WO2024087670A1 - Silicone rubber material performance prediction method and apparatus based on lstm neural network - Google Patents
Silicone rubber material performance prediction method and apparatus based on lstm neural network Download PDFInfo
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- WO2024087670A1 WO2024087670A1 PCT/CN2023/101600 CN2023101600W WO2024087670A1 WO 2024087670 A1 WO2024087670 A1 WO 2024087670A1 CN 2023101600 W CN2023101600 W CN 2023101600W WO 2024087670 A1 WO2024087670 A1 WO 2024087670A1
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- 239000004945 silicone rubber Substances 0.000 title claims abstract description 143
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- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
- G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
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- G—PHYSICS
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- G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
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- G16C60/00—Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Definitions
- the present application relates to the technical field of performance prediction of insulating materials for power equipment, and in particular to a method and device for predicting the performance of silicone rubber materials based on an LSTM neural network.
- the service life of dry-type transformers depends largely on the aging characteristics of the transformer's insulation materials, while silicone rubber materials can improve partial discharge defects and have strong heat dissipation capabilities. They also have the advantages of high strength, strong overload and short-circuit resistance, and silicone rubber materials can be recycled to avoid solid waste pollution.
- Liquid silicone rubber is used for vacuum casting or vacuum impregnation of windings. The silicone rubber that penetrates between turns and layers solidifies the winding into an integral structure. The silicone rubber encapsulation layer formed by casting or impregnation on the inner and outer surfaces of the winding can effectively improve the insulation performance of the dry-type transformer.
- the present application provides a method and device for predicting the performance of silicone rubber materials based on an LSTM neural network, so as to realize the prediction of the aging performance of silicone rubber materials and provide a detection basis for the life prediction of silicone rubber materials in dry-type transformers.
- the present application provides a method for predicting the properties of silicone rubber materials based on an LSTM neural network, comprising:
- the dielectric performance data is fused and processed to obtain a comprehensive performance index sequence of silicone rubber materials.
- the comprehensive performance index sequence can characterize the evolution of the electrical properties of silicone rubber materials during the entire aging process.
- the entire aging process includes multiple aging stages.
- the network parameters of the long short-term memory neural network are optimized by discrete differential evolution method until the long short-term memory neural network reaches the preset convergence condition, and the performance prediction model of silicone rubber material is obtained;
- the silicone rubber material performance prediction model is used to predict the target comprehensive performance index data of the silicone rubber material to be predicted at each aging stage according to the initial sequence of the comprehensive performance index of the silicone rubber material to be predicted.
- the dielectric performance data includes volume resistivity, relative dielectric constant and dielectric loss value.
- the dielectric performance data is fused to obtain a comprehensive performance index sequence of the silicone rubber material, including:
- the sample index matrix is constructed by taking volume resistivity, relative dielectric constant at the target frequency point and dielectric loss value as performance indicators;
- the principal component analysis method is used to fuse the dielectric performance data according to the sample index matrix to generate a comprehensive performance index sequence.
- the principal component analysis method is used to fuse the dielectric performance data according to the sample index matrix to generate a comprehensive performance index sequence, including:
- the correlation coefficient matrix between each performance indicator is calculated, and the eigenvalue of the correlation coefficient matrix is calculated;
- the dielectric performance data is fused and processed to obtain a comprehensive performance index sequence.
- Z represents the comprehensive performance index sequence
- a1 is the target feature vector
- R Vol is the standardized sequence of volume resistivity
- Eps i is the standardized sequence of relative dielectric constant at the i-th frequency point
- Tan ⁇ i is the standardized sequence of dielectric loss value at the i-th frequency point.
- the network parameters of the long short-term memory neural network are optimized by discrete differential evolution method according to the comprehensive performance index sequence until the long short-term memory neural network reaches a preset convergence condition, thereby obtaining a silicone rubber material performance prediction model, including:
- the long short-term memory neural network is trained to obtain a trained target long short-term memory neural network
- the network parameters of the target long short-term memory neural network are optimized according to the test set until the target long short-term memory neural network reaches the preset convergence conditions, and the silicone rubber material performance prediction model is obtained.
- the network parameters include the time window length, the number of hidden layer neurons and the number of hidden layer layers.
- the discrete differential evolution method is used to optimize the network parameters of the target long short-term memory neural network according to the test set until the target long short-term memory neural network reaches a preset convergence condition, thereby obtaining a silicone rubber material performance prediction model, including:
- the target LSTM neural network is trained with the initial population, and the individual fitness is calculated by the root mean square error;
- the target LSTM neural network is trained with the latest population, and the latest individual fitness is calculated with the root mean square error;
- the latest population is used as the new initial population to perform the next round of mutation and crossover operations until the termination condition is met and the optimal individual is output;
- the target long short-term memory neural network is updated with the optimal individual to obtain a silicone rubber material performance prediction model.
- a silicone rubber material performance prediction model is used to predict target comprehensive performance index data of the silicone rubber material to be predicted at each aging stage according to an initial sequence of comprehensive performance indexes of the silicone rubber material to be predicted, including:
- the present application provides a silicone rubber material performance prediction device based on an LSTM neural network, comprising:
- An acquisition module used for acquiring dielectric property data of silicone rubber materials at multiple aging stages
- a fusion module is used to fuse the dielectric performance data to obtain a comprehensive performance index sequence of the silicone rubber material.
- the comprehensive performance index sequence can characterize the evolution of the electrical properties of the silicone rubber material during the entire aging process.
- the entire aging process includes multiple aging stages.
- An optimization module is used to optimize the network parameters of the long short-term memory neural network by discrete differential evolution method according to the comprehensive performance index sequence until the long short-term memory neural network reaches a preset convergence condition to obtain a silicone rubber material performance prediction model;
- the prediction module is used to use the silicone rubber material performance prediction model to predict the target comprehensive performance index data of the silicone rubber material to be predicted at each aging stage according to the initial sequence of the comprehensive performance index of the silicone rubber material to be predicted.
- the present application provides a computer device, including a processor and a memory, the memory being used to store a computer program, and when the computer program is executed by the processor, the method for predicting the performance of silicone rubber materials based on an LSTM neural network as in the first aspect is implemented.
- the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the silicone rubber material performance prediction method based on the LSTM neural network as in the first aspect.
- the dielectric performance data are fused and processed.
- a comprehensive performance index sequence of silicone rubber materials is obtained, and an aging model of the long short-term memory neural network is built by using the comprehensive performance index sequence that can characterize the evolution of the electrical properties of the silicone rubber material throughout the aging process, so that the aging model can learn the electrical performance characteristics of the silicone rubber material at each aging stage;
- the network parameters of the long short-term memory neural network are optimized by discrete differential evolution method until the long short-term memory neural network reaches the preset convergence conditions, and a silicone rubber material performance prediction model (aging model) is obtained, which effectively avoids the problems of insufficient fitting and low prediction accuracy caused by selecting key parameters through experience, and improves the model accuracy;
- the silicone rubber material performance prediction model is used to predict the target comprehensive performance index data of the silicone rubber material to be predicted at each aging stage according to the initial sequence of the comprehensive performance index of the silicone rubber material to be predicted, thereby realizing the prediction
- FIG1 is a schematic flow chart of a method for predicting properties of silicone rubber materials based on an LSTM neural network according to an embodiment of the present application
- FIG2 is a schematic diagram of the structure of an LSTM neural network shown in an embodiment of the present application.
- FIG3 is a flow chart of a DDE algorithm according to an embodiment of the present application.
- FIG. 4 is a schematic diagram showing the division result of the training set and the test set according to the comprehensive performance index sequence in the embodiment of the present application.
- FIG5 is a schematic diagram of prediction results of a silicone rubber material to be predicted shown in an embodiment of the present application.
- FIG6 is a schematic diagram of the structure of a silicone rubber material performance prediction device based on an LSTM neural network shown in an embodiment of the present application
- FIG. 7 is a schematic diagram of the structure of a computer device according to an embodiment of the present application.
- Figure 1 is a schematic flow chart of a method for predicting the performance of a silicone rubber material based on an LSTM neural network provided in an embodiment of the present application.
- the method for predicting the performance of a silicone rubber material based on an LSTM neural network in an embodiment of the present application can be applied to computer devices, including but not limited to smart phones, laptops, tablet computers, desktop computers, physical servers, and cloud servers.
- the method for predicting the performance of a silicone rubber material based on an LSTM neural network in this embodiment includes steps S101 to S105, which are described in detail as follows:
- Step S101 obtaining dielectric property data of the silicone rubber material at multiple aging stages.
- the entire aging process includes multiple aging stages.
- the different aging stages of this embodiment can be regarded as the aging process.
- the dielectric properties data include but are not limited to volume resistivity, relative dielectric constant and dielectric loss value.
- the silicone rubber material is processed into a thin sheet sample, the surface of the sample is cleaned, the cleaned sample is placed in a glass container, and placed in a vacuum drying oven for drying, and then a heat aging test is performed on the sample. Part of the sample is taken out at specific intervals and preserved in a vacuum, and the volume resistivity, relative dielectric constant and dielectric loss value of the taken sample at the target frequency are measured.
- the silicone rubber material is processed into a square thin sheet sample with a thickness of 1 mm and a length and width of 40 ⁇ 40 mm2.
- Step S102 fusing the dielectric performance data to obtain a comprehensive performance index sequence of the silicone rubber material, wherein the comprehensive performance index sequence can characterize the evolution of the electrical performance of the silicone rubber material during the entire aging process, and the entire aging process includes multiple aging stages.
- the comprehensive performance index sequence is a sequence obtained by combining the dielectric performance data of multiple aged dielectrics in order of aging time.
- the fusion processing includes data dimensionality reduction and data fusion, and the comprehensive performance index sequence is generated by reducing the dimensionality of the dielectric performance data of multiple aged dielectrics and then fusing the reduced dimensionality dielectric performance data.
- Step S103 according to the comprehensive performance index sequence, the network parameters of the long short-term memory neural network are optimized by discrete differential evolution method until the long short-term memory neural network reaches a preset convergence condition, thereby obtaining a silicone rubber material performance prediction model.
- DDE Discrete Differential Evolution
- the comprehensive performance index sequence is decomposed into a training set and a test set, and a long short-term memory (LSTM) neural network is used for training.
- the prediction error on the test set is used as the target, and the network parameters of the LSTM neural network are optimized using the DDE algorithm.
- the network parameters include the time window length, the number of hidden layer neurons, and the number of hidden layer layers.
- the finally optimized LSTM neural network is used as a silicone rubber material performance prediction model, that is, a silicone rubber material aging model. It should be noted that compared with the current setting of network parameters based on user experience, this embodiment determines the network parameters through the DDE algorithm, which can improve the model fit and model prediction accuracy.
- Step S104 using the silicone rubber material performance prediction model, according to the initial sequence of comprehensive performance indicators of the silicone rubber material to be predicted, predicting target comprehensive performance indicator data of the silicone rubber material to be predicted at each aging stage.
- the dielectric properties of a preset number of samples of the silicone rubber material to be predicted are measured at the current stage.
- the electrical performance data are fused and processed into an initial sequence of comprehensive performance indicators, which are used as the model input of the silicone rubber material performance prediction model to predict the target comprehensive performance indicator data of the silicone rubber material to be predicted in the next aging stage.
- the target comprehensive performance indicator data are used to update the initial sequence of comprehensive performance indicators, and the target comprehensive performance indicator data of the next aging stage are continuously predicted.
- the target comprehensive performance indicator data of the silicone rubber material to be predicted in each aging stage are predicted.
- the step S102 includes:
- the dielectric performance data is fused and processed according to the sample indicator matrix by using the principal component analysis method to generate the comprehensive performance indicator sequence.
- the using of principal component analysis to fuse the dielectric performance data according to the sample indicator matrix to generate the comprehensive performance indicator sequence includes:
- the dielectric performance data is fused according to the target feature vector to obtain the comprehensive performance index sequence.
