A Convolutional Autoencoder Based Fault Detection Method for Metro Railway Turnout

Railway turnout is one of the critical equipment of Switch&Crossing(S&C)Systems in railway,related to the train’s safety and operation efficiency.With the advancement of intelligent sensors,data-driven fault detection technology for railway turnout has become an important research topic.However,little research in the literature has investigated the capability of data-driven fault detection technology for metro railway turnout.This paper presents a convolutional autoencoder-based fault detection method for the metro railway turnout considering human field inspection scenarios.First,the one-dimensional original time-series signal is converted into a twodimensional image by data pre-processing and 2D representation.Next,a binary classification model based on the convolutional autoencoder is developed to implement fault detection.The profile and structure information can be captured by processing data as images.The performance of our method is evaluated and tested on real-world operational current data in themetro stations.Experimental results show that the proposedmethod achieves better performance,especially in terms of error rate and specificity,and is robust in practical engineering applications.

Robust Deep 3D Convolutional Autoencoder for Hyperspectral Unmixing with Hypergraph Learning

Hyperspectral unmixing aims to acquire pure spectra of distinct substances(endmembers)and fractional abundances from highly mixed pixels.In this paper,a deep unmixing network framework is designed to deal with the noise disturbance.It contains two parts:a three⁃dimensional convolutional autoencoder(denoising 3D CAE)which recovers data from noised input,and a restrictive non⁃negative sparse autoencoder(NNSAE)which incorporates a hypergraph regularizer as well as a l2,1⁃norm sparsity constraint to improve the unmixing performance.The deep denoising 3D CAE network was constructed for noisy data retrieval,and had strong capacity of extracting the principle and robust local features in spatial and spectral domains efficiently by training with corrupted data.Furthermore,a part⁃based nonnegative sparse autoencoder with l2,1⁃norm penalty was concatenated,and a hypergraph regularizer was designed elaborately to represent similarity of neighboring pixels in spatial dimensions.Comparative experiments were conducted on synthetic and real⁃world data,which both demonstrate the effectiveness and robustness of the proposed network.

Bridge damage identification based on convolutional autoencoders and extreme gradient boosting trees

To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.

Characteristic extraction of soliton dynamics based on convolutional autoencoder neural network

In this article,we use a convolutional autoencoder neural network to reduce data dimensioning and rebuild soliton dynamics in a passively mode-locked fiber laser.Based on the particle characteristic in double solitons and triple solitons interactions,we found that there is a strict correspondence between the number of minimum compression parameters and the number of independent parameters of soliton interaction.This shows that our network effectively coarsens the high-dimensional data in nonlinear systems.Our work not only introduces new prospects for the laser self-optimization algorithm,but also brings new insights into the modeling of nonlinear systems and description of soliton interactions.

Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network

Plants are susceptive to various diseases in their growing phases.Early detection of diseases in plants is one of the most challenging problems in agriculture.If the diseases are not identified in the early stages,then theymay adversely affect the total yield,resulting in a decrease in the farmers'profits.To overcome this problem,many researchers have presented different state-of-the-art systems based on Deep Learning and Machine Learning approaches.However,most of these systems either use millions of training parameters or have lowclassification accuracies.This paper proposes a novel hybrid model based on Convolutional Autoencoder(CAE)network and Convolutional Neural Network(CNN)for automatic plant disease detection.To the best of our knowledge,a hybrid system based on CAE and CNN to detect plant diseases automatically has not been proposed in any state-ofthe-art systems present in the literature.In this work,the proposed hybrid model is applied to detect Bacterial Spot disease present in peach plants using their leaf images,however,it can be used for any plant disease detection.The experiments performed in this paper use a publicly available dataset named PlantVillage to get the leaf images of peach plants.The proposed system achieves 99.35%training accuracy and 98.38%testing accuracy using only 9,914 training parameters.The proposed hybrid model requires lesser number of training parameters as compared to other approaches existing in the literature.This,in turn,significantly decreases the time required to train the model for automatic plant disease detection and the time required to identify the disease in plants using the trained model.

