WO2020228025A1 - Procédé et appareil pour effectuer une analyse de modélisation sur des données de type d'outils de machine - Google Patents

Procédé et appareil pour effectuer une analyse de modélisation sur des données de type d'outils de machine Download PDF

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WO2020228025A1
WO2020228025A1 PCT/CN2019/087284 CN2019087284W WO2020228025A1 WO 2020228025 A1 WO2020228025 A1 WO 2020228025A1 CN 2019087284 W CN2019087284 W CN 2019087284W WO 2020228025 A1 WO2020228025 A1 WO 2020228025A1
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mathematical model
data
machine tool
parameters
determined
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PCT/CN2019/087284
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English (en)
Chinese (zh)
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罗章维
俞悦
冯程
曲颖
王焦剑
施内加斯丹尼尔
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西门子股份公司
西门子（中国）有限公司
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Priority to PCT/CN2019/087284 priority Critical patent/WO2020228025A1/fr
Publication of WO2020228025A1 publication Critical patent/WO2020228025A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/14—Error detection or correction of the data by redundancy in operation

Definitions

  • the invention relates to the field of monitoring, in particular to monitoring/analyzing the performance of mechanical tools.
  • a long and short memory neural network can be used to predict the remaining service life of mechanical tools. This method needs to know all the data of the machine tool from the beginning of use to the end of its life. Another method is to perform regression analysis by fitting the known data of machine tool parameters to a certain curve equation. It is often difficult to determine what type of curve equation should be used for a specific type of machine tool to obtain the most accurate fit.
  • a method for modeling and analyzing data of a type of mechanical tool includes obtaining first data of a plurality of parameters of each of the plurality of mechanical tools of the type; using at least one unknown coefficient according to a predefined rule to restrict at least one element of the first mathematical model,
  • the first mathematical model represents the relationship between the plurality of parameters; based on the acquired first data of the plurality of parameters of each machine tool and the constrained first mathematical model, it is determined to represent the Second data on the suitability of the first mathematical model to the type of machine tool; determine whether the first mathematical model is suitable based on the second data; and if it is determined that the first mathematical model is not suitable, obtain The new first mathematical model.
  • a device for modeling and analyzing data of one type of machine tool includes an obtaining unit, which is used to obtain the first data of a plurality of parameters of each of the plurality of mechanical tools of the type; a constraint unit, which is used to use at least one pair of unknown coefficients according to a predefined rule At least one component element of the first mathematical model is constrained, the first mathematical model represents the relationship between the plurality of parameters; and a determining unit, which is based on the acquired parameters of the plurality of parameters of each machine tool
  • the first data and the constrained first mathematical model determine second data representing the suitability of the first mathematical model to the type of machine tool, and determine the first mathematical model based on the second data Whether the model is suitable; wherein, if the determining unit determines that the first mathematical model is not suitable, the acquiring unit acquires a new first mathematical model.
  • a machine-readable medium is provided, and program instructions are stored, which when executed by a processor are used to perform the method according to the various embodiments of the present invention.
  • a modeling analysis system including a memory that stores program instructions; and a processor that runs the program instructions to execute the method according to various embodiments of the present invention.
  • the parameter data from a plurality of mechanical tools of the same type is used to check whether the current mathematical model is suitable for modeling the current type of mechanical tool, and when it is determined that it is not suitable, continue to check the next mathematical model , Which can find a more accurate mathematical model for modeling current types of mechanical tools.
  • the new first mathematical model is determined as the current first mathematical model, and the constraint is repeatedly executed for the current first mathematical model, and the second mathematical model is determined The data and the determination are appropriate.
  • the previous first mathematical model when it is determined that the previous first mathematical model is not suitable for modeling the current type of mechanical tool, it can be further judged whether the new first mathematical model is suitable. As a result, iteratively find a more suitable mathematical model for this type of machine tool.
  • constraining at least one component element of the first mathematical model according to a predefined rule includes at least for each independent variable in the first mathematical model, using the The first unknown coefficient in the at least one unknown coefficient is weighted and constrained and/or the second unknown coefficient in the at least one unknown coefficient is used for scaling.
  • weighting constraints on each independent variable in the first mathematical model can ensure that the dimensions of the equation on the left and right of the mathematical model are consistent, and the independent variables are scaled, taking into account the manufacturer’s The difference in scale.
