CN111008502B - A digital twin-driven fault prediction method for complex equipment - Google Patents
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Abstract
本发明公开了一种数字孪生驱动的复杂装备故障预测方法和装置，包括：复杂装备数字孪生体建立模块，该模块能够建立与设置几何模型、物理参数、运行行为以及限制约束；数字孪生体校准模块，该模块利用数字孪生体生成的数据和实体装备采集数据的偏差进行校准；故障数据生成模块，该模块能够根据设置的故障行为生成故障数据；故障预测模型训练及验证模块，该模块能够设置训练参数并训练神经网络。本发明公开的方法能够解决获取复杂装备故障数据主要依赖实验造成的成本高且数据不全面问题，使复杂装备的故障预测的成本降低，并且提高了故障预测的准确性。
The invention discloses a digital twin-driven complex equipment fault prediction method and device, comprising: a complex equipment digital twin body establishment module, which can establish and set geometric models, physical parameters, operating behavior and restriction constraints; digital twin body calibration module, which uses the data generated by the digital twin to calibrate the deviation of the data collected by the physical equipment; fault data generation module, which can generate fault data according to the set fault behavior; fault prediction model training and verification module, which can set Train the parameters and train the neural network. The method disclosed by the invention can solve the problems of high cost and incomplete data caused by mainly relying on experiments to obtain the fault data of complex equipment, reduce the cost of fault prediction of complex equipment, and improve the accuracy of fault prediction.
Description
Technical Field
The invention belongs to the field of electronic engineering and computer science, and particularly relates to a digital twin-driven complex equipment fault prediction method.
Background
In the manufacturing field, various complex equipment is provided, and the fault prediction of the complex equipment is always the key point of expert research in various countries due to the characteristics of multiple structures, high price of single equipment, complex process and the like of the complex equipment. Many failure prediction techniques have been studied, including linear correlation methods, but these methods have significant disadvantages, cannot predict the simultaneous occurrence of multiple failures, and also have problems in that the prediction result is insufficient when a single failure occurs, and therefore, it is necessary to train a neural network prediction model using a method with higher accuracy. However, training a neural network requires a large amount of data, and in the field of failure prediction, a large amount of failure data is required, and the acquisition of the failure data becomes a difficult problem: the experimental method is too high in cost and irrecoverable, time cost cannot be estimated when a historical database is established, and partial stable complex equipment cannot break down even for a long time.
Disclosure of Invention
Aiming at the problem that fault data are difficult to acquire due to the use of a neural network method, in order to solve the technical problem, the invention provides a method for acquiring fault data by establishing a complex equipment twin body matched with actual equipment by using a digital twin technology and performing simulation generation of the fault data on each fault and the combination of the faults of the equipment. The method can effectively generate the fault data of the complex equipment, thereby greatly reducing the cost for acquiring the fault prediction data of the complex equipment, and ensuring that the method has higher precision in the aspect of fault prediction by using the neural network prediction method. The method can solve the problems of difficult acquisition of the fault prediction data of the complex equipment and low prediction accuracy, and can realize the high-accuracy fault prediction of the complex equipment.
The technical problem to be solved by the invention is realized by adopting the following technical scheme: a digital twin driven complex equipment failure prediction method, comprising:
a digital twin driven complex equipment failure prediction method comprises the following steps:
step (1), establishing a complex equipment digital twin body, wherein the step is specifically realized as follows:
firstly, aiming at a complex device, according to the composition structure of the device, MATLAB software is used for establishing a geometric model of the device, and the model needs to reflect the structure and the assembly relation of the device;
adding physical attributes to each structure in the equipment geometric model by using an MATLAB software toolbox;
creating a production behavior according to the characteristics of the complex equipment, wherein the behavior comprises the response of each component of the equipment to the behavior and the characteristic of generating data;
setting production behavior constraints according to the specific field of the complex equipment, wherein the specific constraints with multiple dimensions and multiple ranges are required to be set;
fifthly, sorting, classifying and screening the parameters according to the structural characteristics of the equipment and the parameters collected in the production process, and establishing a parameter set of the equipment, wherein the parameter set is the parameter set collected under various fault conditions and input into a neural network;
step (2) and a digital twin calibration step, wherein the step is specifically realized as follows:
simulating the production process which is the same as the equipment entity according to the established complex equipment digital twin body and the actual production plan of the equipment; the twin body is established for simulating fault data of actual equipment through the twin body, and the twin body needs to be calibrated to be in line with the actual equipment, so that actual production process data is used for calibration;
generating simulation data of each parameter in the production process;
calculating the deviation value of the actual data and the simulated data, judging whether the twin model is matched with the equipment entity according to the deviation value, if so, turning to fifth, and if not, turning to fourth;
fourthly, calculating a gradient according to the deviation value, adjusting parameters of the twin model and converting;