- sample indicator matrix is standardized:
- the correlation coefficient matrix R ⁇ rij ⁇ between each performance index Xij is calculated, and the eigenvalues ( ⁇ 1 , ⁇ 2 , ..., ⁇ p ) of the correlation coefficient matrix R and the corresponding eigenvectors a1 , ..., ap are calculated.
- Z represents the comprehensive performance index sequence
- a1 is the target eigenvector corresponding to the maximum eigenvalue of the correlation coefficient matrix R
- R Vol is the standardized sequence of volume resistivity
- Eps i is the standardized sequence of relative dielectric constant at the i-th frequency point
- Tan ⁇ i is the standardized sequence of dielectric loss value at the i-th frequency point.
- the step S103 includes:
- training the long short-term memory neural network to obtain a trained target long short-term memory neural network
- the discrete differential evolution method is used to optimize the network parameters of the target long short-term memory neural network according to the test set until the target long short-term memory neural network reaches the preset convergence condition, thereby obtaining the silicone rubber material performance prediction model, wherein the network parameters include the time window length, the number of hidden layer neurons and the number of hidden layer layers.
- the network structure of the LSTM neural network is shown in Figure 2.
- the LSTM network includes four gate structures, each of which is a neural network containing num_layer hidden layers, and each hidden layer contains num_hidden neurons and is fully connected to the input vector [h(t-1), x(t)].
- h(t-1) is the output of the previous moment and is a vector of dimension num_hidden.
- x(t) is the input of the current moment, which is composed of the previous L outputs, and L is the length of the time window.
- the comprehensive performance index sequence is optionally used to form (NL) group samples, 80% of which are used as training sets for network training, and 20% are used as test sets for testing.
- the division result is shown in Figure 4.
- the output of the previous moment is added to the comprehensive performance index sequence, the sliding time window forms new L inputs, and the root mean square error (RMSE) of the neural network is defined as:
- T is the number of samples in the test set
- Zi is the i-th comprehensive performance evaluation index in the test set
- f( xi ) is the predicted value of the LSTM neural network for the i-th sample input xi .
- the target LSTM neural network trained based on the training set is optimized.
- the discrete differential evolution method is used to perform the target long short-term memory neural network according to the test set.
- the network parameters of the network are optimized until the target long short-term memory neural network reaches the preset convergence condition, and the silicone rubber material performance prediction model is obtained, including:
- the target long short-term memory neural network is trained with the initial population, and the individual fitness is calculated with the root mean square error;
- the target long short-term memory neural network is trained with the latest population, and the latest individual fitness is calculated with the root mean square error;
- the latest population is used as the new initial population to perform the next round of mutation and crossover operations until the termination condition is met and the optimal individual is output;
- the target long short-term memory neural network is updated with the optimal individual to obtain the silicone rubber material performance prediction model.
- the network parameters that determine the prediction effect of the LSTM neural network include the number of hidden layers num_layer, the number of hidden layer neurons num_hidden and the time window length L.
- the three network parameters of the LSTM neural network are optimized using the DDE algorithm with RMSE as the optimization target.
- num_hidden, num_layer, L range are set, and the maximum number of iterations t max , the population size N p , the mutation operator F 0 and the crossover operator CR are set;
- V X(1) + floor ⁇ F 0 (X(2) - X(3)) ⁇ ;
- X(1), X(2) and X(3) are three different individuals in population X, and floor means rounding down;
- the crossover operation is: replace the individuals in population X with the individuals in the mutant population V using the crossover operator CR, and process the out-of-bounds individuals in V using the boundary absorption method to obtain the latest population V(i);
- the termination condition is RSME(V(i)) ⁇ preset value, or the number of iterations reaches t max .
- the step S104 includes:
- the initial sequence of comprehensive performance indicators is used as the model input of the silicone rubber material performance prediction model to predict the The target comprehensive performance index data of the silicone rubber material to be predicted at the next aging stage;
- the LSTM neural network predicts the target comprehensive performance index data at the next moment based on the three initial values of the initial sequence Z of the comprehensive performance index, and inserts the target comprehensive performance index data into the last one of the original sequence Z, deletes the first one, keeps the sample size of the original sequence unchanged, realizes the dynamic update of the comprehensive performance index sequence, and estimates the remaining life of the silicone rubber material to be predicted according to the number of iterations experienced when the comprehensive performance index is lower than the threshold Zend.
- FIG. 7 shows a block diagram of a silicone rubber material performance prediction device based on LSTM neural network provided in an embodiment of the present application. For ease of explanation, only the parts related to this embodiment are shown.
- the silicone rubber material performance prediction device based on LSTM neural network provided in an embodiment of the present application includes:
- An acquisition module 601 is used to acquire dielectric property data of the silicone rubber material at multiple aging stages
- a fusion module 602 is used to fuse the dielectric performance data to obtain a comprehensive performance index sequence of the silicone rubber material, wherein the comprehensive performance index sequence can characterize the evolution of the electrical performance of the silicone rubber material during the entire aging process, wherein the entire aging process includes multiple aging stages;
- An optimization module 603 is used to optimize the network parameters of the long short-term memory neural network by discrete differential evolution method according to the comprehensive performance index sequence until the long short-term memory neural network reaches a preset convergence condition to obtain a silicone rubber material performance prediction model;
- the prediction module 604 is used to use the silicone rubber material performance prediction model to predict the target comprehensive performance index data of the silicone rubber material to be predicted at each aging stage according to the initial sequence of comprehensive performance indexes of the silicone rubber material to be predicted.
- the dielectric property data includes volume resistivity, relative dielectric constant and dielectric loss value
- the fusion module 602 includes:
- a construction unit configured to construct a sample index matrix by taking the volume resistivity, the relative dielectric constant at a target frequency point and the dielectric loss value as performance indicators;
- the processing unit is used to use the principal component analysis method to fuse the dielectric performance data according to the sample indicator matrix to generate the comprehensive performance indicator sequence.
- the processing unit is specifically configured to:
- the dielectric performance data is fused to obtain the comprehensive performance index sequence.
- Z represents the comprehensive performance index sequence
- a1 is the target feature vector
- R Vol is the standardized sequence of volume resistivity
- Eps i is the standardized sequence of relative dielectric constant at the i-th frequency point
- Tan ⁇ i is the standardized sequence of dielectric loss value at the i-th frequency point.
- the optimization module 603 includes:
- a division unit used for dividing the comprehensive performance indicator sequence into a training set and a test set
- a training unit used to train the long short-term memory neural network using the training set to obtain a trained target long short-term memory neural network
- An optimization unit is used to optimize the network parameters of the target long short-term memory neural network according to the test set by using the discrete differential evolution method until the target long short-term memory neural network reaches the preset convergence condition, thereby obtaining the silicone rubber material performance prediction model, wherein the network parameters include the time window length, the number of hidden layer neurons and the number of hidden layer layers.
- the optimization unit is specifically used to:
- the target long short-term memory neural network is trained with the initial population, and the individual fitness is calculated with the root mean square error;
- the target long short-term memory neural network is trained with the latest population, and the latest individual fitness is calculated with the root mean square error;
- the latest population is used as the new initial population to perform the next round of mutation and crossover operations until the termination condition is met and the optimal individual is output;
- the target long short-term memory neural network is updated with the optimal individual to obtain the silicone rubber material performance prediction model.
- the prediction module 604 is specifically configured to:
- the above-mentioned silicone rubber material performance prediction device based on LSTM neural network can implement the silicone rubber material performance prediction method based on LSTM neural network of the above-mentioned method embodiment.
- the options in the above-mentioned method embodiment are also applicable to this embodiment and will not be described in detail here.
- the rest of the contents of the embodiment of this application can refer to the contents of the above-mentioned method embodiment, and will not be repeated in this embodiment.
- FIG7 is a schematic diagram of the structure of a computer device provided in an embodiment of the present application.
- the computer device 7 of this embodiment includes: at least one processor 70 (only one is shown in FIG7 ), a memory 71, and a computer program 72 stored in the memory 71 and executable on the at least one processor 70, and when the processor 70 executes the computer program 72, the steps in any of the above method embodiments are implemented.
- the computer device 7 may be a computing device such as a smart phone, a tablet computer, a desktop computer, and a cloud server.
- the computer device may include, but is not limited to, a processor 70 and a memory 71.
- FIG. 7 is merely an example of the computer device 7 and does not constitute a limitation on the computer device 7.
- the computer device 7 may include more or fewer components than shown in the figure, or may combine certain components, or different components, and may also include, for example, input and output devices, network access devices, etc.
- the processor 70 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
- CPU central processing unit
- DSP digital signal processors
- ASIC application-specific integrated circuits
- FPGA field-programmable gate arrays
- a general-purpose processor may be a microprocessor or any conventional processor, etc.
- the memory 71 may be an internal storage unit of the computer device 7, such as a hard disk or memory of the computer device 7. In other embodiments, the memory 71 may also be an external storage device of the computer device 7, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 7. It may also include both the internal storage unit and the external storage device of the computer device 7.
- the memory 71 is used to store an operating system, an application program, a boot loader, data, and other programs, such as the program code of the computer program, etc.
- the memory 71 may also be used to temporarily store data that has been output or is to be output.
- an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.
- An embodiment of the present application provides a computer program product.
- the computer program product When the computer program product is run on a computer device, the computer device implements the steps in the above-mentioned method embodiments when executing the computer device.
- each box in the flow chart or block diagram can represent a module, a program segment or a part of a code
- the module, a program segment or a part of a code contains one or more executable instructions for realizing the specified logical function.
- the functions marked in the box can also occur in a different order from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.
- the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium.
- the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art.
- the computer software product is stored in a storage medium and includes several instructions for a computer device to execute all or part of the steps of the methods described in the various embodiments of the present application.
- the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.
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Abstract
Disclosed in the present application are a silicone rubber material performance prediction method and apparatus based on a long short-term memory (LSTM) neural network. Dielectric performance data of a silicone rubber material in a plurality of aging stages are acquired, and fusion processing is performed on the dielectric performance data to obtain a comprehensive performance index sequence of the silicone rubber material; according to the comprehensive performance index sequence, network parameters of the LSTM neural network are optimized by using a discrete differential evolution method until the LSTM neural network reaches a preset convergence condition to obtain a silicone rubber material performance prediction model, thereby effectively avoiding the problems of insufficient fitting degree and low precision caused by selecting key parameters according to experience; and according to a comprehensive performance index initial sequence of a silicone rubber material to be predicted, target comprehensive performance index data of the silicone rubber material to be predicted in each aging stage is predicted by using the silicone rubber material performance prediction model, thereby implementing prediction of aging performance of the silicone rubber material, and providing a detection basis for life prediction of the silicone rubber material in a dry-type transformer.
Description
本申请涉及电力设备绝缘材料性能预测技术领域,尤其涉及一种基于LSTM神经网络的硅橡胶材料性能预测方法及装置。The present application relates to the technical field of performance prediction of insulating materials for power equipment, and in particular to a method and device for predicting the performance of silicone rubber materials based on an LSTM neural network.
干式变压器的使用寿命很大程度上取决于变压器的绝缘材料的老化特性,而硅橡胶材料能够改善局放缺陷,而且散热能力强,同时具备强度高、过载及抗短路能力强等优点,并且硅橡胶材料可回收利用,避免固体废物的污染。采用液态硅橡胶真空浇注或真空浸渍绕组,匝间和层间渗透的硅橡胶将绕组固化为整体结构,绕组内外表面浇注或浸渍形成的硅橡胶包封层能够有效提升干式变压器的绝缘性能。The service life of dry-type transformers depends largely on the aging characteristics of the transformer's insulation materials, while silicone rubber materials can improve partial discharge defects and have strong heat dissipation capabilities. They also have the advantages of high strength, strong overload and short-circuit resistance, and silicone rubber materials can be recycled to avoid solid waste pollution. Liquid silicone rubber is used for vacuum casting or vacuum impregnation of windings. The silicone rubber that penetrates between turns and layers solidifies the winding into an integral structure. The silicone rubber encapsulation layer formed by casting or impregnation on the inner and outer surfaces of the winding can effectively improve the insulation performance of the dry-type transformer.