  • the determining the second data includes determining, for each mechanical tool, based on the first data of the plurality of parameters of the mechanical tool, The at least one unknown coefficient of the constrained first mathematical model of the machine tool, thereby obtaining the second mathematical model of the machine tool; and the first parameter based on the plurality of parameters of each machine tool Data and the second mathematical model of each machine tool to determine the second data.
  • the current first mathematical model is first fitted to the first data of each mechanical tool, thereby determining the second mathematical model for each mechanical tool, and then according to the second mathematical model and each mechanical tool.
  • the first data of each machine tool is used to determine the second data, which considers the data of each machine tool to determine the second data indicating the suitability of the current first mathematical model to the current type of machine tool.
  • the determining the second data includes, for each machine tool, the first data and the machine tool based on the plurality of parameters of the machine tool
  • the second mathematical model of the tool determines the loss data for the mechanical tool, and the loss data represents the output data of the selected parameter determined according to the second mathematical model of each mechanical tool and the acquired selected
  • the current first mathematical model is suitable based on the loss data obtained by modeling the first data of each mechanical tool using the current first mathematical model, which is to determine whether the current first mathematical model is suitable It is used to model the current type of mechanical tools to provide a basis for judgment.
  • the first mathematical model is determined by using symbolic regression based on genetic algorithms; the method further includes when it is determined that the first mathematical model is not suitable, based on the The sum of a plurality of loss data determines the new first mathematical model through symbolic regression based on genetic algorithm.
  • the sum of loss data indicating the suitability of the current first mathematical model can be used as feedback and returned to the symbolic regression based on genetic algorithm to further optimize the mathematical model.
  • Model and thereby obtain the optimized mathematical model as the new first mathematical model, which increases the convergence speed of the entire algorithm and obtains the most suitable mathematical model faster.
  • the method further includes outputting the first mathematical model and the constrained first mathematical model for the type of machine tool when it is determined that the first mathematical model is suitable And/or a second mathematical model for each of the at least one target machine tool included in the plurality of machine tools.
  • a mathematical model that is more suitable for modeling certain parameter data of this type of machine tool can also be output for at least one target at one time.
  • At least one fitted mathematical model of the machine tool to provide accurate monitoring/analysis of the at least one target machine tool.
  • Figure 2 shows the first data of a plurality of parameters obtained from seven numerically controlled machine tools according to an embodiment of the present invention
  • 3A-3D show curves of a second mathematical model of a target mechanical tool acquired according to an embodiment of the present invention
  • Figure 4 shows a flowchart of a method for modeling and analyzing data of a type of mechanical tool according to another embodiment of the present invention
  • FIG. 5 shows a flowchart of a method for modeling and analyzing data of a type of mechanical tool according to another embodiment of the present invention
  • Figure 6 shows a block diagram of a device for modeling and analyzing data of a type of machine tool according to an embodiment of the present invention
  • Fig. 7 shows a modeling analysis system according to an embodiment of the present invention.
  • regression analysis is performed on the known parameter data from a specific mechanical tool, and a specific curve model is established. According to the model, the service life of the specific mechanical tool or the early warning of its failure can be predicted. It is unknown what form of model can be used in such regression analysis to obtain more accurate prediction results; and because the model is built only based on the known parameter data of a specific mechanical tool, it is difficult to ensure the accuracy of the model.
  • Fig. 1 shows a flowchart of a method 100 for modeling and analyzing data of a type of mechanical tool according to an embodiment of the present invention.
  • it is possible to simultaneously perform modeling analysis on data of a plurality of mechanical tools of the same type. It can be expected that only a part of the plurality of mechanical tools or one mechanical tool is the target mechanical tool of interest, and the data of other mechanical tools of the same type is introduced only to find the mathematical model form for the target mechanical tool more accurately. It can also be expected that the plurality of mechanical tools does not contain the target mechanical tool, but only to find the most suitable mathematical model for the type of mechanical tool. After the mathematical model is found, the mathematical model can be fitted to the target of the type. Machine tool data. It can also be expected that the plurality of machine tools are all target machine tools, and thus, the respective fitting mathematical models for the plurality of machine tools of the same type can be found at one time.
  • first data of a plurality of parameters of each of the plurality of machine tools is acquired, where the plurality of machine tools are the same type of machine tool, preferably, the so-called same type does not only refer to
  • These mechanical tools belong to the same type from the point of view of their functions/functions, which also means that these mechanical tools are all operated in the same or similar operating environment. For example, they are the same type of motors working in the same environment.