fifthly, completing the construction of a digital twin body of complex equipment;
step (3), generating fault data, wherein the step is specifically realized as follows:
setting fault behaviors, namely actual fault conditions simulated by a twin body, including conditions of a single structure and a plurality of combined structures, and respectively generating corresponding fault data of different conditions;
simulating production behaviors containing faults according to the selected fault behaviors to generate simulation data;
calculating the deviation value of the set fault behavior according to the data reference value/expected value of the equipment production behavior;
fourthly, converting the deviation value into a characteristic vector to form a characteristic vector group;
step (4), training and verifying a fault prediction model, wherein the step is specifically realized as follows:
setting BP neural network training parameters;
secondly, training a neural network; obtaining a deviation value by training a twin of the equipment and setting corresponding fault behaviors, processing the deviation value, and converting the deviation value into a characteristic vector group, wherein the characteristic vector group is a data source for training a neural network;
using the actual data and the simulation data to calibrate and verify the neural network, and if the training requirements are not met, modifying the training parameters to continue training until the neural network meeting the fitting requirements is trained;
and fourthly, inputting the data acquired in the actual operation process of the complex equipment into the trained neural network, and outputting the prediction conditions of various faults and fault combinations by the neural network.
According to another aspect of the present invention, a digital twin driven complex equipment failure prediction apparatus is provided, including:
(1) the complex equipment digital twin establishing module is specifically realized as follows:
firstly, aiming at a complex device, according to the composition structure of the device, MATLAB software is used for establishing a geometric model of the device, and the model needs to reflect the structure and the assembly relation of the device;
adding physical attributes to each structure in the equipment geometric model by using an MATLAB software toolbox;
creating a production behavior according to the characteristics of the complex equipment, wherein the behavior comprises the response of each component of the equipment to the behavior and the characteristic of generating data;
setting production behavior constraints according to the specific field of the complex equipment, wherein the specific constraints with multiple dimensions and multiple ranges are required to be set;
fifthly, sorting, classifying and screening the parameters according to the structural characteristics of the equipment and the parameters collected in the production process, and establishing a parameter set of the equipment, wherein the parameter set is the parameter set collected under various fault conditions and input into a neural network;
(2) the digital twin body calibration module is implemented as follows:
simulating the production process which is the same as the equipment entity according to the established complex equipment digital twin body and the actual production plan of the equipment; the twin body is established for simulating fault data of actual equipment through the twin body, and the twin body needs to be calibrated to be in line with the actual equipment, so that actual production process data is used for calibration;
generating simulation data of each parameter in the production process;
calculating the deviation value of the actual data and the simulated data, judging whether the twin model is matched with the equipment entity according to the deviation value, if so, turning to fifth, and if not, turning to fourth;
fourthly, calculating a gradient according to the deviation value, adjusting parameters of the twin model and converting;
fifthly, completing the construction of a digital twin body of complex equipment;
(3) the fault data generation module is specifically implemented as follows:
setting fault behaviors, namely actual fault conditions simulated by a twin body, including conditions of a single structure and a plurality of combined structures, and respectively generating corresponding fault data of different conditions;
simulating production behaviors containing faults according to the selected fault behaviors to generate simulation data;
calculating the deviation value of the set fault behavior according to the data reference value/expected value of the equipment production behavior;
fourthly, converting the deviation value into a characteristic vector to form a characteristic vector group;
(4) the fault prediction model training and verifying module is specifically realized as follows:
setting BP neural network training parameters;
secondly, training a neural network; obtaining a deviation value by training a twin of the equipment and setting corresponding fault behaviors, processing the deviation value, and converting the deviation value into a characteristic vector group, wherein the characteristic vector group is a data source for training a neural network;
using the actual data and the simulation data to calibrate and verify the neural network, and if the training requirements are not met, modifying the training parameters to continue training until the neural network meeting the fitting requirements is trained;
and fourthly, inputting the data acquired in the actual operation process of the complex equipment into the trained neural network, and outputting the prediction conditions of various faults and fault combinations by the neural network.
Compared with the prior art, the invention has the advantages that:
(1) the invention uses the digital twin technology, and autonomously generates the fault data required by training the neural network by establishing a complex equipment digital twin body mode, thereby greatly reducing the time cost and the economic cost and solving the problem of difficulty in acquiring the fault data.
