CN114077235A - Equipment predictive maintenance system and method based on digital twin technology - Google Patents
Equipment predictive maintenance system and method based on digital twin technology Download PDFInfo
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- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention provides a device predictive maintenance system and a device predictive maintenance method based on a digital twin technology, which comprise the following steps: the communication module is used for completing data real-time connection of all equipment; the protocol conversion module is used for unifying industrial equipment with different protocol standards; the three-dimensional scene module is used for carrying out high-precision three-dimensional modeling reduction on the industrial equipment; the engine rendering module is used for loading static three-dimensional model data and endowing specific physical, geometric, behavioral and regular attributes; the data fusion module is used for importing a predictive maintenance algorithm into the digital twin platform to provide calculation and driving the update of the digital twin; and the visualization module is used for calling the simulation result to perform visualization display and experience. The operation and maintenance work of maintenance and maintenance of the industrial internet industry according to the specific conditions and specific analysis of the equipment is solved, and the accuracy of equipment maintenance and maintenance is improved.
Description
Technical Field
The invention relates to the technical field of industrial internet intelligent manufacturing, in particular to a device predictive maintenance system and method based on a digital twin technology.
Background
The operation and maintenance mode of regular maintenance is generally adopted by a factory for processing and manufacturing equipment, but in the face of a complex production and operation environment and instability of the equipment, equipment failure often occurs, so that the limitation of the traditional maintenance mode is increasingly large. How to utilize the operation data of the industrial equipment, collect the state parameters, and realize the predictive maintenance of the equipment is a critical urgency of the operation and maintenance work of the factory at present.
At present, some factories are still in a passive control and management stage, data of equipment is manually input through manual collection or offline, so that the collected data volume is small, the processed data also has certain limitation and hysteresis, the state and the problems in the production process of the equipment cannot be reflected in time, an enterprise faces the problem of poor data timeliness in the management process, the equipment cannot respond in time, great influences such as shutdown can be caused, and the manufacturing cost of the enterprise is increased.
Although some factories adopt a relatively advanced internet of things form to carry out interconnection real-time control management on manufacturing and processing equipment, so that production data can be presented in time, and the problems of loose data information connection and the like in the production process of various equipment are solved, the method still needs to monitor, manage and maintain the data information acquired by the manufacturing and processing equipment by operators, the accuracy of information decision is completely dependent on the experience of the operators, different operators can possibly draw different conclusions, and the problems of unstable production process, incapability of eliminating interference factors and the like still exist.
Disclosure of Invention
The invention aims to provide a device predictive maintenance system and a device predictive maintenance method based on a digital twin technology, which aim to solve the technical problems in the background technology.
In order to achieve the purpose, the invention adopts the following technical scheme:
a system for predictive maintenance of equipment based on digital twinning techniques, comprising:
the communication module is used for completing data real-time connection of all equipment;
the protocol conversion module is used for unifying industrial equipment with different protocol standards;
the three-dimensional scene module is used for carrying out high-precision three-dimensional modeling reduction on the industrial equipment;
the engine rendering module is used for loading static three-dimensional model data and endowing specific physical, geometric, behavioral and regular attributes;
the data fusion module is used for importing a predictive maintenance algorithm into the digital twin platform to provide calculation and driving the update of the digital twin;
and the visualization module is used for calling the simulation result to perform visualization display and experience.
In some embodiments, the communication module and the protocol conversion module together form a physical information layer of the digital twin system; the physical information layer acts on the physical industrial equipment for fault diagnosis and prediction and the sensors arranged on various parts of the physical industrial equipment, and is the basis of the system; the physical information layer is used for collecting various states and signals of industrial equipment in the production process, and completing collection, transmission and calculation of data, and the generated data is the driving force of the digital twin.
In some embodiments, the three-dimensional scene restoration module, the engine rendering module and the data fusion module together form a digital simulation layer of the system, and the digital simulation layer is configured to complete mapping of the physical industrial equipment and the virtual digital twin and generate a corresponding three-dimensional digital model.
The invention also provides a device predictive maintenance method based on the digital twin technology, which comprises the following steps:
establishing a physical information layer for an industrial equipment object needing predictive maintenance;
constructing a static digital twin body;
constructing a dynamic intelligent digital twin body;
performing data fusion operation;
constructing a digital twin platform;
and constructing an application function layer.
In some embodiments, the building a physical information layer for an industrial equipment object requiring predictive maintenance includes: the method comprises the following steps of carrying out data acquisition on sensors arranged on entity industrial equipment and all parts of the entity industrial equipment, wherein the data types comprise vibration, temperature, sound, current, voltage, displacement and other multi-dimensional information, and the data range comprises equipment state historical data, equipment fault data and equipment maintenance record data; the protocol conversion module is responsible for unifying the mainstream protocol to complete structured data transmission, realizing the access capability of multi-source heterogeneous data, and finally completing the communication of unstructured data in an SDK calling mode.