  • the multiple parameters of these mechanical tools are related to their performance. For example, for a motor, its operating temperature may indicate its performance and aging trend. Therefore, the operating temperature data of the motor can be obtained over time. In this case, multiple parameters can refer to the working temperature of the motor and the corresponding time.
  • the plural parameters are the temperature of the CNC machine tool and the corresponding time.
  • the chiller in order to maintain the predetermined temperature, the chiller needs to reach a certain cooling load, which means that each chiller needs Consuming a certain amount of energy, it is desirable to model the energy consumed by the chiller and the corresponding cooling load to predict the energy required to reach the predetermined cooling load or the cooling load that can be achieved under the predetermined energy. In this case, multiple parameters can be the energy consumed by the chiller and the corresponding cooling load.
  • the first data of a plurality of parameters for each machine tool can be obtained separately, and they can be stored for processing. It is expected that the first data of a plurality of parameters of each machine tool can be obtained through various types of sensors installed to the machine tool.
  • Figure 2 shows the first data obtained from seven CNC machine tools, where the x-axis represents time and the y-axis represents the reciprocal of the energy used. According to this embodiment, the energy used by this type of machine tool is modeled over time.
  • a first mathematical model is obtained, which can represent the relationship between a plurality of parameters related to the performance of each mechanical tool. For example, for motors or CNC machine tools, it can represent the relationship between temperature and time, or the relationship between used energy and time.
  • the first mathematical model is used here, this does not mean any limitation, and is only for distinguishing from the second mathematical model later.
  • the first mathematical model obtained can be any mathematical model.
  • the first mathematical model obtained can be any mathematical model.
  • the first mathematical model can be obtained in a variety of ways.
  • a plurality of expected mathematical models can be stored in a memory, and one of the mathematical models can be obtained from the memory as the first mathematical model.
  • the first mathematical model can be a mathematical model determined by using a symbolic regression method based on genetic algorithms. This will be described in more detail in the following embodiments.
  • the mathematical model can be selected according to the object to be monitored/analyzed. For example, when the object to be monitored/analyzed does not involve periodic motion, for example, when the vibration parameters of the machine tool are not monitored, it is not necessary to select operations such as sine and cosine These periodic mathematical models can speed up the convergence rate of the entire analysis.
  • At 130 at least one component element in the acquired first mathematical model is restricted.
  • only the structure of the acquired first mathematical model may be retained, and one or more of the constituent elements including independent variables, arithmetic functions, and/or constants may be respectively restricted.
  • An offset can be added to the first mathematical model.
  • At least one unknown coefficient may be used to perform one or more constraints on the first mathematical model according to a predefined rule.
  • a first unknown coefficient in at least one unknown coefficient is used for weighted constraint and/or a second unknown coefficient in at least one unknown coefficient is used for Scaled.
  • the independent variables in the first mathematical model can be weighted and constrained to ensure that the dimensions of both sides of the equation of the mathematical model are consistent, and the independent variables can be scaled to consider the difference of each machine tool at the factory setting. Further preferably, the calculation function in the first mathematical model can be weighted and restricted to further ensure that the dimensions of both sides of the equation of the mathematical model are consistent.
  • constraint rules For example, for a mathematical model representing sensor data y that changes with time t, the following constraint rules can be predefined:
  • m, c, n, K, O are unknown coefficients.
  • m is a weighted constraint on the independent variable t
  • c is a scaled on the independent variable t.
  • m 1 , m 2 , c 1 , c 2 , n 1 , n 2 , K 1 , and O are all unknown coefficients.
  • the second data of the suitability of this type of machine tool in other words, the second data indicating the accuracy of modeling the first data of the plurality of parameters of this type of machine tool using the current first mathematical model .
  • At least one unknown coefficient for constraining the first mathematical model can be determined for each machine tool. This can be achieved by fitting the first data of a plurality of parameters from each machine tool based on the above-mentioned constrained first mathematical model, such as the above formula (2), to solve the above-mentioned constrained first mathematical model Unknown coefficient to achieve.
  • a second mathematical model for each machine tool is obtained.
  • the second mathematical model is a fitting mathematical model for each mechanical tool obtained by respectively fitting the constrained first mathematical model to the first data of each mechanical tool.
