An intelligent diagnosis method based on self-adaptiveWasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extra...An intelligent diagnosis method based on self-adaptiveWasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extraction,which are commonly faced by rolling bearings and lead to low diagnostic accuracy.Initially,dual models of the Wasserstein deep convolutional generative adversarial network incorporating gradient penalty(1D-2DWDCGAN)are constructed to augment the original dataset.A self-adaptive loss threshold control training strategy is introduced,and establishing a self-adaptive balancing mechanism for stable model training.Subsequently,a diagnostic model based on multidimensional feature fusion is designed,wherein complex features from various dimensions are extracted,merging the original signal waveform features,structured features,and time-frequency features into a deep composite feature representation that encompasses multiple dimensions and scales;thus,efficient and accurate small sample fault diagnosis is facilitated.Finally,an experiment between the bearing fault dataset of CaseWestern ReserveUniversity and the fault simulation experimental platformdataset of this research group shows that this method effectively supplements the dataset and remarkably improves the diagnostic accuracy.The diagnostic accuracy after data augmentation reached 99.94%and 99.87%in two different experimental environments,respectively.In addition,robustness analysis is conducted on the diagnostic accuracy of the proposed method under different noise backgrounds,verifying its good generalization performance.展开更多
In the production processes of modern industry,accurate assessment of the system’s health state and traceability non-optimal factors are key to ensuring“safe,stable,long-term,full load and optimal”operation of the ...In the production processes of modern industry,accurate assessment of the system’s health state and traceability non-optimal factors are key to ensuring“safe,stable,long-term,full load and optimal”operation of the production process.The benzene-to-ethylene ratio control system is a complex system based on anMPC-PID doublelayer architecture.Taking into consideration the interaction between levels,coupling between loops and conditions of incomplete operation data,this paper proposes a health assessment method for the dual-layer control system by comprehensively utilizing deep learning technology.Firstly,according to the results of the pre-assessment of the system layers and loops bymultivariate statisticalmethods,seven characteristic parameters that have a significant impact on the health state of the system are identified.Next,aiming at the problem of incomplete assessment data set due to the uneven distribution of actual system operating health state,the original unbalanced dataset is augmented using aWasserstein generative adversarial network with gradient penalty term,and a complete dataset is obtained to characterise all the health states of the system.On this basis,a new deep learning-based health assessment framework for the benzeneto-ethylene ratio control system is constructed based on traditionalmultivariate statistical assessment.This framework can overcome the shortcomings of the linear weighted fusion related to the coupling and nonlinearity of the subsystem health state at different layers,and reduce the dependence of the prior knowledge.Furthermore,by introducing a dynamic attention mechanism(AM)into the convolutional neural network(CNN),the assessment model integrating both assessment and traceability is constructed,which can achieve the health assessment and trace the non-optimal factors of the complex control systems with the double-layer architecture.Finally,the effectiveness and superiority of the proposed method have been verified by the benzene-ethylene ratio control system of the alkylation process unit in a styrene plant.展开更多
The phenomenon of sub-synchronous oscillation(SSO)poses significant threats to the stability of power systems.The advent of artificial intelligence(AI)has revolutionized SSO research through data-driven methodologies,...The phenomenon of sub-synchronous oscillation(SSO)poses significant threats to the stability of power systems.The advent of artificial intelligence(AI)has revolutionized SSO research through data-driven methodologies,which necessitates a substantial collection of data for effective training,a requirement frequently unfulfilled in practical power systems due to limited data availability.To address the critical issue of data scarcity in training AI models,this paper proposes a novel transfer-learning-based(TL-based)Wasserstein generative adversarial network(WGAN)approach for synthetic data generation of SSO in wind farms.To improve the capability of WGAN to capture the bidirectional temporal features inherent in oscillation data,a bidirectional long short-term memory(BiLSTM)layer is introduced.Additionally,to address the training instability caused by few-shot learning scenarios,the discriminator is augmented with mini-batch discrimination(MBD)layers and gradient penalty(GP)terms.Finally,TL is leveraged to finetune the model,effectively bridging the gap between the training data and real-world system data.To evaluate the quality of the synthetic data,two indexes are proposed based on dynamic time warping(DTW)and frequency domain analysis,followed by a classification task.Case studies demonstrate the effectiveness of the proposed approach in swiftly generating a large volume of synthetic SSO data,thereby significantly mitigating the issue of data scarcity prevalent in SSO research.展开更多
Privacy-sensitive data encounter immense security and usability challenges in processing,analyzing,and sharing.Meanwhile,traditional privacy data desensitization methods suffer from issues such as poor quality and low...Privacy-sensitive data encounter immense security and usability challenges in processing,analyzing,and sharing.Meanwhile,traditional privacy data desensitization methods suffer from issues such as poor quality and low usability after desensitization.Therefore,a text data desensitization model that combines Transformer and Wasserstein Text convolutional Generative Adversarial Network(Trans-WTGAN)is proposed.Transformer as the generator and its self-attention mechanism can handle long-range dependencies,enabling the generated of higher-quality text;Text Convolutional Neural Network(TextCNN)integrates the idea of Wasserstein as the discriminator to enhance the stability of model training;and the strategy gradient scheme of reinforcement learning is employed.Reinforcement learning utilizes the policy gradient scheme as the updating method of generator parameters,ensuring the generated data retains the original key features and maintains a certain level of usability.The experimental results indicate that the proposed model scheme holds a greater advantage over existing methods in terms of text quality and structural consistency,can guarantee the desensitization effect,and ensures the usability of the privacy-sensitive data to a certain extent after desensitization,facilitates the simulation of the development environment for the use of real data and the analysis and sharing of data.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12272259 and 52005148).
