintenance scheme. Therefore, how to effectively predict the failure is a difficulty in the integrated process of operation, maintenance and manufacture of complex products. The method further provides a fault prediction model based on the digital twin, fault state decomposition, fault mode reasoning and fault reason analysis are carried out in a virtual space through fault part identification, prediction results and spare part inventory are jointly optimized, accurate prediction of faults is achieved, timely repair of abnormal parts is guaranteed, and normal operation of products is guaranteed.
As shown in fig. 5, based on the digital twin spare part inventory prediction model, firstly sensing fault data in a physical space, including historical product operation data and real-time monitoring data; secondly, obtaining data with strong correlation with fault prediction by processing the data, establishing a simulation model of the fault part in a virtual space, and performing fault state decomposition, fault mode reasoning and fault reason analysis; then, establishing a corresponding fault prediction model, such as a Deep Neural Network (DNN) and a Recurrent Neural Network (RNN), and predicting the possible future occurrence times of the fault part; and finally, making a maintenance scheme and scheduling an inventory plan according to the prediction result, performing multi-objective combined virtual implementation optimization on spare part inventory requirements in combination with complex product maintenance, and feeding back the optimized result to a physical space to realize accurate fault prediction based on digital twinning. The established fault prediction system framework can evolve, evaluate and predict the future fault state of the part in advance under the virtual environment according to the historical fault data of the part, so that the part inventory can be scheduled in time, and the normal operation and maintenance of complex products can be met.
The comprehensive perception of operation, maintenance and manufacturing data is the first premise for realizing the integration of operation, maintenance and manufacturing of complex products, the data perception is the basis for providing services for the integration of operation, maintenance and manufacturing, and intelligent decision is an important means for realizing the value of operation, maintenance and manufacturing data. The data perception network based on the operation and maintenance and manufacturing integration is expected to realize the comprehensive application of data in the operation and maintenance and manufacturing integration process of complex products. As shown in fig. 6, the operation, maintenance, and manufacturing integrated data comprehensive sensing system for complex products in this embodiment includes: firstly, the resource sharing standard among a product operation site, a maintenance center and a manufacturing plant is established; secondly, online monitoring is carried out on a product operation site, a maintenance center, a manufacturing plant and the like through a sensor, multi-scale and multi-angle observation is realized, and a complex product sudden abnormity sensing network is constructed; carrying out multi-source heterogeneous data fusion, and establishing a complex product multi-source heterogeneous data comprehensive perception index, a common technology and a standard system; dimension reduction processing and storage of structured and unstructured data.
The complex product integration data comprehensive perception framework has the following 5 basic characteristics: the management method can meet the requirements of different place data management in the operation, maintenance and manufacturing processes of various products. Secondly, the dynamic state data of the product can be efficiently managed. And the real-time observation data of the sensor network can be efficiently managed. And fourthly, the real-time dynamic simulation of the maintenance and manufacturing process can be supported. The interrelationship of various products, objects, states, time, processes and other elements can be effectively established.
The operation, maintenance and manufacturing integrated data comprehensive sensing fully utilizes the integrated sensing framework to acquire data generated in the operation, maintenance and manufacturing processes of complex products in real time, and extracts dynamic changes in the operation, maintenance and manufacturing processes timely, comprehensively and accurately, so that the data becomes a driving force in the operation, maintenance and manufacturing processes of the products, and a data basis is provided for realizing the operation, maintenance and manufacturing integrated operation of the complex products.
The modeling and simulation of the virtual maintenance unit are supported by a digital twin technology, and the virtual-real synchronization between a physical maintenance space and the virtual maintenance unit in the operation, maintenance and manufacturing integration process of the complex product is realized through the organic fusion of data, so that accurate service is provided for the operation, maintenance and manufacturing integration of the complex product. The virtual maintenance unit is an important component of the maintenance process of the complex product, is not only a digital mirror image of the physical maintenance unit, but also an important reference basis for the processes of making a virtual maintenance scheme, data processing, data interaction and the like, so that the coupling degree of the constructed virtual maintenance unit and the physical maintenance unit directly determines the operation, maintenance and manufacturing integration performance of the complex product. The modeling of the virtual maintenance unit and the maintenance operation of the simulated complex product comprise a workflow model of manufacturing, maintenance and operating personnel, a model and inventory data of spare parts, a virtual prototype model and a part fault model of a maintained part, intelligent analysis of a maintenance data processing result based on a digital twin, a simulation knowledge base model and the like. The modeling and simulation of the virtual maintenance unit not only comprises two-dimensional data and a model, but also comprises a three-dimensional model and a simulation process, and is complete information of the part maintenance process. The method can effectively serve the rapid modeling and maintenance of the abnormal parts of the complex products only by establishing data and a simulation model for maintaining the parts of the complex products and realizing the modeling and simulation of the virtual maintenance unit in the digital twin process. As shown in fig. 7, the complex product virtual repair unit modeling and simulation provided by the present application includes three aspects: the method comprises the steps of modeling simulation of a physical maintenance unit of the complex product, construction of a simulation knowledge base of a virtual maintenance unit of the complex product and modeling of a key module of the virtual maintenance unit of the complex product.