(2) Compared with the traditional linear deviation method and the like, the method has good prediction speed and higher accuracy by using the neural network method.
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FIG. 1 is a block diagram of the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying 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, rather than all embodiments, and all other embodiments obtained by a person skilled in the art without creative efforts based on the embodiments in the present invention belong to the protection scope of the present invention. .
The invention relates to a digital twin driven complex equipment fault prediction method. The method provides that a digital twinning technology is utilized to establish a complex equipment twinning body matched with actual equipment, and fault data is simulated and generated for each fault and the combination of the faults of the equipment, so that the fault data is acquired. The method can effectively generate the fault data of the complex equipment, thereby greatly reducing the cost for acquiring the fault prediction data of the complex equipment, and ensuring higher precision of the method in the aspect of fault prediction by using the neural network prediction method.
Fig. 1 shows a block diagram of a digital twin-driven complex equipment failure prediction device, and the specific implementation manner is as follows:
(1) fig. 1 shows a complex equipment digital twin establishment module, which is implemented as follows:
firstly, aiming at a certain specific complex equipment, establishing a geometric model of the equipment by using MATLAB software according to a composition structure of the equipment, wherein the model needs to embody the structure and the assembly relation of the equipment;
adding physical attributes to each structure in the equipment geometric model by using an MATLAB software toolbox;
creating a production behavior according to the characteristics of the complex equipment, wherein the behavior comprises the response of each component of the equipment to the behavior and the characteristic of generating data;
fourthly, according to the specific field of the complex equipment, setting production behavior constraint of the complex equipment, wherein the constraint has great influence on subsequently generated fault data, so that multi-dimensional and multi-range specific constraint is needed;
fifthly, sorting, classifying and screening the parameters according to the structural characteristics of the equipment and the parameters collected in the production process, and establishing a parameter set of the equipment, wherein the parameter set is the parameter set collected under various fault conditions and input into a neural network;
(2) fig. 1, 2 is a digital twin calibration module, which is implemented as follows:
simulating the production process which is the same as the equipment entity according to the established complex equipment digital twin body and the actual production plan of the equipment; the twin body is established for simulating the fault data of the actual equipment through the twin body, and the twin body needs to be calibrated to be as close to the actual equipment as possible, so that the actual production process data is used for calibration.
Generating simulation data of each parameter in the production process;
calculating the deviation value of the actual data and the simulated data, judging whether the twin model is matched with the equipment entity according to the deviation value, if so, turning to fifth, and if not, turning to fourth;
fourthly, calculating a gradient according to the deviation value, adjusting parameters of the twin model and converting;
fifthly, completing the construction of a digital twin body of complex equipment;
(3) fig. 1, 3 is a fault data generation module, which is implemented as follows:
setting fault behaviors, namely actual fault conditions simulated by a twin body, including conditions of a single structure and a plurality of combined structures, and respectively generating corresponding fault data of different conditions;
simulating production behaviors containing faults according to the selected fault behaviors to generate simulation data;
thirdly, calculating the deviation value of the fault behavior set in the first step according to the data reference value/expected value of the production behavior of the equipment; the parameter set established by the module 1, such as the heating rate, the holding temperature, the full pressure of the fan and the like, has reference/expected values under the condition of no fault, such as the holding temperature between 118 ℃ and 120 ℃, and the product quality/process is in accordance with the requirements. The step is to calculate the fault behavior deviation set in the step (i).
Fourthly, converting the deviation value into a characteristic vector to form a characteristic vector group;
(4) fig. 1, 4, is a fault prediction model training and verifying module, which is implemented as follows:
setting BP neural network training parameters;
secondly, training a neural network; one of the contributions of the invention is to solve the problem of the fault data source of the complex equipment, obtain the deviation value by training the twin of the equipment and setting the corresponding fault behavior, process the deviation value and convert the deviation value into a characteristic vector group, wherein the characteristic vector group is the data source for training the neural network.
Using the actual data and the simulation data to calibrate and verify the neural network, and if the training requirements are not met, modifying the training parameters to continue training until the neural network meeting the fitting requirements is trained;
and fourthly, inputting the data acquired in the actual operation process of the complex equipment into the trained neural network, and outputting the prediction conditions of various faults and fault combinations by the neural network.
In summary, the present invention discloses a method for predicting a failure of a complex device driven by a digital twin, including: the device comprises a complex equipment digital twin body establishing module, a digital twin body calibrating module, a fault data generating module and a fault prediction neural network training and verifying module. The method disclosed by the invention can solve the problems of high cost and incomplete data caused by the fact that the acquisition of the fault data of the complex equipment mainly depends on experiments, so that the cost of fault prediction of the complex equipment is reduced, and the accuracy of fault prediction is improved.
