CN117170310A - Digital twin-based remote fault diagnosis method and system for numerical control machine tool - Google Patents
Digital twin-based remote fault diagnosis method and system for numerical control machine tool Download PDFInfo
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Abstract
The embodiment of the invention discloses a digital twin-based remote fault diagnosis method and system for a numerical control machine tool, which relate to a digital twin-based remote fault diagnosis system for the numerical control machine tool, and can be used for carrying out real-time remote diagnosis on faults of the numerical control machine tool, so that the intelligent degree and detection efficiency of the remote fault diagnosis of the machine tool are improved. Collecting data information of a numerical control machine tool; information in the machine tool operation information database is input into an NB-IoT module of a transmission layer through a serial port, and the NB-IoT wireless communication network is transmitted to an NB-IoT cloud platform; constructing a digital twin model of the numerical control machine according to real-time operation data and historical data acquired by the cloud platform; performing fault diagnosis on the numerical control machine tool by adopting an event detection mode, analyzing the running state of the numerical control machine tool, and performing comparative analysis according to attribute data in a state database to generate a fault diagnosis report; the remote man-machine interaction interface can receive the alarm and acquire the diagnosis result.
Description
Technical Field
The invention relates to a digital twin-based remote fault diagnosis system of a numerical control machine tool, in particular to a digital twin-based remote fault diagnosis method and system of the numerical control machine tool.
Background
Numerical control machine tools are taken as important bases in manufacturing industry, once the machine tools are out of order, the machine tools are light, the parts are scrapped, and the machine tools are heavy, the production of enterprises is stopped, and therefore irrecoverable economic losses are caused. The real-time monitoring of the running state of the numerical control machine tool is an important way for realizing predictive maintenance, reducing shutdown loss and improving production efficiency.
Diagnosing the numerical control machine tool by adopting a traditional fault diagnosis mode, and providing diagnosis assistance and guidance for a user through oral or written description for simple faults; for complex faults, experienced engineers must attend to the field to provide service. On one hand, the mode causes reduced fault diagnosis agility, reduced service efficiency and increased service cost, and on the other hand, the mode affects the production efficiency and the product quality of enterprises and causes economic loss.
Therefore, how to realize the real-time remote diagnosis of the faults of the numerical control machine tool, so as to improve the intelligent degree and the detection efficiency of the remote fault diagnosis of the machine tool, and become a new research direction.
Disclosure of Invention
The embodiment of the invention provides a digital twin-based remote fault diagnosis method and system for a numerical control machine tool, which can carry out real-time remote diagnosis on faults of the numerical control machine tool and improve the intelligent degree and detection efficiency of remote fault diagnosis of the machine tool.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method, including:
s101, receiving working data of a numerical control machine tool from a sensor data acquisition module, wherein the working data comprise: static data and dynamic data of the numerical control machine during working;
s102, identifying the numerical control machine tool, and inquiring a digital twin model corresponding to the numerical control machine tool from a model library;
s103, after the working data are imported into a digital twin model corresponding to the numerical control machine tool, determining the movement behavior of the numerical control machine tool, wherein the movement behavior comprises scaling, translation and rotation actions of the numerical control machine tool in the working process;
s104, analyzing the abnormality of the working state of the numerical control machine by utilizing the movement behaviors determined in the step 103, and generating a fault analysis report.
In a second aspect, embodiments of the present invention provide a system comprising: the system comprises a sensor data acquisition module, a digital twin model construction module, a data processing and fault diagnosis module, a data transmission module and a remote human-computer interaction module.
The sensor data acquisition module is used for acquiring working data of the numerical control machine tool, and the working data comprise: static data and dynamic data of the numerical control machine during working;
the data transmission module is used for uploading the collected working data of the numerical control machine tool;
the digital twin construction module is used for identifying the numerical control machine tool and inquiring a digital twin model corresponding to the numerical control machine tool from a model library; or constructing a digital twin model of the numerical control machine according to the collected real-time operation data and the prestored historical data;
the data processing and fault diagnosis module is used for determining the movement behavior of the numerical control machine after the working data are imported into a digital twin model corresponding to the numerical control machine, wherein the movement behavior comprises the zooming, translation and rotation actions of the numerical control machine in the working process;
the remote man-machine interaction module is used for remotely providing a man-machine interaction interface for personal terminals of personnel, and fault alarm information and diagnosis results are displayed in the interaction interface.
According to the digital twin-based remote fault diagnosis method and system for the numerical control machine tool, provided by the embodiment of the invention, the data transmission module is used for inputting information in the machine tool operation information database into the NB-IoT module of the transmission layer through the serial port, and the NB-IoT wireless communication network is used for transmitting the information to the NB-IoT cloud platform; the digital twin model construction module is used for constructing a digital twin model of the numerical control machine tool according to the real-time operation data and the historical data acquired by the cloud platform; the data processing and fault diagnosis module is used for carrying out fault diagnosis on the numerical control machine tool in an event detection mode, analyzing the running state of the numerical control machine tool, carrying out comparison analysis according to attribute data in a state database and generating a fault diagnosis report; the remote man-machine interaction interface can receive the alarm and acquire the diagnosis result. The digital twin technology is adopted to monitor the behavior of the numerical control machine tool in real time, accurately predict the performance of the numerical control machine tool, achieve the mapping effect of the full life cycle, and simultaneously combine the fault diagnosis technology of the numerical control machine tool with the wireless communication technology on the basis, so that the real-time remote diagnosis of the fault of the numerical control machine tool is realized, and the intelligent degree and the detection efficiency of the remote fault diagnosis of the machine tool are improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an overall architecture of a digital twin-based remote fault diagnosis system for a numerically-controlled machine tool according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a model transformation principle according to an embodiment of the present invention;
fig. 3 is a state detection flow diagram of event detection.