但是,当前在干式变压器绕组固体绝缘工况下,硅橡胶材料的老化建模与寿命预测的研究相对较少。因此,亟需一种针对干式变压器绕组固体绝缘工况下的硅橡胶材料性能预测方法。However, there is relatively little research on aging modeling and life prediction of silicone rubber materials under the condition of solid insulation of dry-type transformer windings. Therefore, a performance prediction method for silicone rubber materials under the condition of solid insulation of dry-type transformer windings is urgently needed.
发明内容Summary of the invention
本申请提供了一种基于LSTM神经网络的硅橡胶材料性能预测方法及装置,以实现对硅橡胶材料老化性能的预测,为干式变压器中硅橡胶材料的寿命预测提供检测依据。The present application provides a method and device for predicting the performance of silicone rubber materials based on an LSTM neural network, so as to realize the prediction of the aging performance of silicone rubber materials and provide a detection basis for the life prediction of silicone rubber materials in dry-type transformers.
为了解决上述技术问题,第一方面,本申请提供了一种基于LSTM神经网络的硅橡胶材料性能预测方法,包括:In order to solve the above technical problems, in the first aspect, the present application provides a method for predicting the properties of silicone rubber materials based on an LSTM neural network, comprising:
获取硅橡胶材料在多个老化阶段下的介电性能数据;Obtain dielectric properties data of silicone rubber materials at multiple aging stages;
对介电性能数据进行融合处理,得到硅橡胶材料的综合性能指标序列,综合性能指标序列能够表征硅橡胶材料在整个老化过程的电性能演变情况,整个老化过程包括多个老化阶段;The dielectric performance data is fused and processed to obtain a comprehensive performance index sequence of silicone rubber materials. The comprehensive performance index sequence can characterize the evolution of the electrical properties of silicone rubber materials during the entire aging process. The entire aging process includes multiple aging stages.
根据综合性能指标序列,以离散差分进化法对长短时记忆神经网络的网络参数进行优化,直至长短时记忆神经网络达到预设收敛条件,得到硅橡胶材料性能预测模型;According to the comprehensive performance index sequence, the network parameters of the long short-term memory neural network are optimized by discrete differential evolution method until the long short-term memory neural network reaches the preset convergence condition, and the performance prediction model of silicone rubber material is obtained;
利用硅橡胶材料性能预测模型,根据待预测硅橡胶材料的综合性能指标初始序列,预测待预测硅橡胶材料在各个老化阶段的目标综合性能指标数据。The silicone rubber material performance prediction model is used to predict the target comprehensive performance index data of the silicone rubber material to be predicted at each aging stage according to the initial sequence of the comprehensive performance index of the silicone rubber material to be predicted.
在一些实现方式中,介电性能数据包括体积电阻率、相对介电常数和介电损耗值,对介电性能数据进行融合处理,得到硅橡胶材料的综合性能指标序列,包括:
In some implementations, the dielectric performance data includes volume resistivity, relative dielectric constant and dielectric loss value. The dielectric performance data is fused to obtain a comprehensive performance index sequence of the silicone rubber material, including:
以体积电阻率、目标频点上的相对介电常数和介电损耗值为性能指标,构建样本指标矩阵;The sample index matrix is constructed by taking volume resistivity, relative dielectric constant at the target frequency point and dielectric loss value as performance indicators;
利用主成分分析法,根据样本指标矩阵,对介电性能数据进行融合处理,生成综合性能指标序列。The principal component analysis method is used to fuse the dielectric performance data according to the sample index matrix to generate a comprehensive performance index sequence.
在一些实现方式中,利用主成分分析法,根据样本指标矩阵,对介电性能数据进行融合处理,生成综合性能指标序列,包括:In some implementations, the principal component analysis method is used to fuse the dielectric performance data according to the sample index matrix to generate a comprehensive performance index sequence, including:
利用主成分分析法,根据样本指标矩阵,计算各个性能指标间的相关系数矩阵,以及计算相关系数矩阵的特征值;Using principal component analysis, according to the sample indicator matrix, the correlation coefficient matrix between each performance indicator is calculated, and the eigenvalue of the correlation coefficient matrix is calculated;
确定满足在成分贡献率条件时的目标特征值对应的目标特征向量;Determine the target eigenvector corresponding to the target eigenvalue when the component contribution rate condition is satisfied;
根据目标特征向量,对介电性能数据进行融合处理,得到综合性能指标序列。According to the target feature vector, the dielectric performance data is fused and processed to obtain a comprehensive performance index sequence.
在一些实现方式中,综合性能指标序列为:
a1=[aR,aei,ati]T;In some implementations, the comprehensive performance indicator sequence is:
a 1 = [a R , a ei , a ti ] T ;
a1=[aR,aei,ati]T;In some implementations, the comprehensive performance indicator sequence is:
a 1 = [a R , a ei , a ti ] T ;
其中,Z表示综合性能指标序列,a1为目标特征向量,RVol为体积电阻率的标准化序列,Epsi为第i个频点上的相对介电常数的标准化序列,Tanδi为第i个频点上的介电损耗值的标准化序列。Among them, Z represents the comprehensive performance index sequence, a1 is the target feature vector, R Vol is the standardized sequence of volume resistivity, Eps i is the standardized sequence of relative dielectric constant at the i-th frequency point, and Tanδ i is the standardized sequence of dielectric loss value at the i-th frequency point.
在一些实现方式中,根据综合性能指标序列,以离散差分进化法对长短时记忆神经网络的网络参数进行优化,直至长短时记忆神经网络达到预设收敛条件,得到硅橡胶材料性能预测模型,包括:In some implementations, the network parameters of the long short-term memory neural network are optimized by discrete differential evolution method according to the comprehensive performance index sequence until the long short-term memory neural network reaches a preset convergence condition, thereby obtaining a silicone rubber material performance prediction model, including:
将综合性能指标序列分为训练集和测试集;Divide the comprehensive performance indicator sequence into a training set and a test set;
利用训练集,对长短时记忆神经网络进行训练,得到训练后的目标长短时记忆神经网络;Using the training set, the long short-term memory neural network is trained to obtain a trained target long short-term memory neural network;
利用离散差分进化法,根据测试集,对目标长短时记忆神经网络的网络参数进行优化,直至目标长短时记忆神经网络的达到预设收敛条件,得到硅橡胶材料性能预测模型,网络参数包括时间窗口长度、隐含层神经元个数和隐含层层数。Using the discrete differential evolution method, the network parameters of the target long short-term memory neural network are optimized according to the test set until the target long short-term memory neural network reaches the preset convergence conditions, and the silicone rubber material performance prediction model is obtained. The network parameters include the time window length, the number of hidden layer neurons and the number of hidden layer layers.
在一些实现方式中,利用离散差分进化法,根据测试集,对目标长短时记忆神经网络的网络参数进行优化,直至目标长短时记忆神经网络的达到预设收敛条件,得到硅橡胶材料性能预测模型,包括:In some implementations, the discrete differential evolution method is used to optimize the network parameters of the target long short-term memory neural network according to the test set until the target long short-term memory neural network reaches a preset convergence condition, thereby obtaining a silicone rubber material performance prediction model, including:
基于时间窗口长度、隐含层神经元个数和隐含层层数,生成初始种群;Generate the initial population based on the time window length, the number of hidden layer neurons and the number of hidden layer layers;
根据测试集,以初始种群训练目标长短时记忆神经网络,并以均方根误差计算个体适应度;According to the test set, the target LSTM neural network is trained with the initial population, and the individual fitness is calculated by the root mean square error;
对初始种群进行变异操作和交叉操作,得到最新种群;
Perform mutation and crossover operations on the initial population to obtain the latest population;
根据测试集,以最新种群训练目标长短时记忆神经网络,并以均方根误差计算最新个体适应度;According to the test set, the target LSTM neural network is trained with the latest population, and the latest individual fitness is calculated with the root mean square error;
若最新个体适应度不大于个体适应度,则以所述最新种群作为新的初始种群进行下一轮变异操作和交叉操作,直至满足终止条件,输出最优个体;If the latest individual fitness is not greater than the individual fitness, the latest population is used as the new initial population to perform the next round of mutation and crossover operations until the termination condition is met and the optimal individual is output;
以所述最优个体更新目标长短时记忆神经网络,得到硅橡胶材料性能预测模型。The target long short-term memory neural network is updated with the optimal individual to obtain a silicone rubber material performance prediction model.
在一些实现方式中,利用硅橡胶材料性能预测模型,根据待预测硅橡胶材料的综合性能指标初始序列,预测待预测硅橡胶材料在各个老化阶段的目标综合性能指标数据,包括:In some implementations, a silicone rubber material performance prediction model is used to predict target comprehensive performance index data of the silicone rubber material to be predicted at each aging stage according to an initial sequence of comprehensive performance indexes of the silicone rubber material to be predicted, including:
以综合性能指标初始序列作为硅橡胶材料性能预测模型的模型输入,预测待预测硅橡胶材料在下一个老化阶段时的目标综合性能指标数据;Taking the initial sequence of comprehensive performance indicators as the model input of the silicone rubber material performance prediction model, predicting the target comprehensive performance indicator data of the silicone rubber material to be predicted at the next aging stage;
将目标综合性能指标数据插入到综合性能指标初始序列,得到新的综合性能指标初始序列;Inserting the target comprehensive performance index data into the initial sequence of comprehensive performance indicators to obtain a new initial sequence of comprehensive performance indicators;
以新的综合性能指标初始序列,继续预测下一个老化阶段的目标综合性能指标数据,直至目标综合性能指标数据低于预设阈值,并根据目标综合性能指标数据低于预设阈值时所经历的迭代次数,预测待预测硅橡胶材料的剩余寿命。Using the new initial sequence of comprehensive performance indicators, continue to predict the target comprehensive performance indicator data of the next aging stage until the target comprehensive performance indicator data is lower than the preset threshold, and predict the remaining life of the silicone rubber material to be predicted based on the number of iterations experienced when the target comprehensive performance indicator data is lower than the preset threshold.
第二方面,本申请提供一种基于LSTM神经网络的硅橡胶材料性能预测装置,包括:In a second aspect, the present application provides a silicone rubber material performance prediction device based on an LSTM neural network, comprising:
获取模块,用于获取硅橡胶材料在多个老化阶段下的介电性能数据;An acquisition module, used for acquiring dielectric property data of silicone rubber materials at multiple aging stages;
融合模块,用于对介电性能数据进行融合处理,得到硅橡胶材料的综合性能指标序列,综合性能指标序列能够表征硅橡胶材料在整个老化过程的电性能演变情况,整个老化过程包括多个老化阶段;A fusion module is used to fuse the dielectric performance data to obtain a comprehensive performance index sequence of the silicone rubber material. The comprehensive performance index sequence can characterize the evolution of the electrical properties of the silicone rubber material during the entire aging process. The entire aging process includes multiple aging stages.
优化模块,用于根据综合性能指标序列,以离散差分进化法对长短时记忆神经网络的网络参数进行优化,直至长短时记忆神经网络达到预设收敛条件,得到硅橡胶材料性能预测模型;An optimization module is used to optimize the network parameters of the long short-term memory neural network by discrete differential evolution method according to the comprehensive performance index sequence until the long short-term memory neural network reaches a preset convergence condition to obtain a silicone rubber material performance prediction model;
预测模块,用于利用硅橡胶材料性能预测模型,根据待预测硅橡胶材料的综合性能指标初始序列,预测待预测硅橡胶材料在各个老化阶段的目标综合性能指标数据。The prediction module is used to use the silicone rubber material performance prediction model to predict the target comprehensive performance index data of the silicone rubber material to be predicted at each aging stage according to the initial sequence of the comprehensive performance index of the silicone rubber material to be predicted.