The virtual maintenance unit module is composed of a geometric model and maintenance data of each maintenance element, and the modeling simulation of the physical maintenance unit is realized by modeling simulation among factors such as people, parts, supporting resources and the like in the maintenance process under the support of a digital twin technology, so that the maintenance task is provided with low cost and high efficiency; the physical objects of the maintenance process modeling simulation are a manufacturing plant, an operation site and a maintenance base for completing the maintenance task of the parts of the complex product, main elements comprise personnel, environment and data modeling of different stages of maintenance task requests, maintenance plans, operation and maintenance scheduling, maintenance execution, completion confirmation and the like, and the mutual behavior relation among the elements is established through different behaviors of the elements, wherein the mutual behavior relation comprises transportation of spare parts, various personnel activities, operation of various workshops and maintenance activities of different parts.
The complex product virtual maintenance unit simulation knowledge base is constructed in order to ensure that the virtual maintenance unit can truly reflect part maintenance, personnel activity and task allocation of the physical maintenance unit, and the complex product virtual maintenance unit simulation knowledge base is constructed and comprises the steps of simulation time step rule determination, maintenance unit division, determination of different-layer modeling tools and calculation of maintenance costs of different maintenance units. And establishing a multilayer structure capable of truly depicting the complex product maintenance unit, thereby effectively describing the structure from the maintenance unit subsystem to the maintenance unit at the component level and forming a full-element simulation knowledge base for the complex product maintenance unit.
The complex product virtual maintenance unit modeling and simulation process relates to people, environment, materials, core equipment and auxiliary equipment, and is a complex system with coupled multiple elements such as equipment, personnel and environment. Through the combination of the dynamic models of the complex product virtual maintenance unit system, the whole maintenance process of the part is analyzed and simulated, the dynamic process of the part maintenance and the evolution rule of the maintenance state are revealed, and the part maintenance efficiency is improved. The complex product virtual maintenance unit key module modeling comprises the steps of conversion and modeling of geometric data of parts to be maintained, modeling of a key part digital prototype, modeling of part maintenance environment and conversion and modeling of data generated in the part maintenance process, and provides a dynamic, programmed and visual simulation analysis platform for performance analysis of complex product part maintenance.
Through the virtual maintenance unit constructed in the above 3 aspects, the behavior, process, operation and result of the maintenance of the physical maintenance unit by the virtual maintenance unit can be truly simulated, and an important support is provided for the operation, maintenance and manufacturing integration process of complex products.
Because knowledge utilized by the complex product in the operation, maintenance and manufacturing integration process has the characteristics of diversity, complexity, ambiguity and the like, the establishment of the knowledge base/instance base plays an important role in the maintenance and manufacturing of the product. By establishing the knowledge base/the instance base, operation and maintenance personnel can accurately judge and timely process the faults and hidden dangers of the product operation system in the first time. The construction of the knowledge base/the example base is a very complex dynamic, multivariate, nonlinear and huge data volume process, and the knowledge base/the example base is constructed by means of a digital twin technology, so that the knowledge base/the example base plays a role in a virtual space and a physical space, the iteration optimization is carried out in time, the knowledge base/the example base is updated, and the service is better provided for the integration of operation, maintenance and manufacturing of complex products.
As shown in fig. 8, the knowledge base/instance base construction based on the digital twin is divided into six levels of data acquisition and evaluation, data mode and classification, data storage and release, data calculation, data sharing and application, and knowledge base/instance base virtual implementation. The established data of the knowledge base/database is derived from simulation data generated by virtual spaces of a product operation site, a maintenance center, a manufacturing plant and a twin system; the collected data are classified, and the data comprise daily maintenance, fault maintenance, maintenance records, abnormal reports, search history and the like; data storage and release are carried out by means of a data platform, and data calculation is carried out by means of data calculation methods such as data ontology reasoning, rule reasoning, similarity calculation, inconsistency detection and the like; the knowledge base/instance base is formed by modules for realizing data sharing and application, an expert system, a visual analysis module, a recommendation system, historical search module and the like. And applying the formed knowledge base/example base in a virtual space for scheme decision, fault diagnosis, fault prediction, inventory prediction, plan prediction and the like, and feeding back the formed knowledge base/example base to a physical space corresponding to the operation, maintenance and manufacturing of the complex product through iterative update to realize the fusion application of data in the integrated process of the operation, maintenance and manufacturing of the complex product.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention.