  • the above constrained first mathematical model can be used to fit the parameter data of each of these 7 different mechanical tools , Thereby obtaining 7 curves for the 7 different mechanical tools, and then obtaining 7 second mathematical models, and each second mathematical model is for each of the 7 different mechanical tools.
  • y 1 n 11 *exp(n 21 *tanh(-(m 11 *t+c 11 )))+(m 21 *t+c 21 ) K11 +O 1 (3).
  • y 2 n 12 *exp(n 22 *tanh(-(m 12 *t+c 12 )))+(m 22 *t+c 22 ) K12 +O 2 (4).
  • m 1i , m 2i , c 1i , c 2i , n 1i , n 2i , K 1i , O i are all known coefficients derived by curve fitting for parameter data of different mechanical tools.
  • the loss for each mechanical tool can be determined, for example, by substituting the data of multiple parameters of each mechanical tool into the corresponding second mathematical model to obtain the parameter value derived from the second mathematical model The difference between the actual parameter value of the machine tool and the actual parameter value to determine the loss data for each machine tool.
  • the total loss data for all machine tools of the same type can be determined.
  • the total loss data can be calculated by using the following formula to sum the loss data for each machine tool.
  • y i data is the sensor measurement data of the parameters of the machine tool acquired at 110.
  • the second data is determined at 140, it can be determined at 150 whether the current first mathematical model is suitable based on the second data, that is, whether the current first mathematical model is suitable for modeling the current type of machine tool.
  • whether the first mathematical model is suitable can be determined based on the above-mentioned total loss data. For example, the total loss data can be compared with a predetermined threshold range. If the total loss data is within the predetermined threshold range, it indicates that the first mathematical model is acceptable, that is, the form of the currently used first mathematical model is appropriate , No need to reacquire. Otherwise, it indicates that the first mathematical model needs to be reacquired.
  • the total loss data determined for the previous first mathematical model can be compared with the total loss data determined according to the current first mathematical model, and whether the current first mathematical model is suitable can be determined according to the comparison result.
  • the current first mathematical model is not suitable for modeling the current type of mechanical tool, or the accuracy of modeling the current type of mechanical tool using the current first mathematical model does not meet the predetermined standard, you need to return 120 obtain a new first mathematical model to represent the relationship between a plurality of parameters related to the performance of a plurality of machine tools. For example, a new first mathematical model can be obtained from the memory. After that, the process from 130 to 150 is repeated for the new first mathematical model.
  • the current first mathematical model is suitable, it indicates that the accuracy of modeling the current type of mechanical tool using the current first mathematical model meets the predetermined standard.
  • the first mathematical model or /The first mathematical model constrained so that the form of the first mathematical model is used when modeling the same type of machine tool later.
  • the second mathematical model for each machine tool in at least one machine tool as the target machine tool among the plurality of machine tools can be output , For further processing.
  • the second mathematical model of each target machine tool can be used to predict the remaining service life of the machine tool.
  • a predetermined threshold for sensor data such as temperature data
  • the predetermined threshold represents parameter data corresponding to the end of the life of the machine tool, such as the temperature at the end of its life.
  • the time corresponding to when the sensor data yi reaches the predetermined threshold can be determined, and the current time can be easily determined according to the determined time and the current time.
  • the second mathematical model can be used to predict the energy consumed by the chiller as described above to reach the predetermined cooling load, and vice versa.
  • the following constrained first mathematical model has the lowest loss data, or the loss data meets a predetermined standard
  • the constrained first mathematical model can be output. Among them, a 1 , a 2 , a 3 , a 4 , a 5 , b 1 , b 2 , b 3 and D are unknown coefficients.
  • the second mathematical model of the target machine tool obtained according to the first mathematical model can also be output.
  • the above-mentioned second mathematical models (8)-(11) can be output for further analysis of these target machine tools. It can be seen from the above second mathematical model (8)-(11) that there are some items with very small coefficients, such as 10-13 . In regression analysis according to the prior art, these items with very small coefficients are often ignored. Obviously, using the above-mentioned method according to the embodiment of the present invention, a more accurate model can be obtained.
  • 3A-3D show the curves of the above-mentioned second mathematical model. The x-axis represents time, and the y-axis represents the value related to sensor data, specifically the reciprocal of the energy used.
