CN112162519A - Compound machine tool digital twin monitoring system - Google Patents
Compound machine tool digital twin monitoring system Download PDFInfo
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Abstract
The invention discloses a composite type machine tool digital twin monitoring system, which relates to the technical field of digital twin, and is characterized in that the system establishes a digital twin monitoring system through a machine tool information module, realizes multi-platform, multi-data and multi-interface communication based on an OPC-UA transmission interface and establishes a human-computer interaction module; the multi-field data acquisition module acquires multi-source heterogeneous data in real time by adopting different types of sensors, processes the acquired data based on an information fusion technology to form twin data, and forwards the twin data to the modeling calculation module; the modeling calculation module is used for forming a digital twin body of the composite machine tool by driving twin data and combining rules such as constraint, prediction and decision; and the personalized decision module is used for monitoring and managing the entity machine tool equipment in real time through reconstructing and optimizing the machine tool monitoring twin model in real time. The invention simplifies the monitoring process of the operation of the composite machine tool, improves the monitoring precision of the machine tool system and realizes the active predictive maintenance of the operation of the composite machine tool.
Description
Technical Field
The invention belongs to the technical field of digital twinning, and particularly relates to a composite type machine tool digital twinning monitoring system.
Background
The digital twin nature concept is a quasi-real time digitized mirror of a physical entity or process. Compared with the traditional life cycle management and simulation technology, the digital twin has the characteristics of two-way, continuous and open, and new capacity is added to the physical entity through means of virtual-real interaction feedback, data fusion analysis, decision iteration optimization and the like. For the manufacturing field, the digital twin may be defined as "a dynamic model that digitally describes manufacturing elements such as people, products, equipment, and processes, and synchronously updates optimization as the development state of objects, working conditions, product geometry, etc. change", reflecting the full life cycle process of the corresponding entity equipment.
The method for analyzing and mining the data is a big data technology, but the big data technology is mainly focused on the data analysis and mining of a machine tool entity system, and operating system data in a virtual world is ignored. With the system of the machine tool being more intelligent, the problems of real-time monitoring of data and running states and the like are more prominent; the data-driven method collects data by installing a large number of sensors, thereby obtaining the operating state and information of the machine tool. However, some key components cannot be equipped with sensors, resulting in great difficulty in data acquisition and further mining. Therefore, the development of the intelligent monitoring technology of the compound machine tool has limited the integration, intelligentization and flexibility process of the machine tool to a certain extent, and becomes one of the bottleneck technologies affecting the reliability and precision development of the compound machine tool system, and a breakthrough of the theoretical method and technology for monitoring and maintaining the machine tool operation system is urgently needed.
Disclosure of Invention
The invention aims to provide a digital twin monitoring system of a composite machine tool, thereby overcoming the defects of the traditional composite machine tool monitoring system.
In order to achieve the above object, the present invention provides a digital twin monitoring system for a composite machine tool, comprising:
the machine tool information module is used for constructing, storing and managing information models of components related in an actual composite machine tool system, and the information models comprise a geometric model, a functional information model, a rule model, a behavior model, a performance prediction model and a control logic model;
the multi-field data acquisition module receives data types such as position, speed, current, rotating speed, load, motor load and the like in real time through multiple channels, respectively stores relevant data information of a main shaft, a feed shaft, a cutter and a machining program, and transmits the data information to the data processing module and the modeling calculation module;
and the data processing module is used for receiving the running state data information transmitted by the real-time multi-field data acquisition module, and deeply processing the acquired numerical information by using a big data technology so as to eliminate noise components and redundant information in the signal and reconstruct a new data set. Then, when time and frequency domain statistical characteristics are extracted from the reconstructed data set, and the component signals decomposed by the characteristic extraction technology are quantized by using indexes such as energy values, entropy values, spectral kurtosis and the like; finally, dimensionality reduction is carried out on the high-dimensional features, and preparation is made for accurate modeling and predictive maintenance of an operating system;
and the modeling calculation module is used for receiving the running state data information transmitted by the real-time multi-field data acquisition module, fusing the data as driving data, and associating constraint rules, prediction rules, decision rules and the like together to jointly form a digital twin body of the manufacturing and processing equipment, wherein the digital twin body exists in the whole life product cycle of the manufacturing and processing equipment and can dynamically, truly and real-timely reflect the real state of the manufacturing and processing equipment in a physical layer.