Fig. 4 is a schematic flow chart of a method according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail below with reference to the drawings and detailed description for the purpose of better understanding of the technical solution of the present invention to those skilled in the art. Embodiments of the present invention will hereinafter be described in detail, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention. As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items. It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The embodiment of the invention provides a digital twin-based remote fault diagnosis method for a numerical control machine tool, which is shown in fig. 4 and comprises the following steps:
s101, receiving working data of the numerical control machine tool from a sensor data acquisition module.
Wherein the working data includes: static data and dynamic data of the numerical control machine during working;
s102, identifying the numerical control machine tool, and inquiring a digital twin model corresponding to the numerical control machine tool from a model library;
s103, after the working data are imported into a digital twin model corresponding to the numerical control machine tool, the movement behavior of the numerical control machine tool is determined, so that the full life cycle mapping capability of the physical machine tool is achieved, and the detection dimension of the physical machine tool is increased.
The motion behavior comprises scaling, translation and rotation actions of the numerical control machine tool in the working process;
s104, analyzing the abnormality of the working state of the numerical control machine by utilizing the movement behaviors determined in the step 103, and generating a fault analysis report.
In this embodiment, in S103, the determining the motion behavior in the digital twin model of the numerically controlled machine tool includes:
s1031, establishing a basic motion model in a digital twin model of the numerical control machine tool, comprising: scaling, translating and rotating models; all real-time data generated by the physical movement of the numerical control machine tool are collected and transmitted to a virtual system (such as a simulation platform existing in the market at present) loaded and operated with a digital twin model, the real-time data are used for driving the virtual entity to execute corresponding actions after the data are processed, the real-time mapping from the physical entity of the numerical control machine tool to the virtual entity is completed, and the simulation of all operation actions of the numerical control machine tool is realized through the combination of basic movements, so that the construction of a behavior model is completed. The basic motion of the twin model of the numerical control machine is mainly scaling, translation and rotation.
S1032, establishing the motion behavior model according to the basic motion model;
for any vector (x, y, z), the vector (R) is wound around any direction x ，R y ，R z ) RotatingThe angle, translation vector is expressed as (T) x ，T y ，T z ) The scaling vector is (S 1 ，S 2 ，S 3 ). And establishing a motion behavior model according to the principles of scaling, rotation and re-translation, wherein the motion behavior model comprises a motion transformation matrix of the object.
S1033, further determining the triaxial displacement condition of the numerical control machine tool through the motion behavior model.
Specifically, S1031 includes:
wherein the scaling model is used for representing scaling transformation of the vector (x, y, z), and is:
wherein, (S) x ，S y ，S z ) To scale the variables S x 、S y 、S z The size of the scaling factor of the model in the X, Y, Z axis is represented respectively;
the translation model is used to represent a vector translation vector (T x ，T y ，T z ) Is:
wherein T is x 、T y 、T z Respectively representing the translation distance of the model on the X, Y, Z axis;
the rotation model is used to represent the vector (x, y, z) around the direction vector (R x ，R y ，R z ) Rotation angleThe method comprises the following steps:
R x 、R y 、R z the coordinate values of the direction vector in the xyz axis are indicated.
For any vector (x, y, z), the vector (R) is wound around any direction x ，R y ，R z ) RotatingThe angle, translation vector is expressed as (T) x ，T y ，T z ) The scaling vector is (S 1 ，S 2 ，S 3 ). According to the principles of scaling, rotation and re-translation, a motion behavior model is established, wherein a motion transformation matrix comprising an object is as follows:
further, S1033 includes: establishing a motion matrix of the numerical control machine tool in an X axis, a Y axis and a Z axis, and respectively positioning M x 、M y 、M z ；
Wherein the distance between the X axis of the machine tool and the coordinate system of the machine tool is X 0 The displacement matrix M of the X-axis only translates relative to the machine coordinate system x The solution is as follows:
the initial coordinate of the Y-axis relative to the machine tool local coordinate system is Y 0 The Y axis only translates relative to the coordinate system of the machine tool, and the motion matrix M of the Y axis of the machine tool is similar to the translation y The method comprises the following steps:
the motion of the Z axis is combined by the movement of the Y axis and the translation of the Z axis relative to the machine coordinate system, and the combination of the motions is represented by multiplication of a matrix. The initial coordinate of the Z axis relative to the machine tool local coordinate system is Z 0 The translation matrix is M tz Motion matrix M of Z axis of machine tool z The method comprises the following steps:wherein x is 0 、y 0 、z 0 Respectively is: and the X axis of the numerical control machine tool is opposite to the distance of the coordinate system of the numerical control machine tool, the Y axis is opposite to the initial coordinate of the local coordinate system of the machine tool, and the Z axis is opposite to the initial coordinate of the local coordinate system of the machine tool.