Claims (10)
1. The operation, maintenance and manufacturing integrated overall model of the complex product based on the digital twin is characterized by comprising an operation and maintenance system module, a virtual maintenance unit module, a maintenance center module, a manufacturing plant module and a data platform module, wherein the operation and maintenance system module, the virtual maintenance unit module, the manufacturing plant module and the maintenance center module respectively transmit service data, simulation data, manufacturing data and maintenance data to the data platform module and receive feedback of the data platform module; the manufacturing plant module and the maintenance center module form a physical space of the twin system, the virtual maintenance unit module forms a virtual space of the twin system, and the data platform module performs data fusion processing on the acquired data information through the data fusion processing unit and then performs data interaction with other modules to provide data support for normal operation of other modules;
the operation and maintenance system module monitors the normal operation of the complex product, not only provides operation and maintenance service data for the data platform module, but also adjusts the operation and maintenance plan in time after receiving feedback information of the data platform to ensure the normal operation of the product; the service data provided by the operation and maintenance system comprises a product running state, part state information, fault reason types, fault classification standards, field spare part inventory and operation and maintenance plan adjustment data;
the virtual maintenance unit module simulates a part maintenance task by simulating a part to be maintained according to maintenance data acquired from the data platform module, and prepares a preliminary maintenance scheme, and an optimal maintenance scheme is prepared according to rules in a data platform knowledge base, the virtual maintenance unit module guides a maintenance unit module which bears a corresponding maintenance task in a physical space to play a role, and physical maintenance of the part is implemented according to the optimal maintenance scheme;
the maintenance center module carries out physical maintenance on parts of complex products according to an optimal maintenance scheme formulated by the virtual maintenance unit module, and the maintenance is divided into operation field maintenance and maintenance base maintenance;
the manufacturing plant module undertakes the tasks of product manufacturing, part manufacturing and part maintenance needing to be returned to the factory for maintenance, when the operation and maintenance system uploads abnormal part information to the data platform, the maintenance state of the part is obtained through fault diagnosis, if the manufacturing plant is required to supply spare parts, the manufacturing plant arranges a spare part inventory scheduling plan, if the factory returning maintenance is required, the manufacturing plant module takes the maintenance information as uncertain factors obtained in the production process and introduces the uncertain factors into a daily production plan management and control system after receiving the maintenance information, the production plan scheduling scheme of the manufacturing plant is adjusted, and the production plan is formulated after the rearrangement is carried out in the production plan system; the manufacturing plant arranges the production of spare parts according to the production plan, and finally ensures the normal operation and maintenance of complex products;
the data platform module is used as a data carrier in the operation, maintenance and manufacturing integration process of the complex product, twin data generated by interaction of all modules and physical data related to the operation, maintenance and manufacturing integration are stored, and a knowledge base and an instance base are established and updated by using data acquired from other modules, so that data are provided for the operation, maintenance and manufacturing integration of the complex product; the data platform module is divided into four processes of data acquisition, storage, processing and application.
2. The operation mode of the integrated operation, maintenance and manufacturing overall model of the complex product based on the digital twin as claimed in claim 1, is characterized by comprising:
1) when a complex product is abnormal in the operation process, operation and maintenance personnel need to analyze the reason of the abnormality in an operation and maintenance system, obtain part maintenance information data needing to be maintained through fault diagnosis, and input the part maintenance information data to a data platform module;
2) the data platform module prepares a preliminary maintenance scheme according to the historical maintenance data, the knowledge base and the instance base data, carries out simulation on the preliminary maintenance scheme in the virtual space, calls the knowledge base in the data platform to judge the preliminary maintenance scheme, continues simulation in the virtual space if the preliminary maintenance scheme is not the optimal maintenance scheme, and corrects the maintenance scheme until the optimal maintenance scheme is formed;
3) implementing the maintenance of the parts according to the optimal maintenance scheme;
4) after the maintenance task of the part to be maintained is completed, the part to be maintained is returned to the product operation site, so that the normal operation of the product is ensured; in the process of processing product abnormity, after a manufacturing plant, an operation site and a maintenance base finish maintenance tasks, updated maintenance data and manufacturing data are input into a data platform to provide data support for the next maintenance scheme formulation, and a knowledge base and an instance base in the data platform are updated by using related maintenance data; in the data platform, on one hand, a fault prediction mechanism is established according to the processed fault data and the data generated in the maintenance process, and the result of the fault prediction is fed back to the fault diagnosis process, so that the diagnosis efficiency is improved; on the other hand, according to the change of the inventory data of the parts in the fault part maintenance process and the manufacturing data of the parts in the manufacturing factory, an inventory prediction mechanism is established, and inventory part information is provided for the preparation of the optimal maintenance scheme.