The human-computer interaction module is used for receiving the machine tool running state data sent by the real-time data acquisition module, processing and analyzing the running state data of the actual production line and obtaining the actual running state data of each component related to the actual running state; establishing a virtual monitoring system corresponding to an actual machine tool system, namely: the digital twin monitoring system is realized by mapping a geometric model corresponding to a part involved in actual operation in a machine tool information model module, the geometric model binds a part function information model, a rule model, a behavior model, a control logic model and a motion control display program in an attribute form, the motion control display program of each part extracts actual operation state data corresponding to the part, then the actual operation state data corresponding to the part is subjected to deep processing to obtain virtual operation state monitoring data, the geometric model is controlled according to the virtual row state monitoring data to complete simulation action, and dynamic simulation of the composite machine tool operation state monitoring system is realized. Meanwhile, visually expressing the state data corresponding to the part on an interface in a chart form;
and the personalized service decision module is used for dynamically tracking and reflecting the latest state of the equipment entity by the machine tool physical entity through the digital twin body, generating corresponding decision information through simulation, and monitoring and optimizing a machine tool physical system by using the generated decision information so as to finally realize the fusion and intelligent monitoring of the physical information and the virtual information of the manufacturing and processing equipment.
Further, the machine tool information module comprises a geometric model, a functional information model, a rule model, a behavior model and a control logic model.
Further, the multi-domain data acquisition module comprises:
data acquisition, including multi-source physical field data, model generation data and virtual-real fusion data, and acquiring numerical information by using different sensors according to different types of physical fields, for example, a temperature sensor is used in a temperature field, a hall sensor is used in an electromagnetic field, a pressure sensor is used in a structural field, and the like;
data management, namely generating multiplication data information by using directly acquired numerical information, finally enabling the data information to be in a mesh structure, and backing up and storing the data;
and data transmission, namely receiving data types such as position, speed, current, rotating speed, load, motor load and the like in real time by multiple channels, transmitting data information to the data processing module and the modeling calculation module, and realizing communication interaction among the modules.
Furthermore, the data processing module receives the running state data information transmitted by the real-time multi-field data acquisition module, and deeply processes the acquired numerical information by using a big data technology so as to eliminate noise components and redundant information in the signals and reconstruct a new data set. Then, when time and frequency domain statistical characteristics are extracted from the reconstructed data set, and the component signals decomposed by the characteristic extraction technology are quantized by using indexes such as energy values, entropy values, spectral kurtosis and the like; finally, dimensionality reduction is carried out on the high-dimensional features, and preparation is made for accurate modeling and predictive maintenance of an operating system;
furthermore, the modeling calculation module receives the running state data information transmitted by the real-time multi-field data acquisition module, constructs a self-adaptive model changing along with the running environment, and can accurately monitor the performance of parts and the whole machine of the machine tool; injecting a fault mode in the historical maintenance data of the machine tool into the three-dimensional physical model and the performance model to construct a fault model which can be used for fault diagnosis and prediction; combining the historical operating data of the machine tool with a performance model and fusing a data driving method to construct a performance prediction model, and predicting the performance and the residual life of the whole machine; the local linearization model and the machine tool running state environment model are fused and a control optimization model is constructed, so that the optimization of the machine tool control performance can be realized, and the machine tool can play better performance in the running process.
Further, the personalized service decision module is debugged and verified by adopting a virtual PLC and a virtual prototype, and is transferred to a real machine tool system for butt joint after being matured. And then, the virtual prototype system and the real physical system are synchronized in real time based on the physical PLC. The machine tool physical entity and the virtual model carry out data information interaction, an information layer is transmitted through the data mapping dictionary, an interface used for transmission is an OPC-UA interface, the interface can unify model data under the condition of ensuring that communication data are not lost, a complex data model is supported, and communication of multiple platforms, multiple data and multiple interfaces can be realized.
Furthermore, the simulation platform adopts an Untiy real-time three-dimensional interactive virtual content construction platform, can perform accurate physical simulation, and can also customize a development editing interface so as to facilitate secondary development.