In this embodiment, in S104, the analyzing whether the working state of the numerically-controlled machine tool is abnormal includes: performing fault diagnosis on the numerical control machine tool through event detection, wherein the state of the numerical control machine tool is represented by a finite set and is G= (S, sigma, delta, S) 0 ) Where S represents a finite and non-empty set of states, s= { S 0 ，s 1 ，……，s m }，s 0 Representing the initial state s 1 ～s m Representing the 1 st to m th states which may appear in the subsequent machine tool; a set of all types of production events that may occur, Σ= { E 0 ，E 0 ，……，E n }，E 0 Representing the event currently occurring, E 1 ～E n 1 st to n th events which may occur later on of the machine tool are represented; delta is a state transition function for handling production events, denoted delta: sigma (S) →S ', m, n are used as numerical subscripts, specifically positive integers, S' represents an event after a process state transition.
"production event" reflects the state transition of an object, and can be described as follows e= {<s i ，s i+1 >O, A, T, C }, E represents an event occurring during the operation of the numerical control machine;<s i ，s i+1 >representing the state of an object from s i Conversion to s i+1 The method comprises the steps of carrying out a first treatment on the surface of the O represents an event source; a is a set of attributes that produce a trigger condition, a= { a 0 ，A 1 ，……，A n }，A 1 ～A n Represents class 1 to n trigger conditions; t is the occurrence time of the production event, which can be at a time point (i.e., t=t 0 ) Or a time interval (i.e., t= [ T ] 0 ，t 1 ]) C is a finite set of event occurrence conditions;
the constraint conditions of C include: wherein c i Is the ith condition, with attribute A i Representing a partial constraint relationship; f (f) i () Is attribute A i For example instantaneous values at a certain point in time (sensor values) or statistical properties over a certain time interval (average, maximum, minimum); />Representing relational operators, i.e.Alpha is a numerical constant representing a certain threshold value of the attribute; />Representing relational operators, i.e.)>The trigger condition may be constituted by a determination of a single attribute (detected by a certain sensor) and multiple attributes (detected by a plurality of sensors), an event occurring when the attribute value satisfies a predefined condition.
The data processing and fault diagnosis module can receive a plurality of data in the processing process of the numerical control machine tool, is used for comparing with the normal running state database, the alarm information and the attribute data A in the state database, and further analyzes the key data, including judging whether the key components O of the machine tool, such as a main shaft, a feed shaft and the like, run normally according to the indexes of the attributes, such as speed, current, temperature and the like, and performs early warning analysis on the running state of the numerical control machine tool to generate early warning results. For example: whether the working state of the analysis numerical control machine tool is abnormal is as follows: analyzing whether the working state of the numerical control machine tool is abnormal, wherein the main shaft state model comprises:
G main shaft ＝(S Main shaft ，∑ Main shaft ，δ Main shaft ，s 0 ) Wherein, the method comprises the steps of, wherein,
S main shaft ＝{s 0 =rest, s 1 =idle, s 2 =process, s 3 =failure },
∑ main shaft ＝{E 0 ＝s 0 →s 1 ，E 1 ＝s 0 →s 3 ，E 2 ＝s 1 →s 0 ，E 3 ＝s 1 →s 2 ，E 4 ＝s 1 →s 3 ，E 5 ＝s 2 ＝s 1 ，E 6 ＝s 2 →s 3 ，E 7 ＝s 3 →s 0 }，δ Main shaft ＝S Main shaft ×∑ Main shaft →S' Main shaft 。
Object o= { principal axis }; the corresponding relation of the attribute value A= { rotating speed, current and temperature }, the event and the corresponding triggering condition thereof comprises:
(1)E 0 ＝{＜s 0 ，s 1 >, spindle, speed/current, t 0 ，C 0 }，C 0 ＝c 1 ∧c 4 ；
(2)E 1 ＝{<s 0 ，s 3 >Spindle, speed/current/temperature, t 1 ，C 1 }，C 1 ＝c 2 ∧c 6 ∧c 8 ；
(3)E 2 ＝{<s 1 ，s 0 <, spindle, speed/current, t 2 ，C 2 }，C 2 ＝c 0 ^c 3 ；
(4)E 3 ＝{<s 1 ，s 2 <, spindle, speed/current/temperature, t 3 ，C 3 }，C 3 ＝c 1 ∧c 5 ∧c 7 ；
(5)E 4 ＝{＜s 1 ，s 3 >, spindle, speed/current/temperature, t 4 ，C 4 }，C 4 ＝c 2 ∧c 6 ∧c 8 ；
(6)E 5 ＝{<s 2 ，s 1 >Spindle, speed/current/temperature, t 5 ，C 5 }，C 5 ＝c 1 ∧c 4 ∧c 7 ；
(7)E 6 ＝{＜s 2 ，s 3 >, spindle, speed/current/temperature, t 6 ，C 6 }，C 2 ＝c 1 ∧c 3 ∧c 6 ∧c 8 ；
(8)E 7 ＝{＜s 3 ，s 0 >, spindle, speed/current/temperature, t 7 ，C 7 }，C 2 ＝c 0 ∧c 3 ∧c 7 ；
The triggering conditions include:
(1)c 0 =average rotation speed is zero;
(2)c 1 =0 < average rotation speed +.alpha;
(3)c 2 =average rotation speed > α;
(4)c 3 =current average value is zero;
(5)c 4 =0 < average current value +.beta 0 ；
(6)c 5 ＝β 0 The average value of the current is less than or equal to beta 1 ；
(7)c 6 =current average > β 1 ；
(8)c 7 Temperature average value +.gamma;
(9)c 8 temperature average > γ.
