Quick Access Recorder(QAR),an important device for storing data from various flight parameters,contains a large amount of valuable data and comprehensively records the real state of the airline flight.However,the reco...Quick Access Recorder(QAR),an important device for storing data from various flight parameters,contains a large amount of valuable data and comprehensively records the real state of the airline flight.However,the recorded data have certain missing values due to factors,such as weather and equipment anomalies.These missing values seriously affect the analysis of QAR data by aeronautical engineers,such as airline flight scenario reproduction and airline flight safety status assessment.Therefore,imputing missing values in the QAR data,which can further guarantee the flight safety of airlines,is crucial.QAR data also have multivariate,multiprocess,and temporal features.Therefore,we innovatively propose the imputation models A-AEGAN("A"denotes attention mechanism,"AE"denotes autoencoder,and"GAN"denotes generative adversarial network)and SA-AEGAN("SA"denotes self-attentive mechanism)for missing values of QAR data,which can be effectively applied to QAR data.Specifically,we apply an innovative generative adversarial network to impute missing values from QAR data.The improved gated recurrent unit is then introduced as the neural unit of GAN,which can successfully capture the temporal relationships in QAR data.In addition,we modify the basic structure of GAN by using an autoencoder as the generator and a recurrent neural network as the discriminator.The missing values in the QAR data are imputed by using the adversarial relationship between generator and discriminator.We introduce an attention mechanism in the autoencoder to further improve the capability of the proposed model to capture the features of QAR data.Attention mechanisms can maintain the correlation among QAR data and improve the capability of the model to impute missing data.Furthermore,we improve the proposed model by integrating a self-attention mechanism to further capture the relationship between different parameters within the QAR data.Experimental results on real datasets demonstrate that the model can reasonably impute the missing values in QAR data with excellent results.展开更多
Contamination events in water distribution networks(WDNs)can have a huge impact on water supply and public health;increasingly,online water quality sensors are deployed for real-time detection of contamination events....Contamination events in water distribution networks(WDNs)can have a huge impact on water supply and public health;increasingly,online water quality sensors are deployed for real-time detection of contamination events.Machine learning has been used to integrate multivariate time series water quality data at multiple stations for contamination detection;however,accurate extraction of spatial features in water quality signals remains challenging.This study proposed a contamination detection method based on generative adversarial networks(GANs).The GAN model was constructed to simultaneously consider the spatial correlation between sensor locations and temporal information of water quality indicators.The model consists of two networksda generator and a discriminatordthe outputs of which are used to measure the degree of abnormality of water quality data at each time step,referred to as the anomaly score.Bayesian sequential analysis is used to update the likelihood of event occurrence based on the anomaly scores.Alarms are then generated from the fusion of single-site and multi-site models.The proposed method was tested on a WDN for various contamination events with different characteristics.Results showed high detection performance by the proposed GAN method compared with the minimum volume ellipsoid benchmark method for various contamination amplitudes.Additionally,the GAN method achieved high accuracy for various contamination events with different amplitudes and numbers of anomalous water quality parameters,and water quality data from different sensor stations,highlighting its robustness and potential for practical application to real-time contamination events.展开更多
In this study, the probabilistic, data driven nature of the generative adversarial neural networks (GANs)was utilized to conduct virtual spray simulations for conditions relevant to aero engine combustors. Themodel co...In this study, the probabilistic, data driven nature of the generative adversarial neural networks (GANs)was utilized to conduct virtual spray simulations for conditions relevant to aero engine combustors. Themodel consists of two sub-modules: (i) an autoencoder converting the variable length droplet trajectories intofixed length, lower dimensional representations and (ii) a Wasserstein GAN that learns to mimic the latentrepresentations of the evaporating droplets along their lifetime. The GAN module was also conditioned withthe injection location and the diameters of the droplets to increase the generalizability of the whole framework.The training data was provided from highly resolved 3D, transient Eulerian–Lagrangian, large eddy simulationsconducted with OpenFOAM. Neural network models were created and trained within the open source machinelearning framework of PyTorch. Predictive capabilities of the proposed method was discussed with respect tospray statistics and evaporation dynamics. Results show that conditioned GAN models offer a great potentialas low order model approximations with high computational efficiency. Nonetheless, the capabilities of theautoencoder module to preserve local dependencies should be improved to realize this potential. For the currentcase study, the custom model architecture was capable of conducting the simulation in the order of secondsafter a day of training, which had taken one week on HPC with the conventional CFD approach for the samenumber of droplets (200,000 trajectories).展开更多
基金This work was supported by the National Natural Science Foundation of China(Nos.61972456,61402329)the Natural Science Foundation of Tianjin(Nos.19JCYBJC15400,21YDTPJC00440)。
文摘Quick Access Recorder(QAR),an important device for storing data from various flight parameters,contains a large amount of valuable data and comprehensively records the real state of the airline flight.However,the recorded data have certain missing values due to factors,such as weather and equipment anomalies.These missing values seriously affect the analysis of QAR data by aeronautical engineers,such as airline flight scenario reproduction and airline flight safety status assessment.Therefore,imputing missing values in the QAR data,which can further guarantee the flight safety of airlines,is crucial.QAR data also have multivariate,multiprocess,and temporal features.Therefore,we innovatively propose the imputation models A-AEGAN("A"denotes attention mechanism,"AE"denotes autoencoder,and"GAN"denotes generative adversarial network)and SA-AEGAN("SA"denotes self-attentive mechanism)for missing values of QAR data,which can be effectively applied to QAR data.Specifically,we apply an innovative generative adversarial network to impute missing values from QAR data.The improved gated recurrent unit is then introduced as the neural unit of GAN,which can successfully capture the temporal relationships in QAR data.In addition,we modify the basic structure of GAN by using an autoencoder as the generator and a recurrent neural network as the discriminator.The missing values in the QAR data are imputed by using the adversarial relationship between generator and discriminator.We introduce an attention mechanism in the autoencoder to further improve the capability of the proposed model to capture the features of QAR data.Attention mechanisms can maintain the correlation among QAR data and improve the capability of the model to impute missing data.Furthermore,we improve the proposed model by integrating a self-attention mechanism to further capture the relationship between different parameters within the QAR data.Experimental results on real datasets demonstrate that the model can reasonably impute the missing values in QAR data with excellent results.