Further, the fault diagnosis and prediction comprises the following steps:
s1, classifying according to the type of the physical field according to the system modeling information;
s2, normalizing the modeling data of one type of physical field;
s3, setting the output of the neural network as the fault type and fault degree of the physical field, and training the normalized data through the neural network to obtain the trained neural network;
s4, repeating S2-S3 to obtain trained neural networks corresponding to all physical fields;
and S5, classifying the system modeling information acquired in real time through S1, and constructing a fault model to perform fault diagnosis and prediction through the correspondingly trained neural network in the corresponding type S4 by combining fault modes in the historical maintenance data of the machine tool to obtain diagnosis and prediction results.
Further, the physical field adopted for fault diagnosis and prediction is selected and fused according to the operation condition of the machine tool system.
Further, the monitoring process comprises the steps of:
a1, classifying according to the type of the operating physical field, and drawing a data curve when corresponding production equipment operates normally;
a2, classifying historical fault information according to the type of production equipment, and drawing a corresponding historical data curve;
a3, comparing the historical data curve with the corresponding data curve in normal operation, judging the contact ratio, and if the historical data curve is basically normal with the corresponding data curve in normal operation, the production equipment corresponding to the historical data curve works normally; otherwise, the fault occurs;
a4, overhauling the corresponding equipment according to the fault, marking the fault type on the corresponding real-time data curve, and forming a data curve with fault analysis to replace the data curve in A1;
a5, repeating A1-A4 to obtain a data curve with fault analysis;
a6, classifying the real-time working condition information according to the type of production equipment, and drawing a corresponding real-time data curve;
a7, comparing the real-time data curve with the corresponding data curve with fault analysis, judging the contact ratio, and when the real-time data curve and the corresponding data curve with fault analysis are basically normal, the production equipment corresponding to the real-time data curve works normally; otherwise, a real-time diagnosis result is obtained for the occurrence of the fault, and the intelligent monitoring of the operation process of the machine tool is further realized.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a composite machine tool digital twin monitoring system, which is characterized in that a digital twin monitoring system is established through a machine tool information module, multi-platform, multi-data and multi-interface communication is realized based on an OPC-UA transmission interface, and a man-machine interaction module of the composite machine tool twin monitoring system is established; the multi-field data acquisition module adopts different sensors to acquire multi-source heterogeneous data in the operation process in real time according to different physical field types, processes the acquired data based on an information fusion technology to form twin data, and forwards the twin data to the modeling calculation module; the modeling calculation module is driven by twin data of the operation of the composite machine tool and is combined with rules such as constraint rules, prediction rules, decision rules and the like to jointly form a digital twin body of the composite machine tool, and the real state of equipment in a physical layer can be dynamically reflected in real time; and the personalized decision module is used for realizing real-time monitoring and management of the entity machine tool equipment by reconstructing and optimizing the machine tool monitoring twin model in real time. The invention simplifies the monitoring process of the operation process of the composite machine tool, improves the monitoring precision of a machine tool system, improves the accuracy of decision information and realizes the active predictive maintenance of the operation of the composite machine tool.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only one embodiment of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a composite type machine tool digital twin monitoring system provided by the invention.
Fig. 2 is a schematic structural diagram of a machine tool information module of a composite machine tool digital twin monitoring system provided by the invention.
Fig. 3 is a schematic structural diagram of a multi-domain data acquisition module of the composite machine tool digital twin monitoring system provided by the invention.
Fig. 4 is a schematic structural diagram of a data processing module of the composite machine tool digital twin monitoring system provided by the invention.
Fig. 5 is a schematic structural diagram of a modeling calculation module of the composite machine tool digital twin monitoring system provided by the invention.
Fig. 6 is a schematic structural diagram of a human-computer interaction module of the composite machine tool digital twin monitoring system provided by the invention.
Fig. 7 is a schematic structural diagram of a personalized service decision module of the composite machine tool digital twin monitoring system provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the digital twin monitoring system of the compound machine tool provided by the invention comprises: the system comprises a machine tool information module, a multi-field data acquisition module, a data processing module, a modeling calculation module, a human-computer interaction module and a personalized service decision module.
As shown in fig. 2, the machine tool information module is configured to construct, store, and manage information models of components involved in an actual composite machine tool system, where the information models include a geometric model, a functional information model, a rule model, a behavior model, a performance prediction model, and a control logic model.
As shown in fig. 3, the multi-domain data acquisition module receives data types such as position, speed, current, rotation speed, load, motor load and the like in real time through multiple channels, respectively stores data information related to a main shaft, a feed shaft, a tool and a machining program, and transmits the data information to the data processing module and the modeling calculation module.