In this embodiment, in S104, the generating a fault analysis report includes: matching the abnormal state data with the feature data of the faults occurring in the historical operation data;
generating a fault analysis report according to the matching result, wherein the fault analysis report comprises the following components: the type of the fault currently occurring in the numerical control machine tool and the fault removal history data of the fault currently occurring in the numerical control machine tool.
The sensor data acquisition module comprises a sensor group deployed in a workshop where the numerical control machine tool is located, wherein the types of the sensors comprise: the system comprises an image acquisition module, a position sensor, a speed sensor, an acceleration sensor, a temperature sensor, a pressure sensor, a power sensor and a vibration sensor. The multi-sensor data acquisition module acquires static data information and dynamic data information of the numerical control machine tool, wherein the static data information comprises: numerical control machine structure, geometry, physical properties, working performance and machine model; the dynamic data information includes: real-time velocity, real-time acceleration, temperature information, vibration information, noise information, and loading force information.
The embodiment also provides a digital twin-based remote fault diagnosis system for a numerical control machine tool, which comprises: the system comprises a sensor data acquisition module, a digital twin model construction module, a data processing and fault diagnosis module, a data transmission module and a remote human-computer interaction module.
The sensor data acquisition module is used for acquiring working data of the numerical control machine tool, and the working data comprise: static data and dynamic data of the numerical control machine during working;
the data transmission module is used for uploading the collected working data of the numerical control machine tool;
the digital twin construction module is used for identifying the numerical control machine tool and inquiring a digital twin model corresponding to the numerical control machine tool from a model library; or constructing a digital twin model of the numerical control machine according to the collected real-time operation data and the prestored historical data;
the data processing and fault diagnosis module is used for determining the movement behavior of the numerical control machine after the working data are imported into a digital twin model corresponding to the numerical control machine, wherein the movement behavior comprises the zooming, translation and rotation actions of the numerical control machine in the working process; for example: analyzing the real-time working data to obtain an abnormal state of the numerical control machine tool, matching the abnormal state with fault characteristic data in the historical operation data, and generating a fault analysis report.
The remote man-machine interaction module is used for remotely providing a man-machine interaction interface for personal terminals of personnel, and fault alarm information and diagnosis results are displayed in the interaction interface.
Uploading the working data of the numerical control machine tool to an IoT cloud platform, wherein the IoT cloud platform provides an API interface, and a client establishes connection with the IoT cloud platform through the API interface; the IoT cloud platform is to maintain real-time operational data and pre-store historical data. The IoT cloud platform has data receiving and storing capabilities, and meets the requirement of machine tool large data volume storage. The cloud platform provides a rich API interface for clients to call. By calling the API interface, the remote client can simply and quickly complete the docking with the cloud platform, and the NB-IoT module uploads the data to the IoT cloud platform.
In practical application, the sensor data acquisition module is connected with the entity of the numerical control machine tool and is used for acquiring real-time working data during the production of the numerical control machine tool, inputting the real-time working data into the data transmission module, uploading the acquired data to the cloud platform of the IoT through the NB-IoT module, providing an API interface by the cloud platform of the IoT, butting the cloud platform by the client through the API interface, constructing a digital twin model of the numerical control machine tool through the real-time data and the historical data, analyzing the working data by the data processing and fault diagnosis module to obtain an abnormal state of the numerical control machine tool, matching the abnormal state with fault characteristic data in the historical operation data, generating a fault analysis report, and requesting the cloud platform to read a diagnosis result by remote man-machine interaction to realize remote fault diagnosis.
In one possible application mode, a digital twin model of the numerical control machine tool can be constructed according to real-time operation data and historical data acquired by the cloud platform; the digital twin model construction module is used for preprocessing the data read by the method in claim 4, removing abnormal values, noise and the like, building a dynamic mathematical model according to the preprocessed data, the working principle of a numerical control machine tool and a kinematic equation, training the built mathematical model by using historical data, dividing a data set into a training set and a verification set, training the model by using the training set, and evaluating the performance of the model by using the verification set. And carrying out parameter adjustment and optimization on the model so as to improve the accuracy and generalization capability of the model. After model training is completed, the trained model is deployed on an actual numerical control machine tool and is compared with real-time data, and according to a comparison result, the model is updated and optimized, and finally an accurate twin model is realized.
For example, in practical application, the general architecture shown in fig. 1 may be referred to, the sensor data acquisition module is connected to the entity of the numerically-controlled machine tool, and is configured to acquire real-time working data during production of the numerically-controlled machine tool, input the real-time working data into the data transmission module, the data transmission module uploads the acquired data to the IoT cloud platform through the NB-IoT module, the cloud platform provides an API interface, the remote client interfaces with the cloud platform through the API interface, constructs a digital twin model of the numerically-controlled machine tool through the real-time data and the historical data, the data processing and fault diagnosis module analyzes the working data to obtain an abnormal state of the numerically-controlled machine tool, matches the abnormal state with the fault feature data in the historical operation data, generates a fault analysis report, and the remote man-machine interaction may request the cloud platform to read the diagnosis result, thereby realizing remote fault diagnosis.