In some embodiments, the constructing the static digital twin body comprises the following steps in sequence from small to large: the method comprises the steps of establishing a key part model of the industrial equipment, an equipment production line model and a digital scene model of the whole factory environment, establishing a three-dimensional digital model which comprises the geometric overall dimension and the assembling position relation of each industrial equipment, and requiring that the appearance shape of the model is similar to that of the entity equipment, and dynamic operation parameters and state data are kept synchronous with the entity industrial equipment in real time.
In some embodiments, the dynamic intelligent digital twin building method relies on digital twin technology, and the dynamic intelligent digital twin building method can dynamically reflect various operation states of the entity industrial equipment by importing a built static digital model through Unreal engine software and endowing the dynamic attributes with specific physical, geometric, behavioral and regular aspects.
In some embodiments, the data fusion operation comprises: before the multi-dimensional heterogeneous data is applied to the digital twin, the data needs to be cleaned and converted; the method comprises the steps of unifying the formats of heterogeneous data in a data source, removing meaningless data, inputting the data into a digital twin body at the same time, ensuring the unification of entity industrial equipment and virtual digital equipment, acquiring a characteristic value causing a fault according to the extraction of a data characteristic point, and storing the characteristic value into a created dynamic intelligent digital twin body to form a self-owned fault knowledge base of the digital twin body.
In some embodiments, the constructing a digital twin platform comprises: after data fusion operation, the acquired physical information data are transmitted to a dynamic digital twin body for algorithm calculation, so that the dynamic digital twin body can predict and analyze the current state; the digital twin can receive the production data transmitted by the industrial entity equipment in real time, and the predictive maintenance calculation is continuously carried out according to the self-fault knowledge base.
In some embodiments, the building an application function layer comprises: the current operation condition of the virtual equipment and the result of predictive maintenance are observed through a visual interface, link intercommunication of the entity industrial equipment and the digital twin platform is realized, different parameters can be input by a user, different equipment operation results are simulated through calculation of the intelligent digital twin platform in an analog mode, the industrial entity equipment can receive control signals processed by the digital twin platform in real time in turn, and the operation parameters of the industrial equipment are adjusted actively.
Advantageous effects
1. Through the established digital scene models of key components of industrial equipment, equipment production lines and factory environments, the full-process digital display of the industrial Internet of things equipment can be intuitively and accurately carried out;
2. through the construction of the digital twin body, the intercommunication and sharing of data among all sensors are realized, the digital twin body can be ensured to receive various key production data transmitted by industrial entity equipment in real time, and the industrial entity equipment can receive control signals processed by the digital twin body in real time and complete corresponding execution actions;
3. the functions of fault diagnosis, optimal control, performance prediction maintenance and the like of the industrial Internet of things equipment are realized through full-process simulation prediction of the digital twin body.
Drawings
FIG. 1 is a relationship of a physical industrial apparatus according to the present invention and a digital twin platform.
FIG. 2 is a flow chart of the predictive maintenance method of the equipment based on the digital twinning technique.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
On the contrary, this application is intended to cover any alternatives, modifications, equivalents, and alternatives that may be included within the spirit and scope of the application as defined by the appended claims. Furthermore, in the following detailed description of the present application, certain specific details are set forth in order to provide a better understanding of the present application. It will be apparent to one skilled in the art that the present application may be practiced without these specific details. The embodiments of the present application will be described in detail below with reference to fig. 1-2. It is to be noted that the following examples are only for explaining the present application and do not constitute a limitation to the present application.
In the embodiment of the present application, as shown in fig. 1-2, a device predictive maintenance system based on a digital twin technology is provided, which aims to establish a digital twin platform, complete data intercommunication of an entity industrial device, and implement virtual-real mapping between the entity industrial device and a digital twin thereof. The whole framework comprises a communication module, a protocol conversion module, a three-dimensional scene restoration module, an engine rendering module, a data fusion module and a visualization module.
The communication module completes the data real-time connection of all the devices; the protocol conversion module is used for unifying industrial equipment with different protocol standards; the three-dimensional scene module is used for carrying out high-precision three-dimensional modeling reduction on the industrial equipment; the engine rendering module is used for loading static three-dimensional model data and endowing the static three-dimensional model data with various attributes such as specific physics, geometry, behavior, rule and the like, so that the static three-dimensional model data can truly reflect entity industrial equipment; the data fusion module adopts technologies such as cloud computing, edge computing and big data to introduce a predictive maintenance algorithm into a digital twin platform to provide computing and drive the update of a digital twin body; the visualization module is used for calling the simulation result to perform visualization display and experience.