3. The operation mode of the integrated operation and maintenance and manufacturing overall model of complex product based on digital twin as claimed in claim 2, wherein in step 3), the maintenance of the part is performed according to the maintenance scheme, the part is transported to the manufacturing plant for maintenance, the part is transported to the product operation site for maintenance on site, and the part is transported to the maintenance site for maintenance on the maintenance site.
4. The operation mode of the integrated operation, maintenance and manufacturing overall model of the complex product based on the digital twin as claimed in claim 2, wherein the data fusion processing unit architecture is divided into a data source layer, a data preprocessing layer, a data screening layer and a data application layer:
the data in the data source layer is derived from data which are stored in a data platform and collected from an operation and maintenance system module, a manufacturing plant module and a maintenance unit module, wherein the data comprise product state data, part fault data, part repair cycle data, part inventory data, spare inventory data, part fault reason data, part production surrounding data and part maintenance data;
the data preprocessing layer processes data according to the algorithm model, the acquired data are subjected to data processing such as data cleaning, data integration, data conversion and data summarization to obtain preliminary data, the preliminary data are classified into maintenance data and manufacturing data according to the operation, maintenance and manufacturing integration requirements of complex products, and a basis is provided for making maintenance and manufacturing schemes; the algorithm model comprises a neural network, cluster analysis, association analysis and deep learning;
the data screening layer establishes a twin body of a physical maintenance-manufacturing space, namely a virtual maintenance-manufacturing unit, by means of a digital twin technology, a virtual simulation model is established in the unit, a maintenance-manufacturing task is decomposed and evolved, different virtual maintenance and manufacturing schemes are formulated, then maintenance-manufacturing task virtual simulation is performed according to different schemes, and an optimal scheme is decided according to a simulation result; finally, data corresponding to the optimal scheme is obtained and stored in a data platform;
the data application layer feeds data back to the operation and maintenance system module to optimize the operation and maintenance process of the complex product; the data and the optimal scheme are fed back to the maintenance unit module, the physical maintenance unit guides physical maintenance and implements physical part maintenance according to the formulated maintenance scheme, and product failure prediction can be carried out according to the fed-back maintenance data, so that the smooth integration of product operation, maintenance and manufacturing is guaranteed; and feeding back the data to a manufacturer module, and performing part maintenance and product manufacturing of parts needing to be returned to the manufacturer for maintenance according to the prepared maintenance and manufacturing scheme by the manufacturer module.
5. The operation mode of the digital twin-based integrated overall model for operation, maintenance and manufacturing of complex products according to claim 2 is characterized in that a digital twin-based intelligent diagnosis model is provided for the fault diagnosis, and firstly, online measured data and historical operation data of various fault product key parts in a physical space are collected; then, constructing a mapping relation model between the virtual space and the physical space product, performing mechanism analysis on a part model in the virtual space, and extracting corresponding fault characteristics according to dynamic data; finally, analyzing the product fault reason by combining the influence factors through a self-learning optimization process based on an intelligent diagnosis algorithm, and realizing intelligent fault diagnosis of the product; and feeding back the final fault diagnosis result to a physical space for product operation, and providing scientific theoretical guidance for normal operation of complex products.
6. The operation mode of the digital twin-based integrated overall model for operation, maintenance and manufacturing of the complex product according to claim 2 is characterized in that a digital twin-based fault prediction model is provided for the fault prediction, and firstly, fault data in a physical space are sensed, wherein the fault data comprise historical operation data and real-time monitoring data of the product; secondly, obtaining data with strong correlation with fault prediction by processing the data, establishing a simulation model of the fault part in a virtual space, and performing fault state decomposition, fault mode reasoning and fault reason analysis; then, establishing a corresponding fault prediction model to predict the possible future occurrence times of the fault part; and finally, making a maintenance scheme and scheduling an inventory plan according to the prediction result, performing multi-objective combined virtual implementation optimization on spare part inventory requirements in combination with complex product maintenance, and feeding back the optimized result to a physical space to realize accurate fault prediction based on digital twinning.