In an application example, the sensor data acquisition module includes an image acquisition module, a position sensor, a speed sensor, an acceleration sensor, a temperature sensor, a pressure sensor, a power sensor, a vibration sensor, and the like.
In an application example, the multi-sensor data acquisition module acquires static data information and dynamic data information of the numerical control machine tool, wherein the static data information comprises: numerical control machine structure, geometric dimension, physical attribute, working performance, machine model and the like; the dynamic data information includes: real-time speed, real-time acceleration, temperature information, vibration information, noise information, loading force information, and the like.
The data transmission module uploads the multi-sensor acquisition data to the IoT cloud platform through the NB-IoT module. IoT cloud platforms have data receiving and storage capabilities that meet machine tool mass data storage requirements. The cloud platform provides a rich API interface for clients to call. By calling the API interface, the client can simply and quickly complete the docking with the cloud platform.
In an application example, the cloud platform is an OneNET Internet of things cloud platform provided by China Mobile Internet of things limited company for NB-IoT developers.
The digital twin model construction module acquires data of the cloud platform, performs preprocessing, removes abnormal values, noise and the like of the data, builds a dynamic mathematical model according to the preprocessed data, the working principle of the numerical control machine tool and a kinematic equation, trains the built mathematical model by using historical data, divides a data set into a training set and a verification set, trains the model by using the training set, and evaluates the performance of the model by using the verification set. And carrying out parameter adjustment and optimization on the model so as to improve the accuracy and generalization capability of the model. After model training is completed, the trained model is deployed on an actual numerical control machine tool and is compared with real-time data, and according to a comparison result, the model is updated and optimized, and finally an accurate twin model is realized.
All real-time data generated by the physical movement of the numerical control machine tool are collected and transmitted to a virtual system, the system uses the real-time data to drive the virtual entity to execute corresponding actions after processing the data, the real-time mapping from the physical entity of the numerical control machine tool to the virtual entity is completed, and the simulation of all operation actions of the numerical control machine tool is realized through the combination of basic movements, so that the construction of a behavior model is completed.
In an application example, referring to the model transformation schematic diagram of fig. 2, the basic motions of the twin model of the numerically controlled machine tool are mainly scaling, translation and rotation. Representing the zoom variable as (S 1 ，S 2 ，S 3 ) For any vector (x, y, z), its scaling transforms to:
representing the translation variable as (S) 1 ，S 2 ，S 3 ) For arbitrary vectors (T x ，T y ，T z ) The scaling transformation is as follows:
for vector (x, y, z) around any direction vector (R x ，R y ，R z ) RotatingAngle of：
For any vector (x, y, z), the vector (R) is wound around any direction x ，R y ，R z ) RotatingThe angle, translation vector is expressed as (T) x ，T y ，T z ) The scaling vector is (S 1 ，S 2 ，S 3 ). According to the principles of scaling, rotation and re-translation, the motion transformation matrix of the object is:
the distance between the X axis of the machine tool and the coordinate system of the machine tool is X 0 The X axis only translates relative to the machine coordinate system, 5 is the displacement matrix M of the X axis x The solution is as follows:
the initial coordinate of the Y-axis relative to the machine tool local coordinate system is Y 0 The Y axis only translates relative to the coordinate system of the machine tool, and the motion matrix M of the Y axis of the machine tool is similar to the translation y The method comprises the following steps:
the motion of the Z axis is combined by the movement of the Y axis and the translation of the Z axis relative to the machine coordinate system, and the combination of the motions is represented by multiplication of a matrix. The initial coordinate of the Z axis relative to the machine tool local coordinate system is Z 0 The translation matrix is M tz Motion matrix M of Z axis of machine tool z The method comprises the following steps:
in the application example, the data processing and fault diagnosis module adopts an event detection mode to carry out fault diagnosis on the numerical control machine tool and analyze the running state of the numerical control machine tool. The state of the numerical control machine tool can be represented by a finite set, and a specific model is as follows:
G＝(S，Σ，δ，s 0 ) (8)
where S represents a finite, non-empty set of states, i.e. s= { S 0 ，s 1 ，……，s m -a }; Σ is the set of "events" that may occur, i.e., Σ= { E 0 ，E 0 ，……，E n -a }; δ is a state transition function for handling events, δ: sigma (S) →S'; s is(s) 0 Representing the initial state s 0 E S. "event" herein refers to a production event, reflecting the state transition of an object, and may be described as follows:
E＝{<s i ，s i+1 >，O，A，T，C} (9)
wherein E represents an event occurring during the manufacturing process;<s i ，s i+1 >representing the state of an object from s i Conversion to s i+1 The method comprises the steps of carrying out a first treatment on the surface of the O is the object itself, representing the event source; a is the set of attributes that produce the trigger condition, i.e., a= { a 0 ，A 0 ，……，A n -a }; t is the time of occurrence of the event, and may be at a time point (i.e., t=t 0 ) Or a time interval (i.e., t= [ T ] 0 ，t 1 ]) C is a finite set of event occurrence conditions, and the specific constraints are described as follows:
wherein c i Is the ith condition, with attribute A i Representing a partial constraint relationship; f (f) i () Is attribute A i For example instantaneous values at a certain point in time (sensor values) or statistical properties over a certain time interval (average, maximum, minimum);representing relational operators, i.e.)>Is a numerical constant representing a certain threshold of the attribute; />Representing relational operators, i.e.)>The trigger condition may be constituted by a determination of a single attribute (detected by a certain sensor) and multiple attributes (detected by a plurality of sensors), an event occurring when the attribute value satisfies a predefined condition.