As shown in fig. 4, the data processing module receives the operation state data information transmitted by the real-time multi-domain data acquisition module, and performs deep processing on the acquired numerical information by using a big data technology to eliminate noise components and redundant information in the signal and reconstruct a new data set. Then, when time and frequency domain statistical characteristics are extracted from the reconstructed data set, and the component signals decomposed by the characteristic extraction technology are quantized by using indexes such as energy values, entropy values, spectral kurtosis and the like; and finally, carrying out dimensionality reduction on the high-dimensional features, and preparing for accurate modeling and predictive maintenance of an operating system.
As shown in fig. 5, the modeling calculation module receives the operation state data information transmitted by the real-time multi-domain data acquisition module, fuses the data as driving data, and associates constraint rules, prediction rules, decision rules and the like together to form a digital twin body of the manufacturing and processing equipment, wherein the digital twin body exists in the whole life product cycle of the manufacturing and processing equipment and can dynamically, truly and real-timely reflect the real state of the manufacturing and processing equipment in the physical layer.
As shown in fig. 6, the human-computer interaction module receives the machine tool running state data sent by the real-time data acquisition module, and processes and analyzes the running state data of the actual production line to obtain the actual running state data of each component involved in the actual running state; establishing a virtual monitoring system corresponding to an actual machine tool system, namely: the digital twin monitoring system is realized by mapping a geometric model corresponding to a part involved in actual operation in a machine tool information model module, the geometric model binds a part function information model, a rule model, a behavior model, a control logic model and a motion control display program in an attribute form, the motion control display program of each part extracts actual operation state data corresponding to the part, then the actual operation state data corresponding to the part is subjected to deep processing to obtain virtual operation state monitoring data, the geometric model is controlled according to the virtual row state monitoring data to complete simulation action, and dynamic simulation of the composite machine tool operation state monitoring system is realized. And simultaneously, visually expressing the state data corresponding to the part on the interface in a chart form.
As shown in fig. 7, in the personalized service decision module, the machine tool physical entity dynamically tracks and reflects the latest state of the equipment entity through the digital twin body, generates corresponding decision information through simulation, and monitors and optimizes the machine tool physical system by using the generated decision information, thereby finally realizing the fusion and intelligent monitoring of the physical information and the virtual information of the manufacturing and processing equipment.
The machine tool information module comprises a geometric model, a functional information model, a rule model, a behavior model, a performance prediction model and a control logic model.
The information of the geometric model comprises the overall dimension, geometric tolerance, assembly position relation, color information, central coordinate point component material information and component matching relation;
the information of the rule model comprises a constraint rule, a prediction rule and a decision rule;
the information of the behavior model comprises dynamics constraint, dynamic scene import and control logic definition;
with continued reference to fig. 1, the multi-domain data acquisition module includes different types of sensors for acquiring data types such as a multi-channel real-time receiving position, speed, current, rotation speed, load, motor load, and the like, the acquisition contents mainly include multi-source physical field data, model generation data, and virtual-real fusion data, and according to the difference of the types of the physical fields, different sensors are used to acquire numerical information, for example, a temperature sensor is used in a temperature field, a hall sensor is used in an electromagnetic field, and a pressure sensor is used in a structural field.
The tasks of the data processing module include:
receiving running state data information transmitted by a real-time multi-field data acquisition module, and carrying out deep processing on the acquired numerical value information by using a big data technology so as to eliminate noise components and redundant information in signals and reconstruct a new data set;
when the statistical characteristics of a frequency domain are extracted from the reconstructed data set, the component signals decomposed by the characteristic extraction technology are quantized by using indexes such as an energy value, an entropy value and a spectral kurtosis;
and finally, carrying out dimensionality reduction on the high-dimensional features, and preparing for accurate modeling and predictive maintenance of an operating system.
The modeling calculation module comprises fusion of multidimensional models of an operating system mechanism model, a data driving model, a fault prediction model and an abnormal event diagnosis model, and is combined with a constraint rule, a prediction rule, a decision rule and the like to form a digital twin body of the manufacturing and processing equipment together.