Deep anomaly detection in horizontal axis wind turbines using GraphConvolutional Autoencoders for Multivariate Time series

Wind power is one of the fastest-growing renewable energy sectors instrumental in the ongoing decarbonizationprocess. However, wind turbines are subjected to a wide range of dynamic loads which can cause more frequentfailures and downtime periods, leading to ever-increasing attention to effective Condition Monitoring strategies.In this paper, we propose a novel unsupervised deep anomaly detection framework to detect anomalies in windturbines based on SCADA data. We introduce a promising neural architecture, namely a Graph ConvolutionalAutoencoder for Multivariate Time series, to model the sensor network as a dynamical functional graph. Thisstructure improves the unsupervised learning capabilities of Autoencoders by considering individual sensormeasurements together with the nonlinear correlations existing among signals. On this basis, we developeda deep anomaly detection framework that was validated on 12 failure events occurred during 20 months ofoperation of four wind turbines. The results show that the proposed framework successfully detects anomaliesand anticipates SCADA alarms by outperforming other two recent neural approaches.

Network Intrusion Detection in Internet of Blended Environment Using Ensemble of Heterogeneous Autoencoders(E-HAE)

Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or data.The growing use of Internet of Things(IoT)technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation,as it facilitates multiple new attack vectors to emerge effortlessly.As such,existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems.To address this problem,we designed a blended threat detection approach,considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence.We collectively refer to the convergence of different technology sectors as the internet of blended environment.The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder.An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02%detection accuracy.Furthermore,performance of the proposed approach was compared with various single model(autoencoder)-based network intrusion detection approaches:autoencoder,variational autoencoder,convolutional variational autoencoder,and long short-term memory variational autoencoder.The proposed model outperformed all compared models,demonstrating F1-score improvements of 4.99%,2.25%,1.92%,and 3.69%,respectively.

Wafer map defect patterns classification based on a lightweight network and data augmentation

Accurately identifying defect patterns in wafer maps can help engineers find abnormal failure factors in production lines.During the wafer testing stage,deep learning methods are widely used in wafer defect detection due to their powerful feature extraction capa-bilities.However,most of the current wafer defect patterns classification models have high complexity and slow detection speed,which are difficult to apply in the actual wafer production process.In addition,there is a data imbalance in the wafer dataset that seriously affects the training results of the model.To reduce the complexity of the deep model without affecting the wafer feature expression,this paper adjusts the structure of the dense block in the PeleeNet network and proposes a lightweight network WM‐PeleeNet based on the PeleeNet module.In addition,to reduce the impact of data imbalance on model training,this paper proposes a wafer data augmentation method based on a convolutional autoencoder by adding random Gaussian noise to the hidden layer.The method proposed in this paper has an average accuracy of 95.4%on the WM‐811K wafer dataset with only 173.643 KB of the parameters and 316.194 M of FLOPs,and takes only 22.99 s to detect 1000 wafer pictures.Compared with the original PeleeNet network without optimization,the number of parameters and FLOPs are reduced by 92.68%and 58.85%,respectively.Data augmentation on the minority class wafer map improves the average classification accuracy by 1.8%on the WM‐811K dataset.At the same time,the recognition accuracy of minority classes such as Scratch pattern and Donut pattern are significantly improved.

Effective Denoising Architecture for Handling Multiple Noises

Object detection,one of the core research topics in computer vision,is extensively used in various industrial activities.Although there have been many studies of daytime images where objects can be easily detected,there is relatively little research on nighttime images.In the case of nighttime,various types of noises,such as darkness,haze,and light blur,deteriorate image quality.Thus,an appropriate process for removing noise must precede to improve object detection performance.Although there are many studies on removing individual noise,only a few studies handle multiple noises simultaneously.In this paper,we pro-pose a convolutional denoising autoencoder(CDAE)-based architecture trained on various types of noises.We also present various composing modules for each noise to improve object detection performance for night images.Using the exclusively dark(ExDark)Image dataset,experimental results show that the Sequentialfiltering architecture showed superior mean average precision(mAP)compared to other architectures.