  • FIG. 4 shows a flowchart of a method 200 for modeling and analyzing data of a type of machine tool according to another embodiment of the present invention.
  • a symbolic regression method based on genetic algorithm is used to obtain the first mathematical model, and the first mathematical model is gradually optimized based on the second data determined above.
  • the first mathematical model is determined by using a symbolic regression method based on genetic algorithm based on the acquired data.
  • the same processing as 130 is performed.
  • the second data representing the suitability of the current first mathematical model for modeling the type of machine tool is determined at 240, at 250, the second data for the current first mathematical model is compared with the previous first mathematical model.
  • Compare the second data of the model for example, calculate the difference between the second data for the current first mathematical model and the second data for the previous mathematical model, and it can also be expected to calculate the second data for the current first mathematical model and For the difference between the average values of the second data of all previous first mathematical models; based on the comparison result, it is determined whether the current first mathematical model is suitable.
  • the comparison result can be compared with a predetermined threshold. It is said that the above determined difference can be compared with a predetermined threshold. When the difference is less than the predetermined threshold, it indicates that as the first mathematical model is optimized using the symbolic regression method based on genetic algorithms, the current first mathematical model is used to The loss of parameter data modeling of the current type of mechanical tool is reduced in line with expectations.
  • the current first mathematical model can proceed to 260 to output the current first mathematical model, the constrained current first mathematical model, or the second mathematical model corresponding to the target mechanical tool; If the difference is still greater than the predetermined threshold, it means that the current first mathematical model still needs to be optimized, and the currently obtained second data is returned to 220, which is used in the symbolic regression method based on genetic algorithm to continue to optimize the first mathematical model.
  • FIG. 3 shows a flowchart of a method 300 for modeling and analyzing data of a type of machine tool according to another embodiment of the present invention.
  • the processes 310-340 are the same as the processes 110-140 according to the method 100 shown in Fig. 1 described above.
  • the second data corresponding to the smallest second data is determined by comparing the current second data with the second data determined for the previous first mathematical model at 341 Model, and store the second mathematical model corresponding to the smallest second data, and determine at 342 whether a predetermined number of first mathematical models have been repeatedly acquired, if it is determined that a predetermined number of first mathematical models have been acquired, then 360 directly outputs the first mathematical model corresponding to the current smallest second data or the second mathematical model for the target machine tool among the plurality of second mathematical models.
  • the second data determined for the current first mathematical model meets a predetermined standard, for example, the same as that determined for the previous first mathematical model. Whether the difference of the second data is less than the predetermined threshold, if it is satisfied, output the current first mathematical model or the second mathematical model for the target machine tool from the plurality of second mathematical models determined according to the current first mathematical model at 360, if If not satisfied, return to 320 to obtain a new first mathematical model.
  • a predetermined standard for example, the same as that determined for the previous first mathematical model.
  • FIG. 4 shows a device 10 for modeling and analyzing data of a type of mechanical tool according to an embodiment of the present invention.
  • the device 10 includes an acquisition unit 11, a restriction unit 12, a determination unit 13, and an output unit 14.
  • the obtaining unit 11 obtains data of a plurality of parameters of a plurality of mechanical tools of this type and the current first mathematical model.
  • the device 10 may include a memory for storing data of a plurality of parameters and a plurality of first mathematical models that may be used.
  • the first mathematical model represents the relationship between a plurality of parameters related to the performance of this type of machine tool.
  • the acquisition unit 11 has processing capability, which can determine the first mathematical model by using symbolic regression based on genetic algorithm, or it can also be expected to use symbolic regression based on genetic algorithm to determine the first mathematical model in other locations.
  • the acquiring unit 11 only acquires the determined first mathematical model.
  • the restriction unit 12 receives the first mathematical model from the acquisition unit 11, and is configured to use at least one unknown coefficient to restrict at least one component element of the first mathematical model according to a predefined rule.
  • Pre-defined rules can be stored in memory.
  • the at least one component element refers to one or more of the independent variables, operation functions, constants, and/or offsets in the first mathematical model.
  • the constraint unit 12 uses the first unknown coefficient in the at least one unknown coefficient for weighted constraint and/or uses the second unknown coefficient in the at least one unknown coefficient for each independent variable in the current first mathematical model.
  • the coefficients are scaled.
  • the determining unit 13 receives the constrained first mathematical model from the constraining unit 12 and the first data of the plural parameters of the plural mechanical tools from the acquiring unit 11, based on the value of each of the plural mechanical tools of the same type.