The human-computer interaction module is used for receiving the machine tool running state data sent by the real-time data acquisition module, processing and analyzing the running state data of the actual production line and obtaining the actual running state data of each component related to the actual running state; establishing a virtual monitoring system corresponding to an actual machine tool system, namely: the digital twin monitoring system is realized by mapping a geometric model corresponding to a part involved in actual operation in a machine tool information model module, the geometric model binds a part function information model, a rule model, a behavior model, a control logic model and a motion control display program in an attribute form, the motion control display program of each part extracts actual operation state data corresponding to the part, then the actual operation state data corresponding to the part is subjected to deep processing to obtain virtual operation state monitoring data, the geometric model is controlled according to the virtual row state monitoring data to complete simulation action, and dynamic simulation of the composite machine tool operation state monitoring system is realized. Meanwhile, visually expressing the state data corresponding to the part on an interface in a chart form;
the machine tool physical entity dynamically tracks and reflects the latest state of the equipment entity through the digital twin body of the machine tool physical entity, generates corresponding decision information through simulation, monitors and optimizes a machine tool physical system by utilizing the generated decision information, and finally realizes the fusion of the physical information and the virtual information of the manufacturing and processing equipment and intelligent monitoring
The monitoring mechanism of the digital twin monitoring system comprises a fault diagnosis prediction part and an intelligent monitoring part, and is based on the driving of massive twin data, and utilizes a big data technology combined with algorithms such as deep learning and neural network to extract characteristic data of interactive data, so that the accurate predictive maintenance of a machine tool system is realized, and an accurate decision can be provided for the system.
The fault diagnosis and prediction comprises the following steps:
s1, classifying according to the type of the physical field according to the system modeling information;
s2, normalizing the modeling data of one type of physical field;
s3, setting the output of the neural network as the fault type and fault degree of the physical field, and training the normalized data through the neural network to obtain the trained neural network;
s4, repeating S2-S3 to obtain trained neural networks corresponding to all physical fields;
and S5, classifying the system modeling information acquired in real time through S1, and constructing a fault model to perform fault diagnosis and prediction through the correspondingly trained neural network in the corresponding type S4 by combining fault modes in the historical maintenance data of the machine tool to obtain diagnosis and prediction results.
Further, the physical field adopted for fault diagnosis and prediction is selected and fused according to the operation condition of the machine tool system.
The intelligent monitoring process comprises the following steps:
a1, classifying according to the type of the operating physical field, and drawing a data curve when corresponding production equipment operates normally;
a2, classifying historical fault information according to the type of production equipment, and drawing a corresponding historical data curve;
a3, comparing the historical data curve with the corresponding data curve in normal operation, judging the contact ratio, and if the historical data curve is basically normal with the corresponding data curve in normal operation, the production equipment corresponding to the historical data curve works normally; otherwise, the fault occurs;
a4, overhauling the corresponding equipment according to the fault, marking the fault type on the corresponding real-time data curve, and forming a data curve with fault analysis to replace the data curve in A1;
a5, repeating A1-A4 to obtain a data curve with fault analysis;
a6, classifying the real-time working condition information according to the type of production equipment, and drawing a corresponding real-time data curve;
a7, comparing the real-time data curve with the corresponding data curve with fault analysis, judging the contact ratio, and when the real-time data curve and the corresponding data curve with fault analysis are basically normal, the production equipment corresponding to the real-time data curve works normally; otherwise, a real-time diagnosis result is obtained for the occurrence of the fault, and the intelligent monitoring of the operation process of the machine tool is further realized.
The working principle of the digital twin monitoring system of the compound machine tool is explained in detail so that the technical personnel in the field can understand the invention more:
the multisource data acquisition module adopts different sensors to acquire numerical information according to different types of physical fields, for example, a temperature sensor is adopted in a temperature field, a Hall sensor is adopted in an electromagnetic field, a pressure sensor is adopted in a structural field, and the like, and data information is sent to the data processing module through a transmission interface OPC-UA; the data processing module processes the received real-time data, such as cleaning, data mining, data association, data noise reduction, feature extraction and the like, so as to eliminate noise components and redundant information in the signals, reconstruct a new data set and transmit the new data set to the modeling calculation module; the modeling calculation module carries out secondary mining on the reconstructed data set to form fusion of multidimensional models of a system mechanism model, a data driving model, a fault prediction model and an abnormal event diagnosis model, and digital twins of manufacturing and processing equipment are formed together by utilizing a constraint rule, a prediction rule, a decision rule and the like in a correlation mode; the digital twin body is corrected and optimized according to real-time data; the digital twin monitoring system combines the digital twin correction optimization model with a historical fault mode and an operating environment condition, forms an early warning signal and decision information according to a monitoring result, and simultaneously stores the early warning signal and the decision information to the server; the method for fault diagnosis and prediction can be selected according to the requirement.