文摘An intelligent diagnosis method based on self-adaptiveWasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extraction,which are commonly faced by rolling bearings and lead to low diagnostic accuracy.Initially,dual models of the Wasserstein deep convolutional generative adversarial network incorporating gradient penalty(1D-2DWDCGAN)are constructed to augment the original dataset.A self-adaptive loss threshold control training strategy is introduced,and establishing a self-adaptive balancing mechanism for stable model training.Subsequently,a diagnostic model based on multidimensional feature fusion is designed,wherein complex features from various dimensions are extracted,merging the original signal waveform features,structured features,and time-frequency features into a deep composite feature representation that encompasses multiple dimensions and scales;thus,efficient and accurate small sample fault diagnosis is facilitated.Finally,an experiment between the bearing fault dataset of CaseWestern ReserveUniversity and the fault simulation experimental platformdataset of this research group shows that this method effectively supplements the dataset and remarkably improves the diagnostic accuracy.The diagnostic accuracy after data augmentation reached 99.94%and 99.87%in two different experimental environments,respectively.In addition,robustness analysis is conducted on the diagnostic accuracy of the proposed method under different noise backgrounds,verifying its good generalization performance.
基金supported by the National Science Foundation of China(62263020)the Key Project of Natural Science Foundation of Gansu Province(25JRRA061)+1 种基金the Key R&D Program of Gansu Province(23YFGA0061)the Scientific Research Initiation Fund of Lanzhou University of Technology(061602).
文摘In the production processes of modern industry,accurate assessment of the system’s health state and traceability non-optimal factors are key to ensuring“safe,stable,long-term,full load and optimal”operation of the production process.The benzene-to-ethylene ratio control system is a complex system based on anMPC-PID doublelayer architecture.Taking into consideration the interaction between levels,coupling between loops and conditions of incomplete operation data,this paper proposes a health assessment method for the dual-layer control system by comprehensively utilizing deep learning technology.Firstly,according to the results of the pre-assessment of the system layers and loops bymultivariate statisticalmethods,seven characteristic parameters that have a significant impact on the health state of the system are identified.Next,aiming at the problem of incomplete assessment data set due to the uneven distribution of actual system operating health state,the original unbalanced dataset is augmented using aWasserstein generative adversarial network with gradient penalty term,and a complete dataset is obtained to characterise all the health states of the system.On this basis,a new deep learning-based health assessment framework for the benzeneto-ethylene ratio control system is constructed based on traditionalmultivariate statistical assessment.This framework can overcome the shortcomings of the linear weighted fusion related to the coupling and nonlinearity of the subsystem health state at different layers,and reduce the dependence of the prior knowledge.Furthermore,by introducing a dynamic attention mechanism(AM)into the convolutional neural network(CNN),the assessment model integrating both assessment and traceability is constructed,which can achieve the health assessment and trace the non-optimal factors of the complex control systems with the double-layer architecture.Finally,the effectiveness and superiority of the proposed method have been verified by the benzene-ethylene ratio control system of the alkylation process unit in a styrene plant.
基金supported by the National Natural Science Foundation of China(No.52377084)the Zhishan Young Scholar Program of Southeast University,China(No.2242024RCB0019)。
文摘The phenomenon of sub-synchronous oscillation(SSO)poses significant threats to the stability of power systems.The advent of artificial intelligence(AI)has revolutionized SSO research through data-driven methodologies,which necessitates a substantial collection of data for effective training,a requirement frequently unfulfilled in practical power systems due to limited data availability.To address the critical issue of data scarcity in training AI models,this paper proposes a novel transfer-learning-based(TL-based)Wasserstein generative adversarial network(WGAN)approach for synthetic data generation of SSO in wind farms.To improve the capability of WGAN to capture the bidirectional temporal features inherent in oscillation data,a bidirectional long short-term memory(BiLSTM)layer is introduced.Additionally,to address the training instability caused by few-shot learning scenarios,the discriminator is augmented with mini-batch discrimination(MBD)layers and gradient penalty(GP)terms.Finally,TL is leveraged to finetune the model,effectively bridging the gap between the training data and real-world system data.To evaluate the quality of the synthetic data,two indexes are proposed based on dynamic time warping(DTW)and frequency domain analysis,followed by a classification task.Case studies demonstrate the effectiveness of the proposed approach in swiftly generating a large volume of synthetic SSO data,thereby significantly mitigating the issue of data scarcity prevalent in SSO research.
基金supported by the National Natural Science Foundation of China(No.62262013).
文摘Privacy-sensitive data encounter immense security and usability challenges in processing,analyzing,and sharing.Meanwhile,traditional privacy data desensitization methods suffer from issues such as poor quality and low usability after desensitization.Therefore,a text data desensitization model that combines Transformer and Wasserstein Text convolutional Generative Adversarial Network(Trans-WTGAN)is proposed.Transformer as the generator and its self-attention mechanism can handle long-range dependencies,enabling the generated of higher-quality text;Text Convolutional Neural Network(TextCNN)integrates the idea of Wasserstein as the discriminator to enhance the stability of model training;and the strategy gradient scheme of reinforcement learning is employed.Reinforcement learning utilizes the policy gradient scheme as the updating method of generator parameters,ensuring the generated data retains the original key features and maintains a certain level of usability.The experimental results indicate that the proposed model scheme holds a greater advantage over existing methods in terms of text quality and structural consistency,can guarantee the desensitization effect,and ensures the usability of the privacy-sensitive data to a certain extent after desensitization,facilitates the simulation of the development environment for the use of real data and the analysis and sharing of data.