Half space object classification via incident angle based fusion of radar and infrared sensors

In this paper,we introduce an incident angle based fusion method for radar and infrared sensors to improve the recognition rate of complex targets under half space scenarios,e.g.,vehicles on the ground in this paper.For radar sensors,convolutional operation is introduced into the autoencoder,a“winner-take-all(WTA)”convolutional autoencoder(CAE)is used to improve the recognition rate of the radar high resolution range profile(HRRP).Moreover,different from the free space,the HRRP in half space is more complex.In order to get closer to the real situation,the half space HRRP is simulated as the dataset.The recognition rate has a growth more than 7%com-pared with the traditional CAE or denoised sparse autoencoder(DSAE).For infrared sensor,a convolutional neural network(CNN)is used for infrared image recognition.Finally,we com-bine the two results with the Dempster-Shafer(D-S)evidence theory,and the discounting operation is introduced in the fusion to improve the recognition rate.The recognition rate after fusion has a growth more than 7%compared with a single sensor.After the discounting operation,the accuracy rate has been improved by 1.5%,which validates the effectiveness of the proposed method.

3D convolutional selective autoencoder for instability detection in combustion systems

While analytical solutions of critical(phase)transitions in dynamical systems are abundant for simple nonlinear systems,such analysis remains intractable for real-life dynamical systems.A key example is thermoacoustic insta-bility in combustion,where prediction or early detection of the onset of instability is a hard technical challenge,which needs to be addressed to build safer and more energy-efficient gas turbine engines powering aerospace and energy industries.The instabilities arising in combustion chambers of engines are mathematically too complex to model.To address this issue in a data-driven manner instead,we propose a novel deep learning architecture called 3D convolutional selective autoencoder(3D-CSAE)to detect the evolution of self-excited oscillations using spatiotemporal data,i.e.,hi-speed videos taken from a swirl-stabilized combustor(laboratory surrogate of gas turbine engine combustor).3D-CSAE consists of filters to learn,in a hierarchical fashion,the complex visual and dynamic features related to combustion instability from the training videos(i.e.,two spatial dimensions for the image frames and the third dimension for time).We train the 3D-CSAE on frames of videos obtained from a limited set of operating conditions.We select the 3D-CSAE hyper-parameters that are effective for characterizing hierarchical and multiscale instability structure evolution by utilizing the dynamic information available in the video.The proposed model clearly shows performance improvement in detecting the precursors and the onset of instability.The machine learning-driven results are verified with physics-based off-line measures.Advanced active control mechanisms can directly leverage the proposed online detection capability of 3D-CSAE to mitigate the adverse effects of combustion instabilities on the engine operating under various stringent requirements and conditions.

A deep learning approach for spatial error correction of numerical seasonal weather prediction simulation data

Numerical Weather Prediction(NWP)simulations produce meteorological data in various spatial and temporal scales,depending on the application requirements.In the current study,a deep learning approach,based on convolutional autoencoders,is explored to effectively correct the error of the NWP simulation.An undercomplete convolutional autoencoder(CAE)is applied as part of the dynamic error correction of NWP data.This work is an attempt to improve the seasonal forecast(3-6 months ahead)data accuracy for Greece using a global reanalysis dataset(that incorporates observations,satellite imaging,etc.)of higher spatial resolution.More specifically,the publically available Meteo France Seasonal(Copernicus platform)and the National Centers for Environmental Prediction(NCEP)Final Analysis(FNL)(NOAA)datasets are utilized.In addition,external information is used as evidence transfer,concerning the time conditions(month,day,and season)and the simulation characteristics(initialization of simulation).It is found that convolutional autoencoders help to improve the resolution of the seasonal data and successfully reduce the error of the NWP data for 6-months ahead forecasting.Interestingly,the month evidence yields the best agreement indicating a seasonal dependence of the performance.