7. The integrated data perception system for the integrated operation, maintenance and manufacturing overall model of complex products based on digital twins as claimed in claim 1, comprising: firstly, the resource sharing standard among a product operation site, a maintenance center and a manufacturing plant is established; secondly, online monitoring is carried out on a product operation site, a maintenance center and a manufacturing plant through sensors, multi-scale and multi-angle observation is achieved, and a complex product sudden abnormity sensing network is constructed; carrying out multi-source heterogeneous data fusion, and establishing a complex product multi-source heterogeneous data comprehensive perception index, a common technology and a standard system; dimension reduction processing and storage of structured and unstructured data.
8. The modeling simulation construction of the virtual maintenance unit module of the integrated operation, maintenance and manufacturing overall model of the complex product based on the digital twin according to claim 1 is characterized by comprising three aspects of modeling simulation of a physical maintenance unit of the complex product, construction of a simulation knowledge base of the virtual maintenance unit of the complex product and modeling of a key module of the virtual maintenance unit of the complex product;
the modeling of the virtual maintenance unit and the maintenance operation of the simulated complex product comprise a workflow model of manufacturing, maintenance and operating personnel, a model and inventory data of spare parts, a virtual prototype model and a part fault model of a maintenance part, and an intelligent analysis and simulation knowledge base model of a maintenance data processing result based on a digital twin;
the modeling simulation of the complex product physical maintenance unit is supported by a digital twin technology, and the low-cost and high-efficiency provision of maintenance tasks is realized by modeling simulation among people, parts and matched resource elements in the maintenance process; the physical objects of the maintenance process modeling simulation are a manufacturing plant, an operation site and a maintenance base for completing the maintenance task of the parts of the complex product, the main elements comprise maintenance task requests, maintenance plans, operation and maintenance scheduling, personnel, environment and data modeling at different stages of maintenance execution and completion confirmation, and the mutual behavior relation among the elements is established through different behaviors of the elements, wherein the mutual behavior relation comprises the transportation of spare parts, various personnel activities, the operation of each workshop and the maintenance activities of different parts;
the complex product virtual maintenance unit simulation knowledge base is constructed in order to ensure that the virtual maintenance unit can truly reflect part maintenance, personnel activity and task allocation of a physical maintenance unit, and the complex product virtual maintenance unit simulation knowledge base is constructed, and comprises the steps of simulation time step rule determination, maintenance unit division, determination of different-layer modeling tools and calculation of maintenance costs of different maintenance units; establishing a multilayer structure capable of truly depicting a complex product maintenance unit, thereby effectively describing the structure from a maintenance unit subsystem to a maintenance unit at a component level and forming a full-element simulation knowledge base facing the complex product maintenance unit;
the complex product virtual maintenance unit modeling and simulation process relates to people, environment, materials, core equipment and auxiliary equipment, and is a complex system with coupled equipment, personnel and environment with multiple elements; through the combination of the dynamic models of the complex product virtual maintenance unit system, the whole maintenance process of the part is analyzed and simulated, the dynamic process of the part maintenance and the evolution rule of the maintenance state are revealed, and the part maintenance efficiency is improved; the complex product virtual maintenance unit key module modeling comprises the steps of conversion and modeling of geometric data of parts to be maintained, modeling of a key part digital prototype, modeling of part maintenance environment and conversion and modeling of data generated in the part maintenance process, and provides a dynamic, programmed and visual simulation analysis platform for performance analysis of complex product part maintenance.
9. The method for constructing the knowledge base/instance base of the digital twin-based integrated overall model for operation, maintenance and manufacturing of the complex product according to claim 1 is characterized by comprising six levels of data acquisition and evaluation, data mode and classification, data storage and release, data calculation, data sharing and application and virtual realization of the knowledge base/instance base;
the data of the data acquisition and evaluation are derived from simulation data generated by virtual spaces of a product operation site, a maintenance center, a manufacturing plant and a twin system; carrying out data mode and classification on the acquired data; data storage and release are carried out by means of a data platform, and data calculation is carried out by means of a data calculation method; data sharing and application are realized; the expert system, the visual analysis and recommendation system and the historical search module jointly form a knowledge base/instance base.
10. The method for constructing the knowledge base/instance base of the digital twin-based integrated overall model for the operation, maintenance and manufacturing of the complex product according to claim 9, wherein the formed knowledge base/instance base is applied to scheme decision, fault diagnosis, fault prediction, inventory prediction and plan prediction in a virtual space, and the formed knowledge base/instance base is fed back to a physical space corresponding to the operation, maintenance and manufacturing of the complex product through iterative update, so that the fusion application of data in the integrated process of the operation, maintenance and manufacturing of the complex product is realized.
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