The above disclosure is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or modifications within the technical scope of the present invention, and shall be covered by the scope of the present invention.
Claims (10)
1. A kind of compound lathe digital twinning monitoring system, characterized by including: the system comprises a machine tool information module, a multi-field data acquisition module, a data processing module, a modeling calculation module, a human-computer interaction module and a personalized service decision module; wherein:
the machine tool information module is used for constructing, storing and managing information models of components related in an actual composite machine tool system, and the information models comprise a geometric model, a functional information model, a rule model, a behavior model, a performance prediction model and a control logic model;
the multi-field data acquisition module receives data types such as position, speed, current, rotating speed, load, motor load and the like in real time through multiple channels, respectively stores relevant data information of a main shaft, a feed shaft, a cutter and a machining program, and transmits the data information to the data processing module and the modeling calculation module;
the data processing module is used for receiving the running state data information transmitted by the real-time multi-field data acquisition module, deeply processing the acquired numerical information by utilizing a big data technology so as to eliminate noise components and redundant information in the signal and reconstruct a new data set;
then, when time and frequency domain statistical characteristics are extracted from the reconstructed data set, and the component signals decomposed by the characteristic extraction technology are quantized by using indexes such as energy values, entropy values, spectral kurtosis and the like; finally, dimensionality reduction is carried out on the high-dimensional features, and preparation is made for accurate modeling and predictive maintenance of an operating system;
the modeling calculation module is used for receiving the running state data information transmitted by the real-time multi-field data acquisition module, fusing the data as driving data, and associating constraint rules, prediction rules, decision rules and the like together to jointly form a digital twin body of the manufacturing and processing equipment, wherein the digital twin body exists in the whole life product cycle of the manufacturing and processing equipment and can dynamically, truly and real-timely reflect the real state of the manufacturing and processing equipment in a physical layer;
the human-computer interaction module is used for receiving the machine tool running state data sent by the real-time data acquisition module, processing and analyzing the running state data of the actual production line and obtaining the actual running state data of each component related to the actual running state; establishing a virtual monitoring system corresponding to an actual machine tool system, namely: the system comprises a digital twin monitoring system, a machine tool information model module and a motion control display program, wherein the digital twin monitoring system is realized by mapping a geometric model corresponding to a component involved in actual operation in the machine tool information model module, the geometric model binds a component function information model, a rule model, a behavior model, a control logic model and the motion control display program in an attribute form, the motion control display program of each component extracts actual operation state data corresponding to the component, then carries out deep processing on the actual operation state data corresponding to the component to obtain virtual operation state monitoring data, controls the geometric model to complete simulation action according to the virtual operation state monitoring data, and realizes dynamic simulation on the composite machine tool operation state monitoring system;
meanwhile, visually expressing the state data corresponding to the part on an interface in a chart form;
and the personalized service decision module is used for dynamically tracking and reflecting the latest state of the equipment entity by the machine tool physical entity through the digital twin body, generating corresponding decision information through simulation, and monitoring and optimizing a machine tool physical system by using the generated decision information so as to finally realize the fusion and intelligent monitoring of the physical information and the virtual information of the manufacturing and processing equipment.
2. A composite machine tool digital twin monitoring system according to claim 1, characterised in that: the machine tool information module comprises a geometric model, a functional information model, a rule model, a behavior model and a control logic model.
3. A composite machine tool digital twin monitoring system according to claim 1, characterised in that: the multi-domain data acquisition module comprises:
data acquisition, including multi-source physical field data, model generation data and virtual-real fusion data, and acquiring numerical information by using different sensors according to different types of physical fields, for example, a temperature sensor is used in a temperature field, a hall sensor is used in an electromagnetic field, a pressure sensor is used in a structural field, and the like;
data management, namely generating multiplication data information by using directly acquired numerical information, finally enabling the data information to be in a mesh structure, and backing up and storing the data;
and data transmission, namely receiving data types such as position, speed, current, rotating speed, load, motor load and the like in real time by multiple channels, transmitting data information to the data processing module and the modeling calculation module, and realizing communication interaction among the modules.