第三方面,本申请提供一种计算机设备,包括处理器和存储器,存储器用于存储计算机程序,计算机程序被处理器执行时实现如第一方面的基于LSTM神经网络的硅橡胶材料性能预测方法。In a third aspect, the present application provides a computer device, including a processor and a memory, the memory being used to store a computer program, and when the computer program is executed by the processor, the method for predicting the performance of silicone rubber materials based on an LSTM neural network as in the first aspect is implemented.
第四方面,本申请提供一种计算机可读存储介质,其存储有计算机程序,计算机程序被处理器执行时实现如第一方面的基于LSTM神经网络的硅橡胶材料性能预测方法。In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the silicone rubber material performance prediction method based on the LSTM neural network as in the first aspect.
与现有技术相比,本申请至少具备以下有益效果:Compared with the prior art, this application has at least the following beneficial effects:
通过获取硅橡胶材料在多个老化阶段下的介电性能数据,对介电性能数据进行融合处理,
得到硅橡胶材料的综合性能指标序列,以能够采用表征硅橡胶材料在整个老化过程的电性能演变情况的综合性能指标序列对长短时记忆神经网络进行老化模型建模,从而使老化模型学习到硅橡胶材料在各个老化阶段的电性能特征;根据综合性能指标序列,以离散差分进化法对长短时记忆神经网络的网络参数进行优化,直至长短时记忆神经网络达到预设收敛条件,得到硅橡胶材料性能预测模型(老化模型),有效规避通过经验选取关键参数而导致拟合度不够和预测精度低的问题,提高模型精度;最后利用硅橡胶材料性能预测模型,根据待预测硅橡胶材料的综合性能指标初始序列,预测待预测硅橡胶材料在各个老化阶段的目标综合性能指标数据,从而实现对硅橡胶材料老化性能的预测,为干式变压器中硅橡胶材料的寿命预测提供检测依据。By obtaining the dielectric performance data of silicone rubber materials at multiple aging stages, the dielectric performance data are fused and processed. A comprehensive performance index sequence of silicone rubber materials is obtained, and an aging model of the long short-term memory neural network is built by using the comprehensive performance index sequence that can characterize the evolution of the electrical properties of the silicone rubber material throughout the aging process, so that the aging model can learn the electrical performance characteristics of the silicone rubber material at each aging stage; according to the comprehensive performance index sequence, the network parameters of the long short-term memory neural network are optimized by discrete differential evolution method until the long short-term memory neural network reaches the preset convergence conditions, and a silicone rubber material performance prediction model (aging model) is obtained, which effectively avoids the problems of insufficient fitting and low prediction accuracy caused by selecting key parameters through experience, and improves the model accuracy; finally, the silicone rubber material performance prediction model is used to predict the target comprehensive performance index data of the silicone rubber material to be predicted at each aging stage according to the initial sequence of the comprehensive performance index of the silicone rubber material to be predicted, thereby realizing the prediction of the aging performance of the silicone rubber material and providing a detection basis for the life prediction of the silicone rubber material in the dry-type transformer.
图1为本申请实施例示出的基于LSTM神经网络的硅橡胶材料性能预测方法的流程示意图;FIG1 is a schematic flow chart of a method for predicting properties of silicone rubber materials based on an LSTM neural network according to an embodiment of the present application;
图2为本申请实施例示出的LSTM神经网络的结构示意图;FIG2 is a schematic diagram of the structure of an LSTM neural network shown in an embodiment of the present application;
图3为本申请实施例示出的DDE算法的流程示意图;FIG3 is a flow chart of a DDE algorithm according to an embodiment of the present application;
图4为本申请实施例示出的以综合性能指标序列划分训练集和测试集的划分结果示意图FIG. 4 is a schematic diagram showing the division result of the training set and the test set according to the comprehensive performance index sequence in the embodiment of the present application.
图5为本申请实施例示出的待预测硅橡胶材料的预测结果示意图;FIG5 is a schematic diagram of prediction results of a silicone rubber material to be predicted shown in an embodiment of the present application;
图6为本申请实施例示出的基于LSTM神经网络的硅橡胶材料性能预测装置的结构示意图FIG6 is a schematic diagram of the structure of a silicone rubber material performance prediction device based on an LSTM neural network shown in an embodiment of the present application
图7为本申请实施例示出的计算机设备的结构示意图。FIG. 7 is a schematic diagram of the structure of a computer device according to an embodiment of the present application.
下面将结合本申请实施例中的附图,对本申请实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本申请一部分实施例,而不是全部的实施例。基于本申请中的实施例,本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例,都属于本申请保护的范围。The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
请参照图1,图1为本申请实施例提供的一种基于LSTM神经网络的硅橡胶材料性能预测方法的流程示意图。本申请实施例的基于LSTM神经网络的硅橡胶材料性能预测方法可应用于计算机设备,该计算机设备包括但不限于智能手机、笔记本电脑、平板电脑、桌上型计算机、物理服务器和云服务器等设备。如图1所示,本实施例的基于LSTM神经网络的硅橡胶材料性能预测方法包括步骤S101至步骤S105,详述如下:Please refer to Figure 1, which is a schematic flow chart of a method for predicting the performance of a silicone rubber material based on an LSTM neural network provided in an embodiment of the present application. The method for predicting the performance of a silicone rubber material based on an LSTM neural network in an embodiment of the present application can be applied to computer devices, including but not limited to smart phones, laptops, tablet computers, desktop computers, physical servers, and cloud servers. As shown in Figure 1, the method for predicting the performance of a silicone rubber material based on an LSTM neural network in this embodiment includes steps S101 to S105, which are described in detail as follows:
步骤S101,获取硅橡胶材料在多个老化阶段下的介电性能数据。Step S101, obtaining dielectric property data of the silicone rubber material at multiple aging stages.
在本步骤中,整个老化过程包括多个老化阶段,本实施例的不同老化阶段可视为老化过
程的不同时刻。介电性能数据包括但不限于体积电阻率、相对介电常数和介电损耗值。In this step, the entire aging process includes multiple aging stages. The different aging stages of this embodiment can be regarded as the aging process. The dielectric properties data include but are not limited to volume resistivity, relative dielectric constant and dielectric loss value.
可选地,将硅橡胶材料加工为薄片试样,对试样表面进行清晰,将清洗后的试样装入玻璃容器中,并放入真空干燥箱中干燥,再对试样进行热老化试验,间隔特定时间取出部分试样且真空保存,测量取出的试样的体积电阻率、目标频点上的相对介电常数和介电损耗值。Optionally, the silicone rubber material is processed into a thin sheet sample, the surface of the sample is cleaned, the cleaned sample is placed in a glass container, and placed in a vacuum drying oven for drying, and then a heat aging test is performed on the sample. Part of the sample is taken out at specific intervals and preserved in a vacuum, and the volume resistivity, relative dielectric constant and dielectric loss value of the taken sample at the target frequency are measured.
示例性地,将硅橡胶材料加工为厚度为1mm、长宽为40×40mm2的正方形薄片试样,采用无水乙醇清洗试样表面,将清洗后的试样装入玻璃容器中,并将玻璃容器放入120℃、50Pa的真空干燥箱中干燥2h;再对干燥后的试样进行热老化试验:环境温度设定为250℃,设定最长老化时间为125天,每隔12h取出部分试样且真空保存,共计n=250组不同老化状态下的试检数据,试验数据包括体积电阻率、10-1Hz至106Hz频段内目标频点上的相对介电常数和介电损耗值。Exemplarily, the silicone rubber material is processed into a square thin sheet sample with a thickness of 1 mm and a length and width of 40× 40 mm2. The surface of the sample is cleaned with anhydrous ethanol, the cleaned sample is placed in a glass container, and the glass container is placed in a vacuum drying oven at 120°C and 50 Pa for drying for 2 hours; then the dried sample is subjected to a thermal aging test: the ambient temperature is set to 250°C, the maximum aging time is set to 125 days, part of the sample is taken out every 12 hours and preserved in a vacuum, a total of n=250 groups of test data under different aging conditions, the test data include volume resistivity, relative dielectric constant and dielectric loss value at the target frequency point in the frequency band of 10-1 Hz to 106 Hz.
步骤S102,对所述介电性能数据进行融合处理,得到所述硅橡胶材料的综合性能指标序列,所述综合性能指标序列能够表征所述硅橡胶材料在整个老化过程的电性能演变情况,所述整个老化过程包括多个老化阶段。Step S102, fusing the dielectric performance data to obtain a comprehensive performance index sequence of the silicone rubber material, wherein the comprehensive performance index sequence can characterize the evolution of the electrical performance of the silicone rubber material during the entire aging process, and the entire aging process includes multiple aging stages.
在本步骤中,综合性能指标序列为以老化时间为顺序对多个老化介电的介电性能数据进行组合得到的序列。可选地,融合处理包括数据降维和数据融合,通过对多个老化介电的介电性能数据进行降维,再对降维后的介电性能数据进行融合,生成综合性能指标序列。In this step, the comprehensive performance index sequence is a sequence obtained by combining the dielectric performance data of multiple aged dielectrics in order of aging time. Optionally, the fusion processing includes data dimensionality reduction and data fusion, and the comprehensive performance index sequence is generated by reducing the dimensionality of the dielectric performance data of multiple aged dielectrics and then fusing the reduced dimensionality dielectric performance data.
步骤S103,根据所述综合性能指标序列,以离散差分进化法对长短时记忆神经网络的网络参数进行优化,直至所述长短时记忆神经网络达到预设收敛条件,得到硅橡胶材料性能预测模型。Step S103, according to the comprehensive performance index sequence, the network parameters of the long short-term memory neural network are optimized by discrete differential evolution method until the long short-term memory neural network reaches a preset convergence condition, thereby obtaining a silicone rubber material performance prediction model.
在本步骤中,离散差分进化法(Discrete Differential Evolution,DDE)是在标准差分进化法(Differential Evolution,DE)的优化流程中取整处理后构建的算法,其具备DE算法受控参数少、鲁棒性强和收敛速度快的特点。In this step, Discrete Differential Evolution (DDE) is an algorithm constructed after rounding off the optimization process of Standard Differential Evolution (DE). It has the characteristics of DE algorithm with few controlled parameters, strong robustness and fast convergence speed.
可选地,将综合性能指标序列分解为训练集和测试集,利用长短时记忆(Long Short-Term Memory,LSTM)神经网络进行训练,以测试集上的预测误差为目标,采用DDE算法对LSTM神经网络的网络参数进行优化,网络参数包括时间窗口长度、隐含层神经元个数和隐含层层数,将最终优化后的LSTM神经网络作为硅橡胶材料性能预测模型,即硅橡胶材料老化模型。需要说明的是,相比于当前根据用户经验设定网络参数,本实施例通过DDE算法确定网络参数,能够提高模型拟合度和模型预测精度。Optionally, the comprehensive performance index sequence is decomposed into a training set and a test set, and a long short-term memory (LSTM) neural network is used for training. The prediction error on the test set is used as the target, and the network parameters of the LSTM neural network are optimized using the DDE algorithm. The network parameters include the time window length, the number of hidden layer neurons, and the number of hidden layer layers. The finally optimized LSTM neural network is used as a silicone rubber material performance prediction model, that is, a silicone rubber material aging model. It should be noted that compared with the current setting of network parameters based on user experience, this embodiment determines the network parameters through the DDE algorithm, which can improve the model fit and model prediction accuracy.
步骤S104,利用所述硅橡胶材料性能预测模型,根据待预测硅橡胶材料的综合性能指标初始序列,预测所述待预测硅橡胶材料在各个老化阶段的目标综合性能指标数据。Step S104, using the silicone rubber material performance prediction model, according to the initial sequence of comprehensive performance indicators of the silicone rubber material to be predicted, predicting target comprehensive performance indicator data of the silicone rubber material to be predicted at each aging stage.