Those skilled in the art will appreciate that the invention may be practiced without these specific details.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (2)
1. A method for predicting faults of complex equipment driven by digital twins is characterized by comprising the following steps:
step (1), establishing a complex equipment digital twin body, wherein the step is specifically realized as follows:
firstly, aiming at a complex device, according to the composition structure of the device, MATLAB software is used for establishing a geometric model of the device, and the model needs to reflect the structure and the assembly relation of the device;
adding physical attributes to each structure in the equipment geometric model by using an MATLAB software toolbox;
creating a production behavior according to the characteristics of the complex equipment, wherein the behavior comprises the response of each component of the equipment to the behavior and the characteristics of generating data;
fourthly, setting production behavior constraints according to the specific field of the complex equipment, wherein the specific constraints with multiple dimensions and multiple ranges are required to be set;
according to the structural characteristics of the equipment and the parameters collected in the production process, sorting, classifying and screening the parameters, and establishing a parameter set of the equipment, wherein the parameter set is the parameter set which is collected under various fault conditions and input into a neural network;
step (2) and a digital twin calibration step, wherein the step is specifically realized as follows:
simulating the production process which is the same as the equipment entity according to the established complex equipment digital twin body and the actual production plan of the equipment; the twin body is established for simulating fault data of actual equipment through the twin body, and the twin body needs to be calibrated to be in line with the actual equipment, so that actual production process data is used for calibration;
generating simulation data of each parameter in the production process;
calculating the deviation value of the actual data and the simulated data, judging whether the twin model is matched with the equipment entity according to the deviation value, if so, turning to fifth, and if not, turning to fourth;
fourthly, calculating a gradient according to the deviation value, adjusting parameters of the twin model and converting;
fifthly, completing the construction of a digital twin body of complex equipment;
step (3), generating fault data, wherein the step is specifically realized as follows:
setting fault behaviors, namely actual fault conditions simulated by a twin body, including conditions of a single structure and a plurality of combined structures, and respectively generating corresponding fault data of different conditions;
simulating production behaviors containing faults according to the selected fault behaviors to generate simulation data;
calculating the deviation value of the set fault behavior according to the data reference value/expected value of the equipment production behavior;
fourthly, converting the deviation value into a characteristic vector to form a characteristic vector group;
step (4), training and verifying a fault prediction model, wherein the step is specifically realized as follows:
setting BP neural network training parameters;
secondly, training a neural network; obtaining a deviation value by training a twin of the equipment and setting corresponding fault behaviors, processing the deviation value, and converting the deviation value into a characteristic vector group, wherein the characteristic vector group is a data source for training a neural network;
using actual data and simulation data to calibrate and verify the neural network, and if the training requirement is not met, modifying the training parameters to continue training until the neural network meeting the fitting requirement is trained;
and fourthly, inputting the data acquired in the actual operation process of the complex equipment into the trained neural network, and outputting the prediction conditions of various faults and fault combinations by the neural network.
2. A digital twin driven complex equipment failure prediction device, comprising:
(1) the complex equipment digital twin establishing module is specifically realized as follows:
firstly, aiming at a complex device, according to the composition structure of the device, MATLAB software is used for establishing a geometric model of the device, and the model needs to reflect the structure and the assembly relation of the device;
adding physical attributes to each structure in the equipment geometric model by using an MATLAB software toolbox;
creating a production behavior according to the characteristics of the complex equipment, wherein the behavior comprises the response of each component of the equipment to the behavior and the characteristics of generating data;
fourthly, setting production behavior constraints according to the specific field of the complex equipment, wherein the specific constraints with multiple dimensions and multiple ranges are required to be set;
according to the structural characteristics of the equipment and the parameters collected in the production process, sorting, classifying and screening the parameters, and establishing a parameter set of the equipment, wherein the parameter set is the parameter set which is collected under various fault conditions and input into a neural network;
(2) the digital twin body calibration module is implemented as follows:
simulating the production process which is the same as the equipment entity according to the established complex equipment digital twin body and the actual production plan of the equipment; the twin body is established for simulating fault data of actual equipment through the twin body, and the twin body needs to be calibrated to be in line with the actual equipment, so that actual production process data is used for calibration;
generating simulation data of each parameter in the production process;
calculating the deviation value of the actual data and the simulated data, judging whether the twin model is matched with the equipment entity according to the deviation value, if so, turning to fifth, and if not, turning to fourth;
fourthly, calculating a gradient according to the deviation value, adjusting parameters of the twin model and converting;
fifthly, completing the construction of a digital twin body of complex equipment;
(3) the fault data generation module is specifically implemented as follows:
setting fault behaviors, namely actual fault conditions simulated by a twin body, including conditions of a single structure and a plurality of combined structures, and respectively generating corresponding fault data of different conditions;
simulating production behaviors containing faults according to the selected fault behaviors to generate simulation data;
calculating the deviation value of the set fault behavior according to the data reference value/expected value of the equipment production behavior;
fourthly, converting the deviation value into a characteristic vector to form a characteristic vector group;
(4) the fault prediction model training and verifying module is specifically realized as follows:
setting BP neural network training parameters;
secondly, training a neural network; obtaining a deviation value by training a twin of the equipment and setting corresponding fault behaviors, processing the deviation value, and converting the deviation value into a characteristic vector group, wherein the characteristic vector group is a data source for training a neural network;
using actual data and simulation data to calibrate and verify the neural network, and if the training requirement is not met, modifying the training parameters to continue training until the neural network meeting the fitting requirement is trained;
and fourthly, inputting the data acquired in the actual operation process of the complex equipment into the trained neural network, and outputting the prediction conditions of various faults and fault combinations by the neural network.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108107841A (en) * | 2017-12-26 | 2018-06-01 | 山东大学 | A kind of twin modeling method of numerically-controlled machine tool number |
| WO2019005185A1 (en) * | 2017-06-28 | 2019-01-03 | General Electric Company | Methods and systems for improving care through post-operation feedback analysis |
| CN110045608A (en) * | 2019-04-02 | 2019-07-23 | 太原理工大学 | Based on the twin mechanical equipment component structural dynamic state of parameters optimization method of number |
| CN110135042A (en) * | 2019-05-08 | 2019-08-16 | 国网江苏省电力有限公司信息通信分公司 | A kind of visualization integrated management method of information machine room |
| CN110276147A (en) * | 2019-06-24 | 2019-09-24 | 广东工业大学 | A manufacturing system fault tracing method and system based on a digital twin model |
| WO2019211288A1 (en) * | 2018-05-02 | 2019-11-07 | Kongsberg Digital AS | A method and system for discovering and visualizing potential operational problems of processes running in equipment and systems in an installation |
| CN110442936A (en) * | 2019-07-24 | 2019-11-12 | 中国石油大学(北京) | Equipment fault diagnosis method, device and system based on digital twin model |
| CN110492186A (en) * | 2019-07-02 | 2019-11-22 | 北京航空航天大学 | A kind of management method of the power battery module based on cloud control technology |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109785180A (en) * | 2019-02-19 | 2019-05-21 | 山东大学 | A scene perception system and method for digital twin workshop |
-
2019
- 2019-11-25 CN CN201911163211.6A patent/CN111008502B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019005185A1 (en) * | 2017-06-28 | 2019-01-03 | General Electric Company | Methods and systems for improving care through post-operation feedback analysis |
| CN108107841A (en) * | 2017-12-26 | 2018-06-01 | 山东大学 | A kind of twin modeling method of numerically-controlled machine tool number |
| WO2019211288A1 (en) * | 2018-05-02 | 2019-11-07 | Kongsberg Digital AS | A method and system for discovering and visualizing potential operational problems of processes running in equipment and systems in an installation |
| CN110045608A (en) * | 2019-04-02 | 2019-07-23 | 太原理工大学 | Based on the twin mechanical equipment component structural dynamic state of parameters optimization method of number |
| CN110135042A (en) * | 2019-05-08 | 2019-08-16 | 国网江苏省电力有限公司信息通信分公司 | A kind of visualization integrated management method of information machine room |
| CN110276147A (en) * | 2019-06-24 | 2019-09-24 | 广东工业大学 | A manufacturing system fault tracing method and system based on a digital twin model |
| CN110492186A (en) * | 2019-07-02 | 2019-11-22 | 北京航空航天大学 | A kind of management method of the power battery module based on cloud control technology |
| CN110442936A (en) * | 2019-07-24 | 2019-11-12 | 中国石油大学(北京) | Equipment fault diagnosis method, device and system based on digital twin model |
Non-Patent Citations (2)
| Title |
|---|
| Digital twin driven prognostics and health management for complex;陶飞等;《CIRP Annals - Manufacturing Technology》;20180524;全文 * |
| Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in;Michael Grieves,John Vickers;《Transdisciplinary Perspectives on Complex Systems》;20160817;全文 * |
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