The data processing and fault diagnosis module can receive a plurality of data in the processing process of the numerical control machine tool, is used for comparing with the normal running state database, the alarm information and the attribute data A in the state database, further analyzes the key data, comprises judging whether the key parts O of the machine tool, such as a main shaft, a feed shaft and the like, run normally according to the indexes of the attributes, such as speed, current, temperature and the like, and performs early warning analysis on the running state of the numerical control machine tool to generate early warning results.
Referring to the state detection flow chart of event detection in fig. 3, in an application example, an initialized state is set first, when one data sampling is completed, a system extracts relevant indexes for analysis, whether a predefined condition is met or not is judged to trigger an event, if a calling function delta (E) is triggered to respond to the event Ei, a new control instruction I is generated and is executed by a machine tool control system, then whether the sampling is finished is judged, if yes, the ending is finished, and if no, the steps are repeated; if not, directly judging whether to end sampling.
In the application example, the Power sensor is deployed into the numerical control machine tool for energy consumption monitoring, the event attribute is Power, when the average value of the Power signal in the last 5 seconds is greater than the threshold value 1000W, a predefined condition is triggered to generate a related event, the event describes the transition of the production state, namely the transition from 'to-be-processed' to 'processing', and in the application example, the event is described as
E＝{<To be processed, being processed>Power sensor, power,7.22.10:35:20, average (v) 0 ，v 1 ，v 2 ，v 3 ，v 4 )＞1000W}。
In an application example, when detecting a spindle of a numerical control machine tool, a spindle state model is designed as follows:
G main shaft ＝(S Main shaft ，∑ Main shaft ，δ Main shaft ，s 0 ) Wherein, the method comprises the steps of, wherein,
S main shaft ＝{s 0 =rest, s 1 =idle, s 2 =process, s 3 =failure },
∑ main shaft ＝{E 0 ＝s 0 →s 1 ，E 1 ＝s 0 →s 3 ，E 2 ＝s 1 →s 0 ，E 3 ＝s 1 →s 2 ，E 4 ＝s 1 →s 3 ，E 5 ＝s 2 →s 1 ，E 6 ＝s 2 →s 3 ，E 7 ＝s 3 →s 0 }，δ Main shaft ＝S Main shaft ×∑ Main shaft →S' Main shaft 。
Object o= { principal axis }; the attribute value a= { rotation speed, current, temperature }, event and its corresponding trigger conditions are as follows:
(1)E 0 ＝{<s 0 ，s 1 >spindle, speed/current, t 0 ，C 0 }，C 0 ＝c 1 ∧c 4 ；
(2)E 1 ＝{<s 0 ，s 3 >Spindle, speed/current/temperature, t 1 ，C 1 }，C 1 ＝c 2 ∧c 6 ∧c 8 ；
(3)E 2 ＝{<s 1 ，s 0 >Spindle, speed/current, t 2 ，C 2 }，C 2 ＝c 0 ∧c 3 ；
(4)E 3 ＝{<s 1 ，s 2 >Spindle, speed/current/temperature, t 3 ，C 3 }，C 3 ＝c 1 ∧c 5 ∧c 7 ；
(5)E 4 ＝{<s 1 ，s 3 >Spindle, speed/current/temperature, t 4 ，C 4 }，C 4 ＝c 2 ∧c 6 ∧c 8 ；
(6)E 5 ＝{<s 2 ，s 1 >Spindle, speed/current/temperature, t 5 ，C 5 }，C 5 ＝c 1 ^c 4 ^c 7 ；
(7)E 6 ＝{<s 2 ，s 3 >Spindle, speed/current/temperature, t 6 ，C 6 }，C 2 ＝c 1 ^c 3 ^c 6 ^c 8 ；
(8)E 7 ＝{<s 3 ，s 0 >Spindle, speed/current/temperature, t 7 ，C 7 }，C 2 ＝c 0 ∧c 3 ∧c 7 ；
The triggering conditions can be divided into:
(1)c 0 =average rotation speed is zero;
(2)c 1 =0 < average rotation speed +.alpha;
(3)c 2 =average rotation speed > α;
(4)c 3 =current average value is zero;
(5)c 4 =0 < average current value +.beta 0 ；
(6)c 5 ＝β 0 The average value of the current is less than or equal to beta 1 ；
(7)c 6 =current average > β 1 ；
(8)c 7 Temperature average value +.gamma;
(9)c 8 temperature average > γ.
In an application example, the remote man-machine interaction module can receive an alarm and acquire a diagnosis result.