在本步骤中,测量待预测硅橡胶材料的预设数量试样在当前阶段的介电性能数据,将介
电性能数据融合处理为综合性能指标初始序列,将综合性能指标初始序列作为硅橡胶材料性能预测模型的模型输入,预测待预测硅橡胶材料在下一老化阶段的目标综合性能指标数据,利用目标综合性能指标数据更新综合性能指标初始序列,继续预测再下一老化阶段的目标综合性能指标数据,依次类推,预测得到待预测硅橡胶材料在各个老化阶段的目标综合性能指标数据。In this step, the dielectric properties of a preset number of samples of the silicone rubber material to be predicted are measured at the current stage. The electrical performance data are fused and processed into an initial sequence of comprehensive performance indicators, which are used as the model input of the silicone rubber material performance prediction model to predict the target comprehensive performance indicator data of the silicone rubber material to be predicted in the next aging stage. The target comprehensive performance indicator data are used to update the initial sequence of comprehensive performance indicators, and the target comprehensive performance indicator data of the next aging stage are continuously predicted. By analogy, the target comprehensive performance indicator data of the silicone rubber material to be predicted in each aging stage are predicted.
在一些实施例中,所述步骤S102,包括:In some embodiments, the step S102 includes:
以所述体积电阻率、目标频点上的所述相对介电常数和所述介电损耗值为性能指标,构建样本指标矩阵;Taking the volume resistivity, the relative dielectric constant at the target frequency point and the dielectric loss value as performance indicators, a sample indicator matrix is constructed;
利用主成分分析法,根据所述样本指标矩阵,对所述介电性能数据进行融合处理,生成所述综合性能指标序列。The dielectric performance data is fused and processed according to the sample indicator matrix by using the principal component analysis method to generate the comprehensive performance indicator sequence.
在本实施例中,对于第i次取样的硅橡胶材料,测定其体积电阻率RVol,目标频点上的相对介电常数Eps和介电损耗值Tanδ,共p个性能指标,则构建样本指标矩阵x={xij},i=1,2,…,p,j=1,2,…,p。再利用主成分分析法,根据该样本指标矩阵,对介电性能数据进行降维和融合,生成综合性能指标序列。In this embodiment, for the silicone rubber material sampled for the i-th time, its volume resistivity R Vol , relative dielectric constant Eps at the target frequency point and dielectric loss value Tanδ are measured, with a total of p performance indicators, and a sample indicator matrix x = {x ij }, i = 1, 2, ..., p, j = 1, 2, ..., p is constructed. Then, the principal component analysis method is used to reduce the dimension and fuse the dielectric performance data according to the sample indicator matrix to generate a comprehensive performance indicator sequence.
可选地,所述利用主成分分析法,根据所述样本指标矩阵,对所述介电性能数据进行融合处理,生成所述综合性能指标序列,包括:Optionally, the using of principal component analysis to fuse the dielectric performance data according to the sample indicator matrix to generate the comprehensive performance indicator sequence includes:
利用主成分分析法,根据所述样本指标矩阵,计算各个性能指标间的相关系数矩阵,以及计算所述相关系数矩阵的特征值;Using principal component analysis, according to the sample indicator matrix, calculate the correlation coefficient matrix between the various performance indicators, and calculate the eigenvalues of the correlation coefficient matrix;
确定满足在成分贡献率条件时的目标特征值对应的目标特征向量;Determine the target eigenvector corresponding to the target eigenvalue when the component contribution rate condition is satisfied;
根据所述目标特征向量,对所述介电性能数据进行融合处理,得到所述综合性能指标序列。The dielectric performance data is fused according to the target feature vector to obtain the comprehensive performance index sequence.
在本可选实施例中,示例性地,对样本指标矩阵进行标准化处理:
In this optional embodiment, illustratively, the sample indicator matrix is standardized:
In this optional embodiment, illustratively, the sample indicator matrix is standardized:
其中,为样本指标矩阵中列数据的均值,Sj为样本指标矩阵中列数据的标准差;in, is the mean of the column data in the sample indicator matrix, Sj is the standard deviation of the column data in the sample indicator matrix;
根据标准化后的样本指标矩阵X,计算各个性能指标Xij之间的相关系数矩阵R={rij},并计算相关系数矩阵R的特征值(λ1,λ2,…,λp)和对应的特征向量a1,…,ap。According to the standardized sample index matrix X, the correlation coefficient matrix R = { rij } between each performance index Xij is calculated, and the eigenvalues ( λ1 , λ2 , ..., λp ) of the correlation coefficient matrix R and the corresponding eigenvectors a1 , ..., ap are calculated.
计算第m个成分序列Fm=Xam,m=1,2,…,p,以及该成分序列的贡献率bm:
Calculate the mth component sequence F m = Xam , m=1,2,…,p, and the contribution rate bm of the component sequence:
Calculate the mth component sequence F m = Xam , m=1,2,…,p, and the contribution rate bm of the component sequence:
成分贡献率条件为若最大值b1>0.85,则确定第一主成分序列F1基本保留了原性能指标的主要信息,以序列Z=F1作为综合性能指标序列。The component contribution rate condition is that if the maximum value b 1 >0.85, it is determined that the first principal component sequence F 1 basically retains the main information of the original performance index, and the sequence Z=F 1 is used as the comprehensive performance index sequence.
可选地,所述综合性能指标序列为:
a1=[aR,aei,ati]T;Optionally, the comprehensive performance indicator sequence is:
a 1 = [a R , a ei , a ti ] T ;
a1=[aR,aei,ati]T;Optionally, the comprehensive performance indicator sequence is:
a 1 = [a R , a ei , a ti ] T ;
其中,Z表示综合性能指标序列,a1为相关系数矩阵R的最大特征值对应的目标特征向量,RVol为体积电阻率的标准化序列,Epsi为第i个频点上的相对介电常数的标准化序列,Tanδi为第i个频点上的介电损耗值的标准化序列。Among them, Z represents the comprehensive performance index sequence, a1 is the target eigenvector corresponding to the maximum eigenvalue of the correlation coefficient matrix R, R Vol is the standardized sequence of volume resistivity, Eps i is the standardized sequence of relative dielectric constant at the i-th frequency point, and Tanδ i is the standardized sequence of dielectric loss value at the i-th frequency point.
在一些实施例中,所述步骤S103,包括:In some embodiments, the step S103 includes:
将所述综合性能指标序列分为训练集和测试集;Dividing the comprehensive performance indicator sequence into a training set and a test set;
利用所述训练集,对所述长短时记忆神经网络进行训练,得到训练后的目标长短时记忆神经网络;Using the training set, training the long short-term memory neural network to obtain a trained target long short-term memory neural network;
利用所述离散差分进化法,根据所述测试集,对所述目标长短时记忆神经网络的网络参数进行优化,直至所述目标长短时记忆神经网络的达到所述预设收敛条件,得到所述硅橡胶材料性能预测模型,所述网络参数包括时间窗口长度、隐含层神经元个数和隐含层层数。The discrete differential evolution method is used to optimize the network parameters of the target long short-term memory neural network according to the test set until the target long short-term memory neural network reaches the preset convergence condition, thereby obtaining the silicone rubber material performance prediction model, wherein the network parameters include the time window length, the number of hidden layer neurons and the number of hidden layer layers.
示例性地,LSTM神经网络的网络结构如图2所示,LSTM网络包括四个门结构,每个门结构均为包含num_layer层隐含层,且每层隐含层含由num_hidden个神经元并且与输入向量[h(t-1),x(t)]进行全连接的神经网络,h(t-1)是上一时刻的输出,是一个维度为num_hidden的向量,x(t)是当前时刻的输入,由之前的L个输出构成,L为时间窗口长度。Exemplarily, the network structure of the LSTM neural network is shown in Figure 2. The LSTM network includes four gate structures, each of which is a neural network containing num_layer hidden layers, and each hidden layer contains num_hidden neurons and is fully connected to the input vector [h(t-1), x(t)]. h(t-1) is the output of the previous moment and is a vector of dimension num_hidden. x(t) is the input of the current moment, which is composed of the previous L outputs, and L is the length of the time window.
在本实施例中,可选地,将综合性能指标序列构成(N-L)组样本,将其中的80%作为训练集用于网络训练,20%作为测试集用于测试,划分结果如图4所示。在测试时,将上一时刻的输出加到综合性能指标序列中,滑动时间窗口形成新的L个输入,并定义神经网络的均方根误差(Root Mean Squared Error,RMSE)为:
In this embodiment, the comprehensive performance index sequence is optionally used to form (NL) group samples, 80% of which are used as training sets for network training, and 20% are used as test sets for testing. The division result is shown in Figure 4. During the test, the output of the previous moment is added to the comprehensive performance index sequence, the sliding time window forms new L inputs, and the root mean square error (RMSE) of the neural network is defined as:
In this embodiment, the comprehensive performance index sequence is optionally used to form (NL) group samples, 80% of which are used as training sets for network training, and 20% are used as test sets for testing. The division result is shown in Figure 4. During the test, the output of the previous moment is added to the comprehensive performance index sequence, the sliding time window forms new L inputs, and the root mean square error (RMSE) of the neural network is defined as:
其中T为测试集样本数量,Zi为测试集中第i个综合性能评价指标,f(xi)是LSTM神经网络对第i个样本输入xi的预测值。Where T is the number of samples in the test set, Zi is the i-th comprehensive performance evaluation index in the test set, and f( xi ) is the predicted value of the LSTM neural network for the i-th sample input xi .
再基于离散差分进化法结合均方根误差,对基于训练集训练得到的目标LSTM神经网络进行优化。Then, based on the discrete differential evolution method combined with the root mean square error, the target LSTM neural network trained based on the training set is optimized.
可选地,所述利用所述离散差分进化法,根据所述测试集,对所述目标长短时记忆神经
网络的网络参数进行优化,直至所述目标长短时记忆神经网络的达到所述预设收敛条件,得到所述硅橡胶材料性能预测模型,包括:Optionally, the discrete differential evolution method is used to perform the target long short-term memory neural network according to the test set. The network parameters of the network are optimized until the target long short-term memory neural network reaches the preset convergence condition, and the silicone rubber material performance prediction model is obtained, including:
基于所述时间窗口长度、所述隐含层神经元个数和所述隐含层层数,生成初始种群;Generate an initial population based on the time window length, the number of neurons in the hidden layer, and the number of hidden layers;
根据所述测试集,以所述初始种群训练所述目标长短时记忆神经网络,并以均方根误差计算个体适应度;According to the test set, the target long short-term memory neural network is trained with the initial population, and the individual fitness is calculated with the root mean square error;
对所述初始种群进行变异操作和交叉操作,得到最新种群;Performing mutation and crossover operations on the initial population to obtain a newest population;
根据所述测试集,以所述最新种群训练所述目标长短时记忆神经网络,并以均方根误差计算最新个体适应度;According to the test set, the target long short-term memory neural network is trained with the latest population, and the latest individual fitness is calculated with the root mean square error;
若所述最新个体适应度不大于所述个体适应度,则以所述最新种群作为新的初始种群进行下一轮变异操作和交叉操作,直至满足终止条件,输出最优个体;If the latest individual fitness is not greater than the individual fitness, the latest population is used as the new initial population to perform the next round of mutation and crossover operations until the termination condition is met and the optimal individual is output;
以所述最优个体更新所述目标长短时记忆神经网络,得到所述硅橡胶材料性能预测模型。The target long short-term memory neural network is updated with the optimal individual to obtain the silicone rubber material performance prediction model.
在本可选实施例中,决定LSTM神经网络预测效果的网络参数包括隐含层层数num_layer,隐含层神经元个数num_hidden和时间窗口长度L。为了保证模型预测效果,以RMSE为优化目标,采用DDE算法对LSTM神经网络的三个网络参数进行优化。In this optional embodiment, the network parameters that determine the prediction effect of the LSTM neural network include the number of hidden layers num_layer, the number of hidden layer neurons num_hidden and the time window length L. In order to ensure the prediction effect of the model, the three network parameters of the LSTM neural network are optimized using the DDE algorithm with RMSE as the optimization target.