The communication module and the protocol conversion module jointly form a physical information layer of the digital twin system; the three-dimensional scene restoration module, the engine rendering module and the data fusion module form a digital simulation layer of the system together; the visualization module serves as a system service calling and application function layer.
The physical information layer mainly acts on the physical industrial equipment for fault diagnosis and prediction and the sensors installed at various parts of the physical industrial equipment, and is the basis of the system. The physical information layer is used for collecting various states and signals of the industrial equipment in the production process, and completing the collection, transmission and calculation of data, and the generated data is the driving force of the digital twin and is also the basis for managing and controlling the industrial equipment; the digital simulation layer is used for completing the mapping of the entity industrial equipment and the virtual digital twin body and generating a corresponding three-dimensional digital model. Carrying out information fusion on the multidimensional parameter data and the three-dimensional digital model, using the fused multidimensional modeling data to drive the operation of the digital twin body, and finally forming the intelligent digital twin body of the industrial equipment, wherein the digital twin body operates in the whole production period of the industrial equipment, can dynamically, truly and real-timely reflect the real state of the entity industrial equipment, and generates a reasonable prediction maintenance suggestion by carrying out real-time simulation, emulation and verification on the digital twin body; and the application function layer is used for completing the monitoring of multidimensional digital information data, the visual display of equipment fault diagnosis service and the man-machine interaction operation.
The embodiment also discloses a device predictive maintenance method based on the digital twin technology, which comprises the following steps:
s101: a physical information layer is established for industrial equipment objects that require predictive maintenance. The construction process mainly comprises the steps of carrying out data acquisition on sensors arranged on the entity industrial equipment and all parts of the entity industrial equipment, wherein the data types comprise vibration, temperature, sound, current, voltage, displacement and other multi-dimensional information, and the data range comprises equipment state historical data, equipment fault data, equipment maintenance record data and the like. The protocol unification module is responsible for unifying a mainstream protocol to finish structured data transmission, realizing the access capability of multi-source heterogeneous data, and finally finishing the communication of unstructured data in an SDK calling mode.
S102: constructing a static digital twin body. The construction process comprises the following steps from small to large: the three-dimensional digital model established by software such as 3dmax, blender and the like comprises the geometric overall dimension, the assembly position relation and the like of each industrial device, and the requirements are that not only the appearance shape of the model is similar to that of the entity device, but also the dynamic operation parameters, state data and the like of the model can be kept synchronous with the entity industrial device in real time;
s103: and constructing dynamic intelligent digital twins. The dynamic intelligent digital twin body is constructed mainly by means of a digital twin technology, a constructed static digital model is introduced through engine software such as Unreal and the like, and dynamic attributes of various aspects such as specific physics, geometry, behavior, rule and the like are given to the dynamic intelligent digital twin body, so that the dynamic intelligent digital twin body can dynamically reflect various running states of entity industrial equipment;
s104: and (5) performing data fusion operation. Since the multi-dimensional heterogeneous data is constructed in S101, the data cannot be directly used, and the data needs to be cleaned, converted, and the like before being applied to the digital twin. Unifying the format of heterogeneous data in a data source, removing meaningless data, inputting the data into a digital twin body at the same time, ensuring the unification of entity industrial equipment and virtual digital equipment, acquiring a characteristic value causing a fault according to the extraction of a data characteristic point, and storing the characteristic value into the created dynamic intelligent digital twin body to form a self-owned fault knowledge base of the digital twin body;
s105: and constructing a digital twin platform. And after data fusion operation, the acquired physical information data are transmitted to the dynamic digital twin body for algorithm calculation, so that the dynamic digital twin body can predict and analyze the current state. The digital twin can receive the production data transmitted by the industrial entity equipment in real time and continuously carry out predictive maintenance calculation according to the self-fault knowledge base;
s106: and constructing an application function layer. The method aims to observe the current operation condition of the virtual equipment and the result of predictive maintenance through a visual interface, simultaneously realize the link intercommunication of the entity industrial equipment and the digital twin platform, input different parameters by a user, simulate different equipment operation results through the calculation of the intelligent digital twin platform, enable the industry entity equipment to reversely receive control signals processed by the digital twin platform in real time, and actively adjust the operation parameters of the industrial equipment, thereby achieving the effect of optimal control.