文摘工作在复杂环境下的多元退化设备面临失效数据少、多源信息融合准确度低和监督学习数据不平衡等问题,对此本文提出一种基于时间序列生成对抗网络(Time-series Generative Adversarial Networks,TimeGAN)与单分类支持向量机(One-Class Support Vector Machine,OCSVM)组合模型的小子样数据增广方法.方法引入了TimeGAN模型拟合真实数据时间序列相关性,从而生成新的多元退化设备数据.本文提出了一种基于最大均值差异改进方法的可信度判据,避免强相关特征对生成数据质量评价的影响,通过使用T-分布随机邻近嵌入(T-distributed Stochastic Neighbor Embedding,T-SNE)和全局最大均值差异(Global Maximum Mean Discrepancy,GMMD)的组合方法,定性定量地评价生成数据的质量水平.基于训练后的OCSVM模型,对生成数据进行异常检测与剔除,进一步提高生成数据的质量.以航空发动机数据集C-MAPSS为例进行方法验证分析,通过与其他数据增强模型对比验证了所提方法的可行性和有效性.
基金supported by the National Natural Science Foundation of China(52122901,52079016)Fundamental Research Funds for the Central Universities(DUT21GJ203+1 种基金the UK Royal Society(Ref:IF160108 and IEC\NSFC\170249)sponsored by the China Scholarship Council(202106060094).
文摘Contamination events in water distribution networks(WDNs)can have a huge impact on water supply and public health;increasingly,online water quality sensors are deployed for real-time detection of contamination events.Machine learning has been used to integrate multivariate time series water quality data at multiple stations for contamination detection;however,accurate extraction of spatial features in water quality signals remains challenging.This study proposed a contamination detection method based on generative adversarial networks(GANs).The GAN model was constructed to simultaneously consider the spatial correlation between sensor locations and temporal information of water quality indicators.The model consists of two networksda generator and a discriminatordthe outputs of which are used to measure the degree of abnormality of water quality data at each time step,referred to as the anomaly score.Bayesian sequential analysis is used to update the likelihood of event occurrence based on the anomaly scores.Alarms are then generated from the fusion of single-site and multi-site models.The proposed method was tested on a WDN for various contamination events with different characteristics.Results showed high detection performance by the proposed GAN method compared with the minimum volume ellipsoid benchmark method for various contamination amplitudes.Additionally,the GAN method achieved high accuracy for various contamination events with different amplitudes and numbers of anomalous water quality parameters,and water quality data from different sensor stations,highlighting its robustness and potential for practical application to real-time contamination events.
文摘In this study, the probabilistic, data driven nature of the generative adversarial neural networks (GANs)was utilized to conduct virtual spray simulations for conditions relevant to aero engine combustors. Themodel consists of two sub-modules: (i) an autoencoder converting the variable length droplet trajectories intofixed length, lower dimensional representations and (ii) a Wasserstein GAN that learns to mimic the latentrepresentations of the evaporating droplets along their lifetime. The GAN module was also conditioned withthe injection location and the diameters of the droplets to increase the generalizability of the whole framework.The training data was provided from highly resolved 3D, transient Eulerian–Lagrangian, large eddy simulationsconducted with OpenFOAM. Neural network models were created and trained within the open source machinelearning framework of PyTorch. Predictive capabilities of the proposed method was discussed with respect tospray statistics and evaporation dynamics. Results show that conditioned GAN models offer a great potentialas low order model approximations with high computational efficiency. Nonetheless, the capabilities of theautoencoder module to preserve local dependencies should be improved to realize this potential. For the currentcase study, the custom model architecture was capable of conducting the simulation in the order of secondsafter a day of training, which had taken one week on HPC with the conventional CFD approach for the samenumber of droplets (200,000 trajectories).