示例性地,如图3所示,设定num_hidden、num_layer、L范围,设定最大迭代次数tmax、种群数量Np、变异算子F0和交叉算子CR;Exemplarily, as shown in FIG3 , num_hidden, num_layer, L range are set, and the maximum number of iterations t max , the population size N p , the mutation operator F 0 and the crossover operator CR are set;
生成初始种群,使用初始种群训练LSTM神经网络,并基于上述均方根误差公式计算个体适应度RSME(X(i));Generate an initial population, use the initial population to train the LSTM neural network, and calculate the individual fitness RSME(X(i)) based on the above root mean square error formula;
变异操作为:
V=X(1)+floor{F0(X(2)-X(3))};The mutation operation is:
V = X(1) + floor{F 0 (X(2) - X(3))};
V=X(1)+floor{F0(X(2)-X(3))};The mutation operation is:
V = X(1) + floor{F 0 (X(2) - X(3))};
其中,X(1)、X(2)和X(3)为种群X中三个不同个体,floor表示为向下取整;Among them, X(1), X(2) and X(3) are three different individuals in population X, and floor means rounding down;
交叉操作为:以交叉算子CR将种群X中的个体替换为变异种群中V的个体,以边界吸收方式处理V中越界个体,得到最新种群V(i);The crossover operation is: replace the individuals in population X with the individuals in the mutant population V using the crossover operator CR, and process the out-of-bounds individuals in V using the boundary absorption method to obtain the latest population V(i);
使用最新种群V(i)训练LSTM神经网络,并基于上述均方根误差公式计算最新个体适应度RSME(V(i));Use the latest population V(i) to train the LSTM neural network, and calculate the latest individual fitness RSME(V(i)) based on the above root mean square error formula;
若RSME(V(i))≤RSME(X(i)),则以最新种群V(i)作为新的初始种群X(i)进行下一次变异操作、交叉操作和选择操作,直至满足终止条件,输出最优个体。If RSME(V(i))≤RSME(X(i)), the latest population V(i) is used as the new initial population X(i) for the next mutation, crossover and selection operations until the termination condition is met and the optimal individual is output.
终止条件为RSME(V(i))≤预设值,或迭代次数达到tmax。经过DDE优化后确定的最优个体,即LSTM模型的参数num_layer=2,num_hidden=8,L=3。The termination condition is RSME(V(i))≤preset value, or the number of iterations reaches t max . The optimal individual determined after DDE optimization, that is, the parameters of the LSTM model are num_layer=2, num_hidden=8, and L=3.
在一些实施例中,所述步骤S104,包括:In some embodiments, the step S104 includes:
以所述综合性能指标初始序列作为所述硅橡胶材料性能预测模型的模型输入,预测所述
待预测硅橡胶材料在下一个老化阶段时的目标综合性能指标数据;The initial sequence of comprehensive performance indicators is used as the model input of the silicone rubber material performance prediction model to predict the The target comprehensive performance index data of the silicone rubber material to be predicted at the next aging stage;
将所述目标综合性能指标数据插入到所述综合性能指标初始序列,得到新的综合性能指标初始序列;Inserting the target comprehensive performance indicator data into the comprehensive performance indicator initial sequence to obtain a new comprehensive performance indicator initial sequence;
以新的综合性能指标初始序列,继续预测下一个老化阶段的目标综合性能指标数据,直至所述目标综合性能指标数据低于预设阈值,并根据所述目标综合性能指标数据低于预设阈值时所经历的迭代次数,预测所述待预测硅橡胶材料的剩余寿命。Using a new initial sequence of comprehensive performance indicators, continue to predict the target comprehensive performance indicator data of the next aging stage until the target comprehensive performance indicator data is lower than the preset threshold, and predict the remaining life of the silicone rubber material to be predicted based on the number of iterations experienced when the target comprehensive performance indicator data is lower than the preset threshold.
在本实施例中,示例性地,获取待预测硅橡胶材料的3个连续的体积电阻率、介电常数和介电损耗,以这3个介电性能数据作为综合性能指标初始序列Z的初始值,并根据材料失效时的介电性能数据计算综合性能指标阈值Zend。LSTM神经网络根综合性能指标初始序列Z的3个初始值,预测下一时刻的目标综合性能指标数据,并将该目标综合性能指标数据插到原序列Z最后一个,删除第一个,保持原序列的样本量不变,实现综合性能指标序列的动态更新,根据综合性能指标低于阈值Zend所经历的迭代次数估计待预测硅橡胶材料的剩余寿命。In this embodiment, illustratively, three consecutive volume resistivities, dielectric constants, and dielectric losses of the silicone rubber material to be predicted are obtained, and these three dielectric performance data are used as the initial values of the initial sequence Z of the comprehensive performance index, and the threshold Zend of the comprehensive performance index is calculated according to the dielectric performance data when the material fails. The LSTM neural network predicts the target comprehensive performance index data at the next moment based on the three initial values of the initial sequence Z of the comprehensive performance index, and inserts the target comprehensive performance index data into the last one of the original sequence Z, deletes the first one, keeps the sample size of the original sequence unchanged, realizes the dynamic update of the comprehensive performance index sequence, and estimates the remaining life of the silicone rubber material to be predicted according to the number of iterations experienced when the comprehensive performance index is lower than the threshold Zend.
示例性地。预测结果如图5所示,由图5可知经过36次迭代后,待预测硅橡胶材料的指标性能低于阈值Zend,所以该待预测硅橡胶材料的剩余寿命为36×12小时=432小时。For example, the prediction result is shown in FIG5 , from which it can be seen that after 36 iterations, the index performance of the silicone rubber material to be predicted is lower than the threshold Zend, so the remaining life of the silicone rubber material to be predicted is 36×12 hours=432 hours.
为了执行上述方法实施例对应的基于LSTM神经网络的硅橡胶材料性能预测方法,以实现相应的功能和技术效果。参见图7,图7示出了本申请实施例提供的一种基于LSTM神经网络的硅橡胶材料性能预测装置的结构框图。为了便于说明,仅示出了与本实施例相关的部分,本申请实施例提供的基于LSTM神经网络的硅橡胶材料性能预测装置,包括:In order to execute the silicone rubber material performance prediction method based on LSTM neural network corresponding to the above method embodiment, to achieve the corresponding functions and technical effects. Referring to FIG. 7, FIG. 7 shows a block diagram of a silicone rubber material performance prediction device based on LSTM neural network provided in an embodiment of the present application. For ease of explanation, only the parts related to this embodiment are shown. The silicone rubber material performance prediction device based on LSTM neural network provided in an embodiment of the present application includes:
获取模块601,用于获取硅橡胶材料在多个老化阶段下的介电性能数据;An acquisition module 601 is used to acquire dielectric property data of the silicone rubber material at multiple aging stages;
融合模块602,用于对所述介电性能数据进行融合处理,得到所述硅橡胶材料的综合性能指标序列,所述综合性能指标序列能够表征所述硅橡胶材料在整个老化过程的电性能演变情况,所述整个老化过程包括多个老化阶段;A fusion module 602 is used to fuse the dielectric performance data to obtain a comprehensive performance index sequence of the silicone rubber material, wherein the comprehensive performance index sequence can characterize the evolution of the electrical performance of the silicone rubber material during the entire aging process, wherein the entire aging process includes multiple aging stages;
优化模块603,用于根据所述综合性能指标序列,以离散差分进化法对长短时记忆神经网络的网络参数进行优化,直至所述长短时记忆神经网络达到预设收敛条件,得到硅橡胶材料性能预测模型;An optimization module 603 is used to optimize the network parameters of the long short-term memory neural network by discrete differential evolution method according to the comprehensive performance index sequence until the long short-term memory neural network reaches a preset convergence condition to obtain a silicone rubber material performance prediction model;
预测模块604,用于利用所述硅橡胶材料性能预测模型,根据待预测硅橡胶材料的综合性能指标初始序列,预测所述待预测硅橡胶材料在各个老化阶段的目标综合性能指标数据。The prediction module 604 is used to use the silicone rubber material performance prediction model to predict the target comprehensive performance index data of the silicone rubber material to be predicted at each aging stage according to the initial sequence of comprehensive performance indexes of the silicone rubber material to be predicted.
在一些实施例中,所述介电性能数据包括体积电阻率、相对介电常数和介电损耗值,所述融合模块602,包括:In some embodiments, the dielectric property data includes volume resistivity, relative dielectric constant and dielectric loss value, and the fusion module 602 includes:
构建单元,用于以所述体积电阻率、目标频点上的所述相对介电常数和所述介电损耗值为性能指标,构建样本指标矩阵;
A construction unit, configured to construct a sample index matrix by taking the volume resistivity, the relative dielectric constant at a target frequency point and the dielectric loss value as performance indicators;
处理单元,用于利用主成分分析法,根据所述样本指标矩阵,对所述介电性能数据进行融合处理,生成所述综合性能指标序列。The processing unit is used to use the principal component analysis method to fuse the dielectric performance data according to the sample indicator matrix to generate the comprehensive performance indicator sequence.
在一些实施例中,所述处理单元,具体用于:In some embodiments, the processing unit is specifically configured to:
利用主成分分析法,根据所述样本指标矩阵,计算各个性能指标间的相关系数矩阵,以及计算所述相关系数矩阵的特征值;Using principal component analysis, according to the sample indicator matrix, calculate the correlation coefficient matrix between the various performance indicators, and calculate the eigenvalues of the correlation coefficient matrix;
确定满足在成分贡献率条件时的目标特征值对应的目标特征向量;Determine the target eigenvector corresponding to the target eigenvalue when the component contribution rate condition is satisfied;
根据所述目标特征向量,对所述介电性能数据进行融合处理,得到所述综合性能指标序列。According to the target feature vector, the dielectric performance data is fused to obtain the comprehensive performance index sequence.
在一些实施例中,所述综合性能指标序列为:
a1=[aR,aei,ati]T;In some embodiments, the comprehensive performance indicator sequence is:
a 1 = [a R , a ei , a ti ] T ;
a1=[aR,aei,ati]T;In some embodiments, the comprehensive performance indicator sequence is:
a 1 = [a R , a ei , a ti ] T ;
其中,Z表示综合性能指标序列,a1为目标特征向量,RVol为体积电阻率的标准化序列,Epsi为第i个频点上的相对介电常数的标准化序列,Tanδi为第i个频点上的介电损耗值的标准化序列。Among them, Z represents the comprehensive performance index sequence, a1 is the target feature vector, R Vol is the standardized sequence of volume resistivity, Eps i is the standardized sequence of relative dielectric constant at the i-th frequency point, and Tanδ i is the standardized sequence of dielectric loss value at the i-th frequency point.
在一些实施例中,所述优化模块603,包括:In some embodiments, the optimization module 603 includes:
划分单元,用于将所述综合性能指标序列分为训练集和测试集;A division unit, used for dividing the comprehensive performance indicator sequence into a training set and a test set;
训练单元,用于利用所述训练集,对所述长短时记忆神经网络进行训练,得到训练后的目标长短时记忆神经网络;A training unit, used to train the long short-term memory neural network using the training set to obtain a trained target long short-term memory neural network;
优化单元,用于利用所述离散差分进化法,根据所述测试集,对所述目标长短时记忆神经网络的网络参数进行优化,直至所述目标长短时记忆神经网络的达到所述预设收敛条件,得到所述硅橡胶材料性能预测模型,所述网络参数包括时间窗口长度、隐含层神经元个数和隐含层层数。An optimization unit is used to optimize the network parameters of the target long short-term memory neural network according to the test set by using the discrete differential evolution method until the target long short-term memory neural network reaches the preset convergence condition, thereby obtaining the silicone rubber material performance prediction model, wherein the network parameters include the time window length, the number of hidden layer neurons and the number of hidden layer layers.