4. A composite machine tool digital twin monitoring system according to claim 1, characterised in that: the data processing module receives the running state data information transmitted by the real-time multi-field data acquisition module, and deeply processes the acquired numerical information by using a big data technology so as to eliminate noise components and redundant information in the signal and reconstruct a new data set; then, when time and frequency domain statistical characteristics are extracted from the reconstructed data set, and the component signals decomposed by the characteristic extraction technology are quantized by using indexes such as energy values, entropy values, spectral kurtosis and the like; and finally, carrying out dimensionality reduction on the high-dimensional features, and preparing for accurate modeling and predictive maintenance of an operating system.
5. A composite machine tool digital twin monitoring system according to claim 1, characterised in that: the modeling calculation module receives the running state data information transmitted by the real-time multi-field data acquisition module, constructs a self-adaptive model changing along with the running environment, and can accurately monitor the performance of parts and the whole machine of the machine tool; injecting a fault mode in the historical maintenance data of the machine tool into the three-dimensional physical model and the performance model to construct a fault model which can be used for fault diagnosis and prediction; combining the historical operating data of the machine tool with a performance model and fusing a data driving method to construct a performance prediction model, and predicting the performance and the residual life of the whole machine; the local linearization model and the machine tool running state environment model are fused and a control optimization model is constructed, so that the optimization of the machine tool control performance can be realized, and the machine tool can play better performance in the running process.
6. A composite machine tool digital twin monitoring system according to claim 1, characterised in that: the personalized service decision module is debugged and verified by adopting a virtual PLC and a virtual prototype, the personalized service decision module is transferred to a real machine tool system for butt joint after being matured, and then the virtual prototype system and the real physical system are synchronized in real time based on a physical PLC;
the machine tool physical entity and the virtual model carry out data information interaction, an information layer is transmitted through the data mapping dictionary, an interface used for transmission is an OPC-UA interface, the interface can unify model data under the condition of ensuring that communication data are not lost, a complex data model is supported, and communication of multiple platforms, multiple data and multiple interfaces can be realized.
7. A composite machine tool digital twin monitoring system according to claim 1, characterised in that: the simulation platform adopts an Untiy real-time three-dimensional interactive virtual content construction platform, can perform accurate physical simulation, and can also customize a development editing interface so as to facilitate secondary development.
8. A composite machine tool digital twin monitoring system according to claim 5, characterised in that: the fault diagnosis and prediction comprises the following steps:
s1, classifying according to the type of the physical field according to the system modeling information;
s2, normalizing the modeling data of one type of physical field;
s3, setting the output of the neural network as the fault type and fault degree of the physical field, and training the normalized data through the neural network to obtain the trained neural network;
s4, repeating S2-S3 to obtain trained neural networks corresponding to all physical fields;
and S5, classifying the system modeling information acquired in real time through S1, and constructing a fault model to perform fault diagnosis and prediction through the correspondingly trained neural network in the corresponding type S4 by combining fault modes in the historical maintenance data of the machine tool to obtain diagnosis and prediction results.
9. A composite machine tool digital twin monitoring system according to claim 5, characterised in that: and selecting and fusing the physical fields adopted by the fault diagnosis and prediction according to the running condition of the machine tool system.
10. A composite machine tool digital twin monitoring system according to claim 1, characterised in that: the monitoring process comprises the following steps:
a1, classifying according to the type of the operating physical field, and drawing a data curve when corresponding production equipment operates normally;
a2, classifying historical fault information according to the type of production equipment, and drawing a corresponding historical data curve;
a3, comparing the historical data curve with the corresponding data curve in normal operation, judging the contact ratio, and if the historical data curve is basically normal with the corresponding data curve in normal operation, the production equipment corresponding to the historical data curve works normally; otherwise, the fault occurs;
a4, overhauling the corresponding equipment according to the fault, marking the fault type on the corresponding real-time data curve, and forming a data curve with fault analysis to replace the data curve in A1;
a5, repeating A1-A4 to obtain a data curve with fault analysis;
a6, classifying the real-time working condition information according to the type of production equipment, and drawing a corresponding real-time data curve;
a7, comparing the real-time data curve with the corresponding data curve with fault analysis, judging the contact ratio, and when the real-time data curve and the corresponding data curve with fault analysis are basically normal, the production equipment corresponding to the real-time data curve works normally; otherwise, a real-time diagnosis result is obtained for the occurrence of the fault, and the intelligent monitoring of the operation process of the machine tool is further realized.
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