The embodiment provides a digital twin-based remote fault diagnosis system for a numerical control machine tool, which relates to the fields of remote fault diagnosis and digital twin of the numerical control machine tool, and can be used for carrying out real-time remote diagnosis on faults of the numerical control machine tool, so that the intelligent degree and detection efficiency of the remote fault diagnosis of the machine tool are improved. The system comprises a sensor data acquisition module for acquiring data information of the numerical control machine; the data transmission module is used for inputting information in the machine tool operation information database into the NB-IoT module of the transmission layer through the serial port, and transmitting the information to the NB-IoT cloud platform through the NB-IoT wireless communication network; the digital twin model construction module is used for constructing a digital twin model of the numerical control machine tool according to the real-time operation data and the historical data acquired by the cloud platform; the data processing and fault diagnosis module is used for carrying out fault diagnosis on the numerical control machine tool in an event detection mode, analyzing the running state of the numerical control machine tool, carrying out comparison analysis according to attribute data in a state database and generating a fault diagnosis report; the remote man-machine interaction interface can receive the alarm and acquire the diagnosis result. The digital twin technology is adopted to monitor the behavior of the numerical control machine tool in real time, accurately predict the performance of the numerical control machine tool, achieve the mapping effect of the full life cycle, and simultaneously combine the fault diagnosis technology of the numerical control machine tool with the wireless communication technology on the basis, so that the problems of complex wiring of a wired network, large electromagnetic interference, limited machine tool operation, increased equipment investment and the like can be solved, the intelligent degree of remote fault diagnosis of the machine tool can be improved, and the timeliness of information and the timeliness of fault feedback can be guaranteed.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the apparatus embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points. The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (10)
1. The digital twin-based remote fault diagnosis method for the numerical control machine tool is characterized by comprising the following steps of:
s101, receiving working data of a numerical control machine tool from a sensor data acquisition module, wherein the working data comprise: static data and dynamic data of the numerical control machine during working;
s102, identifying the numerical control machine tool, and inquiring a digital twin model corresponding to the numerical control machine tool from a model library;
s103, after the working data are imported into a digital twin model corresponding to the numerical control machine tool, determining the movement behavior of the numerical control machine tool, wherein the movement behavior comprises scaling, translation and rotation actions of the numerical control machine tool in the working process;
s104, analyzing the abnormality of the working state of the numerical control machine by utilizing the movement behaviors determined in the step 103, and generating a fault analysis report.
2. The method according to claim 1, wherein in S103, the determining the motion behavior in the digital twin model of the numerically controlled machine tool comprises:
s1031, establishing a basic motion model in a digital twin model of the numerical control machine tool, comprising: scaling, translating and rotating models;
s1032, establishing the motion behavior model according to the basic motion model;
s1033, further determining the triaxial displacement condition of the numerical control machine tool through the motion behavior model.
3. The method according to claim 2, characterized in that in S1031, it comprises:
the scaling model is used for representing the scaling transformation of the vector (x, y, z), and is:wherein, (S) x ，S y ，S z ) To scale the variables S x 、S y 、S z The size of the scaling factor of the model in the X, Y, Z axis is represented respectively;
the translation model is used to represent a vector translation vector (T x ，T y ，T z ) Is:
wherein T is x 、T y 、T z Respectively representing the translation distance of the model on the X, Y, Z axis;
the rotation model is used to represent the vector (x, y, z) around the direction vector (R x ，R y ，R z ) Rotation angleThe method comprises the following steps:
4. a method according to claim 2 or 3, wherein S1033 comprises:
establishing motion matrixes of the numerical control machine tool in an X axis, a Y axis and a Z axis, wherein the motion matrixes are M respectively x 、M y 、M z ；
Wherein x is 0 、y 0 、z 0 The method comprises the following steps of: and the X axis of the numerical control machine tool is opposite to the distance of the coordinate system of the numerical control machine tool, the Y axis is opposite to the initial coordinate of the local coordinate system of the machine tool, and the Z axis is opposite to the initial coordinate of the local coordinate system of the machine tool.
5. The method according to claim 1, wherein in S104, the analyzing whether the operation state of the numerically controlled machine tool is abnormal includes:
performing fault diagnosis on the numerical control machine tool through event detection, wherein the state of the numerical control machine tool is represented by a finite set and is G= (S, sigma, delta, S) 0 ) Where S represents a finite and non-empty set of states, s= { S 0 ，s 1 ，……，s m }，s 0 Representing the initial state s 1 ～s m Representing the 1 st to m th states which may appear in the subsequent machine tool; a set of all types of production events that may occur, Σ= { E 0 ，E 0 ，……，E n }，E 0 Representing the event currently occurring, E 1 ～E n 1 st to n th events which may occur later on of the machine tool are represented; delta is a state transition function for handling production events, denoted delta: sigma (S) →S', m, n are used as numerical subscripts, specifically positive integers;
E＝{＜s i ，s i+1 o, A, T, C }, E represents an event occurring during the operation of the numerical control machine;<s i ，s i+1 >representing the state of an object from s i Conversion to s i+1 The method comprises the steps of carrying out a first treatment on the surface of the O represents an event source; a is a set of attributes that produce a trigger condition, a= { a 0 ，A 1 ，……，A n }，A 1 ～A n Represents class 1 to n trigger conditions; t is the time of occurrence of the production event,c is a finite set of event occurrence conditions;
the constraint conditions of C include:wherein c i Is the ith condition, with attribute A i Representing a partial constraint relationship; f (f) i () Is attribute A i Is a function of the arithmetic function of (a); />Representing a relational operator; alpha is a numerical constant; />Representing the relational operator.
