Data Cleaning Based on Stacked Denoising Autoencoders and Multi-Sensor Collaborations

Wireless sensor networks are increasingly used in sensitive event monitoring.However,various abnormal data generated by sensors greatly decrease the accuracy of the event detection.Although many methods have been proposed to deal with the abnormal data,they generally detect and/or repair all abnormal data without further differentiate.Actually,besides the abnormal data caused by events,it is well known that sensor nodes prone to generate abnormal data due to factors such as sensor hardware drawbacks and random effects of external sources.Dealing with all abnormal data without differentiate will result in false detection or missed detection of the events.In this paper,we propose a data cleaning approach based on Stacked Denoising Autoencoders(SDAE)and multi-sensor collaborations.We detect all abnormal data by SDAE,then differentiate the abnormal data by multi-sensor collaborations.The abnormal data caused by events are unchanged,while the abnormal data caused by other factors are repaired.Real data based simulations show the efficiency of the proposed approach.

Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders

Invoice document digitization is crucial for efficient management in industries.The scanned invoice image is often noisy due to various reasons.This affects the OCR(optical character recognition)detection accuracy.In this paper,letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method.A stacked denoising autoencoder(SDAE)is implemented with two hidden layers each in encoder network and decoder network.In order to capture the most salient features of training samples,a undercomplete autoencoder is designed with non-linear encoder and decoder function.This autoencoder is regularized for denoising application using a combined loss function which considers both mean square error and binary cross entropy.A dataset consisting of 59,119 letter images,which contains both English alphabets(upper and lower case)and numbers(0 to 9)is prepared from many scanned invoices images and windows true type(.ttf)files,are used for training the neural network.Performance is analyzed in terms of Signal to Noise Ratio(SNR),Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Universal Image Quality Index(UQI)and compared with other filtering techniques like Nonlocal Means filter,Anisotropic diffusion filter,Gaussian filters and Mean filters.Denoising performance of proposed SDAE is compared with existing SDAE with single loss function in terms of SNR and PSNR values.Results show the superior performance of proposed SDAE method.

Fault prediction of combine harvesters based on stacked denoising autoencoders

Accurate fault prediction is essential to ensure the safety and reliability of combine harvester operation.In this study,a combine harvester fault prediction method based on a combination of stacked denoising autoencoders(SDAE)and multi-classification support vector machines(SVM)is proposed to predict combine harvester faults by extracting operational features of key combine components.In general,SDAE contains autoencoders and uses a deep network architecture to learn complex non-linear input-output relationships in a hierarchical manner.Selected features are fed into the SDAE network,deep-level features of the input parameters are extracted by SDAE,and an SVM classifier is then added to its top layer to achieve combine harvester fault prediction.The experimental results show that the method can achieve accurate and efficient combine harvester fault prediction.In particular,the experiments used Gaussian noise with a distribution center of 0.05 to corrupt the test data samples obtained by random sampling of the whole population,and the results showed that the prediction accuracy of the method was 95.31%,which has better robustness and generalization ability compared to SVM(77.03%),BP(74.61%),and SAE(90.86%).

A Hybrid Data-Driven Approach for Predicting Remaining Useful Life of Industrial Equipment

Guaranteeing the safety of equipment is extremely important in industry.To improve reliability and availability of equipment,various methods for prognostics and health management(PHM)have been proposed.Predicting remaining useful life(RUL)of industrial equipment is a key aspect of PHM and it is always one of the most challenging issues.With the rapid development of industrial equipment and sensing technology,an increasing amount of data on the health level of equipment can be obtained for RUL prediction.This paper proposes a hybrid data-driven approach based on stacked denoising autoencode(SDAE)and similarity theory for estimating remaining useful life of industrial equipment,which is named RULESS.Our work is making the most of stacked SDAE and similarity theory to improve the accuracy of RUL prediction.The effectiveness of the proposed approach was evaluated by using aircraft engine health data simulated by commercial modular Aero-Propulsion system simulation(C-MAPSS).