  • the first data of a plurality of parameters and the constrained first mathematical model determine the second data indicating the suitability of the current first mathematical model to the type of machine tool, and determine the current first mathematical model based on the second data Whether the model is suitable, that is, whether it is necessary to obtain a new first mathematical model. If the determining unit 13 determines that a new first mathematical model needs to be acquired, the acquiring unit 11 is notified to acquire the new first mathematical model as the current first mathematical model, which is further processed by the constraint unit 12 and the determining unit 13.
  • the determining unit 13 determines the first constrained mathematical model for the machine tool based on the first data of the plurality of parameters of the machine tool At least one unknown coefficient of, thereby obtaining a second mathematical model for the machine tool; and determining the second data based on the first data of the plurality of parameters of each machine tool and the corresponding second mathematical model.
  • the second data is loss data, which represents the difference between the output data of the selected parameter determined according to the second mathematical model of each machine tool and the acquired measurement value of the selected parameter The difference.
  • the determining unit 13 determines the loss data for the machine tool based on the first data and the second mathematical model of the plurality of parameters of the machine tool; determines the loss data for the plurality of machine tools The sum of the data obtains the total loss data; and based on the total loss data, it is determined whether the current first mathematical model is suitable.
  • the first mathematical model is determined by using symbolic regression based on genetic algorithm; therefore, when the determining unit 13 determines that the first mathematical model is not suitable, the acquiring unit may use genetic algorithm-based Symbolic regression determines a new first mathematical model based on the sum of loss data.
  • the output unit 14 is used to output the current first mathematical model, the constrained current first mathematical model, or the second mathematical model for the target machine tool among the plurality of machine tools. .
  • the device 10 further includes an evaluation unit 15 for predicting the remaining service life of the corresponding mechanical tool based on the output second mathematical model for each mechanical tool.
  • Fig. 7 shows a modeling analysis system 20 according to an embodiment of the present invention.
  • the modeling analysis system 20 includes a memory 21 and a processor 22.
  • the memory 21 stores computer program instructions, which when executed, can implement the methods according to the embodiments of the present disclosure.
  • the processor 22 is configured to run these program instructions to implement the method according to the embodiments of the present disclosure as described above. It can be understood that the memory 21 is in a remote location, such as the cloud, and the processor 22 receives program instructions from the cloud through the network.
  • Exemplary embodiments of the present disclosure cover both of the following: creating/using the computer program/software of the present disclosure from the beginning, and converting an existing program/software to the computer program/software using the present disclosure by means of updating.
  • a machine such as a computer
  • a machine such as a computer
  • the readable medium has computer program code stored thereon, and the computer program code when executed
  • the computer or the processor executes the method according to the embodiments of the present disclosure.
  • the machine-readable medium is, for example, an optical storage medium or a solid-state medium supplied with or as part of other hardware.
  • the computer program for executing the method according to the various embodiments of the present disclosure may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.
  • the computer program can also be provided on a network such as the World Wide Web, and can be downloaded from such a network to the working computer of the processor.

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Abstract

L'invention concerne un procédé et un appareil destiné à effectuer une analyse de modélisation sur des données d'un type d'outils de machine. Le procédé consiste à : acquérir des premières données de multiples paramètres de chacun de multiples outils de machine qui sont du même type ; contraindre, selon une règle prédéterminée, au moins un composant d'un premier modèle mathématique à l'aide d'au moins un coefficient inconnu, le premier modèle mathématique représentant une relation entre les multiples paramètres ; déterminer, sur la base des premières données acquises des multiples paramètres de chacun des outils de machine et du premier modèle mathématique contraint, des secondes données représentant un niveau adapté du premier modèle mathématique pour le type des outils de machine ; déterminer, sur la base des secondes données, si le premier modèle mathématique est adapté ; et si le premier modèle mathématique est déterminé comme n'étant pas adapté, acquérir un nouveau premier modèle mathématique. De cette manière, l'invention fournit un modèle d'analyse mieux adapté à un type spécifique d'outils de machine, permettant ainsi l'acquisition d'un résultat d'analyse de données précis.
PCT/CN2019/087284 2019-05-16 2019-05-16 Procédé et appareil pour effectuer une analyse de modélisation sur des données de type d'outils de machine WO2020228025A1 (fr)

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