在一些实施例中,所述优化单元,具体用于:In some embodiments, the optimization unit is specifically used to:
基于所述时间窗口长度、所述隐含层神经元个数和所述隐含层层数,生成初始种群;Generate an initial population based on the time window length, the number of neurons in the hidden layer, and the number of hidden layers;
根据所述测试集,以所述初始种群训练所述目标长短时记忆神经网络,并以均方根误差计算个体适应度;According to the test set, the target long short-term memory neural network is trained with the initial population, and the individual fitness is calculated with the root mean square error;
对所述初始种群进行变异操作和交叉操作,得到最新种群;Performing mutation and crossover operations on the initial population to obtain a newest population;
根据所述测试集,以所述最新种群训练所述目标长短时记忆神经网络,并以均方根误差计算最新个体适应度;According to the test set, the target long short-term memory neural network is trained with the latest population, and the latest individual fitness is calculated with the root mean square error;
若所述最新个体适应度不大于所述个体适应度,则以所述最新种群作为新的初始种群进行下一轮变异操作和交叉操作,直至满足终止条件,输出最优个体;
If the latest individual fitness is not greater than the individual fitness, the latest population is used as the new initial population to perform the next round of mutation and crossover operations until the termination condition is met and the optimal individual is output;
以所述最优个体更新所述目标长短时记忆神经网络,得到所述硅橡胶材料性能预测模型。The target long short-term memory neural network is updated with the optimal individual to obtain the silicone rubber material performance prediction model.
在一些实施例中,所述预测模块604,具体用于:In some embodiments, the prediction module 604 is specifically configured to:
以所述综合性能指标初始序列作为所述硅橡胶材料性能预测模型的模型输入,预测所述待预测硅橡胶材料在下一个老化阶段时的目标综合性能指标数据;Using the initial sequence of comprehensive performance indicators as the model input of the silicone rubber material performance prediction model, predicting the target comprehensive performance indicator data of the silicone rubber material to be predicted at the next aging stage;
将所述目标综合性能指标数据插入到所述综合性能指标初始序列,得到新的综合性能指标初始序列;Inserting the target comprehensive performance indicator data into the comprehensive performance indicator initial sequence to obtain a new comprehensive performance indicator initial sequence;
以新的综合性能指标初始序列,继续预测下一个老化阶段的目标综合性能指标数据,直至所述目标综合性能指标数据低于预设阈值,并根据所述目标综合性能指标数据低于预设阈值时所经历的迭代次数,预测所述待预测硅橡胶材料的剩余寿命。Using a new initial sequence of comprehensive performance indicators, continue to predict the target comprehensive performance indicator data of the next aging stage until the target comprehensive performance indicator data is lower than the preset threshold, and predict the remaining life of the silicone rubber material to be predicted based on the number of iterations experienced when the target comprehensive performance indicator data is lower than the preset threshold.
上述的基于LSTM神经网络的硅橡胶材料性能预测装置可实施上述方法实施例的基于LSTM神经网络的硅橡胶材料性能预测方法。上述方法实施例中的可选项也适用于本实施例,这里不再详述。本申请实施例的其余内容可参照上述方法实施例的内容,在本实施例中,不再进行赘述。The above-mentioned silicone rubber material performance prediction device based on LSTM neural network can implement the silicone rubber material performance prediction method based on LSTM neural network of the above-mentioned method embodiment. The options in the above-mentioned method embodiment are also applicable to this embodiment and will not be described in detail here. The rest of the contents of the embodiment of this application can refer to the contents of the above-mentioned method embodiment, and will not be repeated in this embodiment.
图7为本申请一实施例提供的计算机设备的结构示意图。如图7所示,该实施例的计算机设备7包括:至少一个处理器70(图7中仅示出一个)处理器、存储器71以及存储在所述存储器71中并可在所述至少一个处理器70上运行的计算机程序72,所述处理器70执行所述计算机程序72时实现上述任意方法实施例中的步骤。FIG7 is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. As shown in FIG7 , the computer device 7 of this embodiment includes: at least one processor 70 (only one is shown in FIG7 ), a memory 71, and a computer program 72 stored in the memory 71 and executable on the at least one processor 70, and when the processor 70 executes the computer program 72, the steps in any of the above method embodiments are implemented.
所述计算机设备7可以是智能手机、平板电脑、桌上型计算机和云端服务器等计算设备。该计算机设备可包括但不仅限于处理器70、存储器71。本领域技术人员可以理解,图7仅仅是计算机设备7的举例,并不构成对计算机设备7的限定,可以包括比图示更多或更少的部件,或者组合某些部件,或者不同的部件,例如还可以包括输入输出设备、网络接入设备等。The computer device 7 may be a computing device such as a smart phone, a tablet computer, a desktop computer, and a cloud server. The computer device may include, but is not limited to, a processor 70 and a memory 71. Those skilled in the art will appreciate that FIG. 7 is merely an example of the computer device 7 and does not constitute a limitation on the computer device 7. The computer device 7 may include more or fewer components than shown in the figure, or may combine certain components, or different components, and may also include, for example, input and output devices, network access devices, etc.
所称处理器70可以是中央处理单元(Central Processing Unit,CPU),该处理器70还可以是其他通用处理器、数字信号处理器(Digital Signal Processor,DSP)、专用集成电路(Application Specific Integrated Circuit,ASIC)、现成可编程门阵列(Field-Programmable Gate Array,FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。The processor 70 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.
所述存储器71在一些实施例中可以是所述计算机设备7的内部存储单元,例如计算机设备7的硬盘或内存。所述存储器71在另一些实施例中也可以是所述计算机设备7的外部存储设备,例如所述计算机设备7上配备的插接式硬盘,智能存储卡(Smart Media Card,SMC),安全数字(Secure Digital,SD)卡,闪存卡(Flash Card)等。进一步地,所述存储器71
还可以既包括所述计算机设备7的内部存储单元也包括外部存储设备。所述存储器71用于存储操作系统、应用程序、引导装载程序(BootLoader)、数据以及其他程序等,例如所述计算机程序的程序代码等。所述存储器71还可以用于暂时地存储已经输出或者将要输出的数据。In some embodiments, the memory 71 may be an internal storage unit of the computer device 7, such as a hard disk or memory of the computer device 7. In other embodiments, the memory 71 may also be an external storage device of the computer device 7, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 7. It may also include both the internal storage unit and the external storage device of the computer device 7. The memory 71 is used to store an operating system, an application program, a boot loader, data, and other programs, such as the program code of the computer program, etc. The memory 71 may also be used to temporarily store data that has been output or is to be output.
另外,本申请实施例还提供一种计算机可读存储介质,所述计算机可读存储介质存储有计算机程序,所述计算机程序被处理器执行时实现上述任意方法实施例中的步骤。In addition, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.
本申请实施例提供了一种计算机程序产品,当计算机程序产品在计算机设备上运行时,使得计算机设备执行时实现上述各个方法实施例中的步骤。An embodiment of the present application provides a computer program product. When the computer program product is run on a computer device, the computer device implements the steps in the above-mentioned method embodiments when executing the computer device.
在本申请所提供的几个实施例中,可以理解的是,流程图或框图中的每个方框可以代表一个模块、程序段或代码的一部分,所述模块、程序段或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意的是,在有些作为替换的实现方式中,方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如,两个连续的方框实际上可以基本并行地执行,它们有时也可以按相反的顺序执行,这依所涉及的功能而定。In several embodiments provided in the present application, it is understood that each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.
所述功能如果以软件功能模块的形式实现并作为独立的产品销售或使用时,可以存储在一个计算机可读取存储介质中。基于这样的理解,本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来,该计算机软件产品存储在一个存储介质中,包括若干指令用以使得一台计算机设备执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括:U盘、移动硬盘、只读存储器(ROM,Read-Only Memory)、随机存取存储器(RAM,Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.
以上所述的具体实施例,对本申请的目的、技术方案和有益效果进行了进一步的详细说明,应当理解,以上所述仅为本申请的具体实施例而已,并不用于限定本申请的保护范围。特别指出,对于本领域技术人员来说,凡在本申请的精神和原则之内,所做的任何修改、等同替换、改进等,均应包含在本申请的保护范围之内。
The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of protection of the present application.
Claims (10)
- 一种基于LSTM神经网络的硅橡胶材料性能预测方法,其特征在于,包括:A method for predicting silicone rubber material properties based on LSTM neural network, characterized by comprising:获取硅橡胶材料在多个老化阶段下的介电性能数据;Obtain dielectric properties data of silicone rubber materials at multiple aging stages;对所述介电性能数据进行融合处理,得到所述硅橡胶材料的综合性能指标序列,所述综合性能指标序列能够表征所述硅橡胶材料在整个老化过程的电性能演变情况,所述整个老化过程包括多个老化阶段;The dielectric performance data are fused to obtain a comprehensive performance index sequence of the silicone rubber material, wherein the comprehensive performance index sequence can characterize the evolution of the electrical performance of the silicone rubber material during the entire aging process, wherein the entire aging process includes multiple aging stages;根据所述综合性能指标序列,以离散差分进化法对长短时记忆神经网络的网络参数进行优化,直至所述长短时记忆神经网络达到预设收敛条件,得到硅橡胶材料性能预测模型;According to the comprehensive performance index sequence, the network parameters of the long short-term memory neural network are optimized by discrete differential evolution method until the long short-term memory neural network reaches a preset convergence condition, thereby obtaining a silicone rubber material performance prediction model;利用所述硅橡胶材料性能预测模型,根据待预测硅橡胶材料的综合性能指标初始序列,预测所述待预测硅橡胶材料在各个老化阶段的目标综合性能指标数据。The silicone rubber material performance prediction model is used to predict the target comprehensive performance index data of the silicone rubber material to be predicted at each aging stage according to the initial sequence of comprehensive performance indexes of the silicone rubber material to be predicted.
- 如权利要求1所述的基于LSTM神经网络的硅橡胶材料性能预测方法,其特征在于,所述介电性能数据包括体积电阻率、相对介电常数和介电损耗值,所述对所述介电性能数据进行融合处理,得到所述硅橡胶材料的综合性能指标序列,包括:The method for predicting silicone rubber material performance based on LSTM neural network according to claim 1, characterized in that the dielectric performance data includes volume resistivity, relative dielectric constant and dielectric loss value, and the fusion processing of the dielectric performance data to obtain a comprehensive performance index sequence of the silicone rubber material includes:以所述体积电阻率、目标频点上的所述相对介电常数和所述介电损耗值为性能指标,构建样本指标矩阵;Taking the volume resistivity, the relative dielectric constant at the target frequency point and the dielectric loss value as performance indicators, a sample indicator matrix is constructed;利用主成分分析法,根据所述样本指标矩阵,对所述介电性能数据进行融合处理,生成所述综合性能指标序列。The dielectric performance data is fused and processed according to the sample indicator matrix by using the principal component analysis method to generate the comprehensive performance indicator sequence.
- 如权利要求2所述的基于LSTM神经网络的硅橡胶材料性能预测方法,其特征在于,所述利用主成分分析法,根据所述样本指标矩阵,对所述介电性能数据进行融合处理,生成所述综合性能指标序列,包括:The method for predicting the performance of silicone rubber materials based on an LSTM neural network according to claim 2 is characterized in that the principal component analysis method is used to fuse the dielectric performance data according to the sample indicator matrix to generate the comprehensive performance indicator sequence, including:利用主成分分析法,根据所述样本指标矩阵,计算各个性能指标间的相关系数矩阵,以及计算所述相关系数矩阵的特征值;Using principal component analysis, according to the sample indicator matrix, calculate the correlation coefficient matrix between the various performance indicators, and calculate the eigenvalue of the correlation coefficient matrix;确定满足在成分贡献率条件时的目标特征值对应的目标特征向量;Determine the target eigenvector corresponding to the target eigenvalue when the component contribution rate condition is satisfied;根据所述目标特征向量,对所述介电性能数据进行融合处理,得到所述综合性能指标序列。The dielectric performance data is fused according to the target feature vector to obtain the comprehensive performance index sequence.