Unsupervised noise-robust feature extraction for aerial image classification

The rich data provided by satellites and unmanned aerial vehicles bring opportunities to directly model aerial image features by extracting their spatial and structural patterns.Although convolutional autoencoders(CAEs)have been attained a remarkable performance in ideal aerial image feature extraction,they are still challenging to extract information from noisy images which are generated from capture and transmission.In this paper,a novel CAE-based noise-robust unsupervised learning method is proposed for extracting high-level features accurately from aerial images and mitigating the effect of noise.Different from conventional CAEs,the proposed method introduces the noise-robust module between the encoder and the decoder.Besides,several pooling layers in CAEs are replaced by convolutional layers with stride=2.The performance of feature extraction is evaluated by the prediction accuracy and the accuracy loss in image classification experiments.A 5-classes aerial optical scene and a 9-classes hyperspectral image(HSI)data set are utilized for optical image and HSI feature extraction,respectively.Highlevel features extracted from aerial images are utilized for image classification by a linear support vector machine(SVM)classifier.Experimental results indicate that the proposed method improves the classification accuracy for noisy images(Gaussian noise 2Dσ=0.1,3Dσ=60)in both optical images(2D 87.5%)and HSIs(3D 85.6%)compared with the traditional CAE(2D 78.6%,3D 84.2%).The accuracy loss in classification experiments increases with the increment of noise.Compared with the traditional CAE(2D 15.7%,3D 11.8%),the proposed method shows the lower classification accuracy loss in experiments(2D 0.3%,3D 6.3%).The proposed unsupervised noise-robust feature extraction method attains desirable classification accuracy in ideal input and enhances the feature extraction capability from noisy input.

A novel unsupervised deep learning method for the generalization of urban form

Accurate delineation of urban form is essential to understand the impacts that urbanization has on the environment and regional climate.Conventional supervised classification of urban form requires a rigidly defined scheme and high-quality sample data with class labels.Due to the complexity of urban systems,it is challenging to consistently define urban form types and collect metadata to describe them.Therefore,in this study,we propose a novel unsupervised deep learning method for urban form delineation while avoiding the limitations of conventional super-vised urban form classification methods.The novelty of the proposed method is the Multiscale Residual Convolutional Autoencoder(MRCAE),which can learn the latent representation of differ-ent urban form types.These vectors can be further used to generalize urban form types by using Self-Organizing Map(SOM)and the Gaussian Mixture Model(GMM).The proposed method is applied in the metropolitan area of Guangzhou-Foshan,China.The MRCAE model along with SOM and GMM is used to generalize the urban form types from satellite images.The physical and functional properties of each urban form type are also analyzed using several auxiliary datasets,including building footprints,Points-of-Interests(POIs)and Tencent User Density(TUD)data.The results reveal that the urban form map generated based on the MRCAE can explain 55%of the building height distribution and 55%of the building area distribution,which are 2.1%and 3.3%higher than those derived from the conventional convolutional autoencoder.As the information of urban form is essential to urban climate models,the results presented in this study can become a basis to refine the quantification of urban climate parameters,thereby introducing the urban heterogeneity to help understand the climate response of future urbanization.

A hybrid deep neural network based prediction of 300 MW coalfired boiler combustion operation condition

In power generation industries,boilers are required to be operated under a range of different conditions to accommodate demands for fuel randomness and energy fluctuation.Reliable prediction of the combustion operation condition is crucial for an in-depth understanding of boiler performance and maintaining high combustion efficiency.However,it is difficult to establish an accurate prediction model based on traditional data-driven methods,which requires prior expert knowledge and a large number of labeled data.To overcome these limitations,a novel prediction method for the combustion operation condition based on flame imaging and a hybrid deep neural network is proposed.The proposed hybrid model is a combination of convolutional sparse autoencoder(CSAE)and least support vector machine(LSSVM),i.e.,CSAE-LSSVM,where the convolutional sparse autoencoder with deep architectures is utilized to extract the essential features of flame image,and then essential features are input into the least support vector machine for operation condition prediction.A comprehensive investigation of optimal hyper-parameter and dropout technique is carried out to improve the performance of the CSAE-LSSVM.The effectiveness of the proposed model is evaluated by 300 MW tangential coal-fired boiler flame images.The prediction accuracy of the proposed hybrid model reaches 98.06%,and its prediction time is 3.06 ms/image.It is observed that the proposed model could present a superior performance in comparison to other existing neural network models.