6. The method of claim 5, wherein the analyzing whether the operating state of the numerically controlled machine tool is abnormal is: analyzing whether the working state of the numerical control machine tool is abnormal, wherein the main shaft state model comprises:
G main shaft ＝(S Main shaft ，∑ Main shaft ，δ Main shaft ，s 0 ) Wherein S is Main shaft ＝{s 0 =rest, s 1 =idle, s 2 =process, s 3 =failure }, Σ Main shaft ＝{E 0 ＝s 0 →s 1 ，E 1 ＝s 0 →s 3 ，E 2 ＝s 1 →s 0 ，E 3 ＝s 1 →s 2 ，E 4 ＝s 1 →s 3 ，E 5 ＝s 2 →s 1 ，E 6 ＝s 2 →s 3 ，E 7 ＝s 3 →s 0 }，δ Main shaft ＝S Main shaft ×∑ Main shaft →S' Main shaft 。
Object o= { principal axis }; the corresponding relation of the attribute value A= { rotating speed, current and temperature }, the event and the corresponding triggering condition thereof comprises:
(1)E 0 ＝{＜s 0 ，s 1 > -spindleRotational speed/current, t 0 ，C 0 }，C 0 ＝c 1 ∧c 4 ；
(2)E 1 ＝{＜s 0 ，s 3 >Spindle, speed/current/temperature, t 1 ，C 1 }，C 1 ＝c 2 ∧c 6 ∧c 8 ；
(3)E 2 ＝{＜s 1 ，s 0 >, spindle, speed/current, t 2 ，C 2 }，C 2 ＝c 0 ∧c 3 ；
(4)E 3 ＝{<s 1 ，s 2 >Spindle, speed/current/temperature, t 3 ，C 3 }，C 3 ＝c 1 ∧c 5 ∧c 7 ；
(5)E 4 ＝{<s 1 ，s 3 >Spindle, speed/current/temperature, t 4 ，C 4 }，C 4 ＝c 2 ∧c 6 ∧c 8 ；
(6)E 5 ＝{<s 2 ，s 1 >Spindle, speed/current/temperature, t 5 ，C 5 }，C 5 ＝c 1 ^c 4 ^c 7 ；
(7)E 6 ＝{＜s 2 ，s 3 >, spindle, speed/current/temperature, t 6 ，C 6 }，C 2 ＝c 1 ^c 3 ^c 6 ^c 8 ；
(8)E 7 ＝{<s 3 ，s 0 >Spindle, speed/current/temperature, t 7 ，C 7 }，C 2 ＝c 0 ∧c 3 ∧c 7 ；
The triggering conditions include:
(1)c 0 =average rotation speed is zero;
(2)c 1 =0 < average rotation speed +.alpha;
(3)c 2 =average rotation speed > α;
(4)c 3 =current average value is zero;
(5)c 4 =0 < average current value +.beta 0 ；
(6)c 5 ＝β 0 The average value of the current is less than or equal to beta 1 ；
(7)c 6 =current average > β 1 ；
(8)c 7 Temperature average value +.gamma;
(9)c 8 temperature average > γ.
7. The method of claim 1, wherein in S104, the generating a fault analysis report comprises:
matching the abnormal state data with the feature data of the faults occurring in the historical operation data;
generating a fault analysis report according to the matching result, wherein the fault analysis report comprises the following components: the type of the fault currently occurring in the numerical control machine tool and the fault removal history data of the fault currently occurring in the numerical control machine tool.
8. The method of claim 1, wherein the sensor data acquisition module comprises a set of sensors deployed in a plant where the numerically controlled machine tool is located, wherein the types of sensors include: the system comprises an image acquisition module, a position sensor, a speed sensor, an acceleration sensor, a temperature sensor, a pressure sensor, a power sensor and a vibration sensor.
The multi-sensor data acquisition module acquires static data information and dynamic data information of the numerical control machine tool, wherein the static data information comprises: numerical control machine structure, geometry, physical properties, working performance and machine model; the dynamic data information includes: real-time velocity, real-time acceleration, temperature information, vibration information, noise information, and loading force information.
9. A digital twin-based remote fault diagnosis system for a numerical control machine tool, comprising: the system comprises a sensor data acquisition module, a digital twin model construction module, a data processing and fault diagnosis module, a data transmission module and a remote human-computer interaction module.
The sensor data acquisition module is used for acquiring working data of the numerical control machine tool, and the working data comprise: static data and dynamic data of the numerical control machine during working;
the data transmission module is used for uploading the collected working data of the numerical control machine tool;
the digital twin construction module is used for identifying the numerical control machine tool and inquiring a digital twin model corresponding to the numerical control machine tool from a model library; or constructing a digital twin model of the numerical control machine according to the collected real-time operation data and the prestored historical data;
the data processing and fault diagnosis module is used for determining the movement behavior of the numerical control machine after the working data are imported into a digital twin model corresponding to the numerical control machine, wherein the movement behavior comprises the zooming, translation and rotation actions of the numerical control machine in the working process;
the remote man-machine interaction module is used for remotely providing a man-machine interaction interface for personal terminals of personnel, and fault alarm information and diagnosis results are displayed in the interaction interface.
10. The system of claim 9, wherein the work data of the numerically controlled machine tool is uploaded to an IoT cloud platform, wherein the IoT cloud platform provides an API interface through which a client establishes a connection with the IoT cloud platform;
the IoT cloud platform is to maintain real-time operational data and pre-store historical data.
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