In summary, predictive maintenance helps businesses determine when operating industrial equipment requires maintenance. It will schedule maintenance on a real basis rather than on a fixed period. And forming a digital twin platform by constructing a predictive maintenance algorithm on the digital twin body. The digital twin can receive the production data transmitted by the industrial entity equipment in real time as input, and the functions of performance prediction and maintenance of the industrial equipment and the like are intuitively and accurately realized.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A system for predictive maintenance of equipment based on digital twinning techniques, comprising:
the communication module is used for completing data real-time connection of all equipment;
the protocol conversion module is used for unifying industrial equipment with different protocol standards;
the three-dimensional scene module is used for carrying out high-precision three-dimensional modeling reduction on the industrial equipment;
the engine rendering module is used for loading static three-dimensional model data and endowing specific physical, geometric, behavioral and regular attributes;
the data fusion module is used for importing a predictive maintenance algorithm into the digital twin platform to provide calculation and driving the update of the digital twin;
and the visualization module is used for calling the simulation result to perform visualization display and experience.
2. The system for predictive maintenance of equipment based on digital twinning technology as claimed in claim 1, wherein said communication module and protocol conversion module together form a physical information layer of the digital twinning system; the physical information layer acts on the physical industrial equipment for fault diagnosis and prediction and the sensors arranged on various parts of the physical industrial equipment, and is the basis of the system; the physical information layer is used for collecting various states and signals of industrial equipment in the production process, and completing collection, transmission and calculation of data, and the generated data is the driving force of the digital twin.
3. The system of claim 1, wherein the three-dimensional scene restoration module, the engine rendering module and the data fusion module together form a digital simulation layer of the system, and the digital simulation layer is configured to perform mapping between the physical industrial devices and the virtual digital twins to generate corresponding three-dimensional digital models.
4. A device predictive maintenance method based on a digital twinning technique is characterized by comprising the following steps:
establishing a physical information layer for an industrial equipment object needing predictive maintenance;
constructing a static digital twin body;
constructing a dynamic intelligent digital twin body;
performing data fusion operation;
constructing a digital twin platform;
and constructing an application function layer.
5. The method for predictive maintenance of equipment based on the digital twin technology as claimed in claim 4, wherein the physical information layer is established for the industrial equipment object needing predictive maintenance, and the construction process comprises: the method comprises the following steps of carrying out data acquisition on sensors arranged on entity industrial equipment and all parts of the entity industrial equipment, wherein the data types comprise vibration, temperature, sound, current, voltage, displacement and other multi-dimensional information, and the data range comprises equipment state historical data, equipment fault data and equipment maintenance record data; the protocol conversion module is responsible for unifying the mainstream protocol to complete structured data transmission, realizing the access capability of multi-source heterogeneous data, and finally completing the communication of unstructured data in an SDK calling mode.
6. The method for predictive maintenance of equipment based on the digital twin technology as claimed in claim 4, wherein the static digital twin is constructed through the following steps from small to large: the method comprises the steps of establishing a key part model of the industrial equipment, an equipment production line model and a digital scene model of the whole factory environment, establishing a three-dimensional digital model which comprises the geometric overall dimension and the assembling position relation of each industrial equipment, and requiring that the appearance shape of the model is similar to that of the entity equipment, and dynamic operation parameters and state data are kept synchronous with the entity industrial equipment in real time.
7. The method as claimed in claim 4, wherein the dynamic intelligent digital twin is constructed by means of digital twin technology, and the un real engine software imports the constructed static digital model and gives it specific physical, geometric, behavioral and regular dynamic properties to dynamically reflect various operation states of the physical industrial equipment.
8. The method for predictive maintenance of equipment based on digital twinning technology as claimed in claim 4, wherein said data fusion operation comprises: before the multi-dimensional heterogeneous data is applied to the digital twin, the data needs to be cleaned and converted; the method comprises the steps of unifying the formats of heterogeneous data in a data source, removing meaningless data, inputting the data into a digital twin body at the same time, ensuring the unification of entity industrial equipment and virtual digital equipment, acquiring a characteristic value causing a fault according to the extraction of a data characteristic point, and storing the characteristic value into a created dynamic intelligent digital twin body to form a self-owned fault knowledge base of the digital twin body.
9. The method for predictive maintenance of equipment based on digital twinning technology as claimed in claim 4, wherein said constructing a digital twinning platform comprises: after data fusion operation, the acquired physical information data are transmitted to a dynamic digital twin body for algorithm calculation, so that the dynamic digital twin body can predict and analyze the current state; the digital twin can receive the production data transmitted by the industrial entity equipment in real time, and the predictive maintenance calculation is continuously carried out according to the self-fault knowledge base.
10. The method for predictive maintenance of devices based on digital twinning technology as claimed in claim 4, wherein said constructing an application function layer comprises: the current operation condition of the virtual equipment and the result of predictive maintenance are observed through a visual interface, link intercommunication of the entity industrial equipment and the digital twin platform is realized, different parameters can be input by a user, different equipment operation results are simulated through calculation of the intelligent digital twin platform in an analog mode, the industrial entity equipment can receive control signals processed by the digital twin platform in real time in turn, and the operation parameters of the industrial equipment are adjusted actively.
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