- 如权利要求3所述的基于LSTM神经网络的硅橡胶材料性能预测方法,其特征在于,所述综合性能指标序列为:
The method for predicting silicone rubber material properties based on LSTM neural network according to claim 3, characterized in that the comprehensive performance index sequence is:
a1=[aR,aei,ati]T;a 1 = [a R , a ei , a ti ] T ;其中,Z表示综合性能指标序列,a1为目标特征向量,RVol为体积电阻率的标准化序列,Epsi为第i个频点上的相对介电常数的标准化序列,Tanδi为第i个频点上的介电损耗值的标准化序列。Among them, Z represents the comprehensive performance index sequence, a1 is the target feature vector, R Vol is the standardized sequence of volume resistivity, Eps i is the standardized sequence of relative dielectric constant at the i-th frequency point, and Tanδ i is the standardized sequence of dielectric loss value at the i-th frequency point. - 如权利要求1所述的基于LSTM神经网络的硅橡胶材料性能预测方法,其特征在于,所述根据所述综合性能指标序列,以离散差分进化法对长短时记忆神经网络的网络参数进行优化,直至所述长短时记忆神经网络达到预设收敛条件,得到硅橡胶材料性能预测模型,包括:The method for predicting the performance of a silicone rubber material based on an LSTM neural network according to claim 1 is characterized in that, according to the comprehensive performance index sequence, the network parameters of the long short-term memory neural network are optimized by discrete differential evolution method until the long short-term memory neural network reaches a preset convergence condition, and a silicone rubber material performance prediction model is obtained, comprising:将所述综合性能指标序列分为训练集和测试集;Dividing the comprehensive performance indicator sequence into a training set and a test set;利用所述训练集,对所述长短时记忆神经网络进行训练,得到训练后的目标长短时记忆神经网络;Using the training set, training the long short-term memory neural network to obtain a trained target long short-term memory neural network;利用所述离散差分进化法,根据所述测试集,对所述目标长短时记忆神经网络的网络参数进行优化,直至所述目标长短时记忆神经网络的达到所述预设收敛条件,得到所述硅橡胶材料性能预测模型,所述网络参数包括时间窗口长度、隐含层神经元个数和隐含层层数。 The discrete differential evolution method is used to optimize the network parameters of the target long short-term memory neural network according to the test set until the target long short-term memory neural network reaches the preset convergence condition, thereby obtaining the silicone rubber material performance prediction model, wherein the network parameters include the time window length, the number of hidden layer neurons and the number of hidden layer layers.
- 如权利要求5所述的基于LSTM神经网络的硅橡胶材料性能预测方法,其特征在于,所述利用所述离散差分进化法,根据所述测试集,对所述目标长短时记忆神经网络的网络参数进行优化,直至所述目标长短时记忆神经网络的达到所述预设收敛条件,得到所述硅橡胶材料性能预测模型,包括:The method for predicting the performance of a silicone rubber material based on an LSTM neural network according to claim 5, characterized in that the discrete differential evolution method is used to optimize the network parameters of the target long short-term memory neural network according to the test set until the target long short-term memory neural network reaches the preset convergence condition, thereby obtaining the silicone rubber material performance prediction model, comprising:基于所述时间窗口长度、所述隐含层神经元个数和所述隐含层层数,生成初始种群;Generate an initial population based on the time window length, the number of neurons in the hidden layer, and the number of hidden layers;根据所述测试集,以所述初始种群训练所述目标长短时记忆神经网络,并以均方根误差计算个体适应度;According to the test set, the target long short-term memory neural network is trained with the initial population, and the individual fitness is calculated with the root mean square error;对所述初始种群进行变异操作和交叉操作,得到最新种群;Performing mutation and crossover operations on the initial population to obtain a newest population;根据所述测试集,以所述最新种群训练所述目标长短时记忆神经网络,并以均方根误差计算最新个体适应度;According to the test set, the target long short-term memory neural network is trained with the latest population, and the latest individual fitness is calculated with the root mean square error;若所述最新个体适应度不大于所述个体适应度,则以所述最新种群作为新的初始种群进行下一轮变异操作和交叉操作,直至满足终止条件,输出最优个体;If the latest individual fitness is not greater than the individual fitness, the latest population is used as the new initial population to perform the next round of mutation and crossover operations until the termination condition is met and the optimal individual is output;以所述最优个体更新所述目标长短时记忆神经网络,得到所述硅橡胶材料性能预测模型。The target long short-term memory neural network is updated with the optimal individual to obtain the silicone rubber material performance prediction model.
- 如权利要求1所述的基于LSTM神经网络的硅橡胶材料性能预测方法,其特征在于,所述利用所述硅橡胶材料性能预测模型,根据待预测硅橡胶材料的综合性能指标初始序列,预测所述待预测硅橡胶材料在各个老化阶段的目标综合性能指标数据,包括:The method for predicting the performance of a silicone rubber material based on an LSTM neural network according to claim 1, characterized in that the method uses the silicone rubber material performance prediction model to predict the target comprehensive performance index data of the silicone rubber material to be predicted at each aging stage according to the initial sequence of the comprehensive performance index of the silicone rubber material to be predicted, including:以所述综合性能指标初始序列作为所述硅橡胶材料性能预测模型的模型输入,预测所述待预测硅橡胶材料在下一个老化阶段时的目标综合性能指标数据;Using the initial sequence of comprehensive performance indicators as the model input of the silicone rubber material performance prediction model, predicting the target comprehensive performance indicator data of the silicone rubber material to be predicted at the next aging stage;将所述目标综合性能指标数据插入到所述综合性能指标初始序列,得到新的综合性能指标初始序列;Inserting the target comprehensive performance indicator data into the comprehensive performance indicator initial sequence to obtain a new comprehensive performance indicator initial sequence;以新的综合性能指标初始序列,继续预测下一个老化阶段的目标综合性能指标数据,直至所述目标综合性能指标数据低于预设阈值,并根据所述目标综合性能指标数据低于预设阈值时所经历的迭代次数,预测所述待预测硅橡胶材料的剩余寿命。Using a new initial sequence of comprehensive performance indicators, continue to predict the target comprehensive performance indicator data of the next aging stage until the target comprehensive performance indicator data is lower than the preset threshold, and predict the remaining life of the silicone rubber material to be predicted based on the number of iterations experienced when the target comprehensive performance indicator data is lower than the preset threshold.
- 一种基于LSTM神经网络的硅橡胶材料性能预测装置,其特征在于,包括:A silicone rubber material performance prediction device based on LSTM neural network, characterized by comprising:获取模块,用于获取硅橡胶材料在多个老化阶段下的介电性能数据;An acquisition module, used for acquiring dielectric property data of silicone rubber materials at multiple aging stages;融合模块,用于对所述介电性能数据进行融合处理,得到所述硅橡胶材料的综合性能指标序列,所述综合性能指标序列能够表征所述硅橡胶材料在整个老化过程的电性能演变情况,所述整个老化过程包括多个老化阶段;A fusion module, used for fusing the dielectric performance data to obtain a comprehensive performance index sequence of the silicone rubber material, wherein the comprehensive performance index sequence can characterize the evolution of the electrical performance of the silicone rubber material during the entire aging process, wherein the entire aging process includes multiple aging stages;优化模块,用于根据所述综合性能指标序列,以离散差分进化法对长短时记忆神经网络的网络参数进行优化,直至所述长短时记忆神经网络达到预设收敛条件,得到硅橡胶材料性能预测模型;An optimization module, used to optimize the network parameters of the long short-term memory neural network by discrete differential evolution method according to the comprehensive performance index sequence, until the long short-term memory neural network reaches a preset convergence condition, thereby obtaining a silicone rubber material performance prediction model;预测模块,用于利用所述硅橡胶材料性能预测模型,根据待预测硅橡胶材料的综合性能指标初始序列,预测所述待预测硅橡胶材料在各个老化阶段的目标综合性能指标数据。The prediction module is used to use the silicone rubber material performance prediction model to predict the target comprehensive performance index data of the silicone rubber material to be predicted at each aging stage according to the initial sequence of comprehensive performance indicators of the silicone rubber material to be predicted.
- 一种计算机设备,其特征在于,包括处理器和存储器,所述存储器用于存储计算机程序,所述计算机程序被所述处理器执行时实现如权利要求1至7任一项所述的基于LSTM神经网络的硅橡胶材料性能预测方法。A computer device, characterized in that it includes a processor and a memory, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the method for predicting the performance of silicone rubber materials based on an LSTM neural network as described in any one of claims 1 to 7 is implemented.
- 一种计算机可读存储介质,其特征在于,其存储有计算机程序,所述计算机程序被处理器执行时实现如权利要求1至7任一项所述的基于LSTM神经网络的硅橡胶材料性能预测方法。 A computer-readable storage medium, characterized in that it stores a computer program, and when the computer program is executed by a processor, it implements the silicone rubber material performance prediction method based on the LSTM neural network as described in any one of claims 1 to 7.
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Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110796306A (en) * | 2019-10-29 | 2020-02-14 | 山东科技大学 | Construction method for forex time series prediction |
CN111027775A (en) * | 2019-12-12 | 2020-04-17 | 中国长江三峡集团有限公司 | Step hydropower station generating capacity prediction method based on long-term and short-term memory network |
CN113065283A (en) * | 2021-03-26 | 2021-07-02 | 深圳技术大学 | Battery life prediction method, system, electronic device and storage medium |
US20220050130A1 (en) * | 2018-12-14 | 2022-02-17 | University Of Georgia Research Foundation, Inc. | Condition Monitoring Via Energy Consumption Audit in Electrical Devices and Electrical Waveform Audit in Power Networks |
CN114970412A (en) * | 2022-05-18 | 2022-08-30 | 合肥工业大学 | IGBT service life prediction method based on ICGWO-LSTM |
CN116030911A (en) * | 2022-10-26 | 2023-04-28 | 广东电网有限责任公司 | LSTM neural network-based silicone rubber material performance prediction method and device |
-
2022
- 2022-10-26 CN CN202211321928.0A patent/CN116030911A/en active Pending
-
2023
- 2023-06-21 WO PCT/CN2023/101600 patent/WO2024087670A1/en unknown
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20220050130A1 (en) * | 2018-12-14 | 2022-02-17 | University Of Georgia Research Foundation, Inc. | Condition Monitoring Via Energy Consumption Audit in Electrical Devices and Electrical Waveform Audit in Power Networks |
CN110796306A (en) * | 2019-10-29 | 2020-02-14 | 山东科技大学 | Construction method for forex time series prediction |
CN111027775A (en) * | 2019-12-12 | 2020-04-17 | 中国长江三峡集团有限公司 | Step hydropower station generating capacity prediction method based on long-term and short-term memory network |
CN113065283A (en) * | 2021-03-26 | 2021-07-02 | 深圳技术大学 | Battery life prediction method, system, electronic device and storage medium |
CN114970412A (en) * | 2022-05-18 | 2022-08-30 | 合肥工业大学 | IGBT service life prediction method based on ICGWO-LSTM |
CN116030911A (en) * | 2022-10-26 | 2023-04-28 | 广东电网有限责任公司 | LSTM neural network-based silicone rubber material performance prediction method and device |
Non-Patent Citations (1)
Title |
---|
SHU SHENGMAO: "Research on Power Generation Dispatching and System Design of Cascade Hydropower Station Based on Deep Learning", MASTER THESIS, HUAZHONG UNIVERSITY OF SCIENCE & TECHNOLOGY WUHAN, 5 June 2019 (2019-06-05), Huazhong University of Science & Technology Wuhan, XP093162567 * |
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