A Fault Diagnosis Model for Coaxial-Rotor Unit Using Bidirectional Gate Recurrent Unit and Highway Network

A turbojet engine is the most significant part of an Internal Combustion Engine (ICE) for Hybrid Electric Vehicles. Specifically, the coaxial-rotor unit is the key component, whose performance largely affects the working efficiency. Thereby, the fault diagnosis methods for coaxial-rotor units is a main focus. In line with our test results, the bearing circlip is the most vulnerable element while rotating. Moreover, the low-speed rotating fault diagnosis is even challenging for current methods. Since the fault diagnosis on the bearing circlip of coaxial-rotor units is absent, this paper establishes a test rig on a running coaxial-rotor unit under different working conditions. The three-directional vibration signals are collected and analyzed to demonstrate the working states. On the task of bearing circlip failure classification, a deep-learning-based model using the Bidirectional Gate Recurrent Unit and the Highway Network is developed, which is capable of capturing hidden features and removing unrelated information. For working performance evaluation, experiments on the data of different rotating speeds are carried out. Among all the fault diagnosis methods, our model is the best approach and achieves an average accuracy of 99.4%. The encouraging results reveal that the proposed model is effective in both the high-speed and low-speed fault diagnosis of bearing circlip malfunction

Human Walking Pattern Recognition Based on KPCA and SVM with Ground Reflex Pressure Signal

Algorithms based on the ground reflex pressure (GRF) signal obtained from a pair of sensing shoes for human walking pattern recognition were investigated. The dimensionality reduction algorithms based on principal component analysis (PCA) and kernel principal component analysis (KPCA) for walking pattern data compression were studied in order to obtain higher recognition speed. Classifiers based on support vector machine (SVM), SVM-PCA, and SVM-KPCA were designed, and the classification performances of these three kinds of algorithms were compared using data collected from a person who was wearing the sensing shoes. Experimental results showed that the algorithm fusing SVM and KPCA had better recognition performance than the other two methods. Experimental outcomes also confirmed that the sensing shoes developed in this paper can be employed for automatically recognizing human walking pattern in unlimited environments which demonstrated the potential application in the control of exoskeleton robots

Coupling of two-phase flow in fractured-vuggy reservoir with filling medium

Caves in fractured-vuggy reservoir usually contain lots of filling medium, so the two-phase flow in formations is the coupling of free flow and porous flow, and that usually leads to low oil recovery. Considering geological interpretation results, the physical filled cave models with different filling mediums are designed. Through physical experiment, the displacement mechanism between un-filled areas and the filling medium was studied. Based on the experiment model, we built a mathematical model of laminar two-phase coupling flow considering wettability of the porous media. The free fluid region was modeled using the Navier-Stokes and Cahn-Hilliard equations, and the two-phase flow in porous media used Darcy's theory. Extended BJS conditions were also applied at the coupling interface. The numerical simulation matched the experiment very well, so this numerical model can be used for two-phase flow in fracture-vuggy reservoir. In the simulations, fluid flow between inlet and outlet is free flow, so the pressure difference was relatively low compared with capillary pressure. In the process of water injection, the capillary resistance on the surface of oil-wet filling medium may hinder the oil-water gravity differentiation, leading to no fluid exchange on coupling interface and remaining oil in the filling medium. But for the water-wet filling medium, capillary force on the surface will coordinate with gravity. So it will lead to water imbibition and fluid exchange on the interface, high oil recovery will finally be reached at last

A Generative Adversarial Network for Pixel-Scale Lunar DEM Generation from High-Resolution Monocular Imagery and Low-Resolution DEM

Digital elevation models (DEMs) provide fundamental data for scientific and engineering applications in lunar exploration missions. Lunar DEMs have been mainly generated by laser altimetry and stereophotogrammetry. Complementarity to stereo photogrammetry, reflection-based surface reconstruction methods, such as shape from shading (SFS), have been studied and applied in lunar DEM reconstruction from a single image. However, this method often suffers from solution ambiguity and instability. In this paper, we propose a generative adversarial network (GAN)-based method that is able to generate high-resolution pixel-scale DEMs from a single image aided by a low-resolution DEM. We have evaluated the accuracy of the reconstructed high-resolution DEMs from 25 LROC NAC images of four regions using LROC NAC DEMs (2 m/pixel) as ground truth. The experimental results demonstrate good accuracy and adaptability to changes in illumination conditions. The root mean square error (RMSE) can reach about 2 m in areas where the degree of elevation variation is less than 100 m, and the RMSE value ranges from around 3 m to 10 m without considering the degree of the elevation variation in large-area reconstruction. As high-resolution monocular images and low-resolution DEMs are available for the entire lunar surface, the proposed GAN-based method has great potential in high-resolution lunar DEM reconstruction for lunar mapping applications

A physics-informed autoencoder for system health state assessment based on energy-oriented system performance

International audienc

A Generative Adversarial Network for Pixel-Scale Lunar DEM Generation from High-Resolution Monocular Imagery and Low-Resolution DEM

Digital elevation models (DEMs) provide fundamental data for scientific and engineering applications in lunar exploration missions. Lunar DEMs have been mainly generated by laser altimetry and stereophotogrammetry. Complementarity to stereo photogrammetry, reflection-based surface reconstruction methods, such as shape from shading (SFS), have been studied and applied in lunar DEM reconstruction from a single image. However, this method often suffers from solution ambiguity and instability. In this paper, we propose a generative adversarial network (GAN)-based method that is able to generate high-resolution pixel-scale DEMs from a single image aided by a low-resolution DEM. We have evaluated the accuracy of the reconstructed high-resolution DEMs from 25 LROC NAC images of four regions using LROC NAC DEMs (2 m/pixel) as ground truth. The experimental results demonstrate good accuracy and adaptability to changes in illumination conditions. The root mean square error (RMSE) can reach about 2 m in areas where the degree of elevation variation is less than 100 m, and the RMSE value ranges from around 3 m to 10 m without considering the degree of the elevation variation in large-area reconstruction. As high-resolution monocular images and low-resolution DEMs are available for the entire lunar surface, the proposed GAN-based method has great potential in high-resolution lunar DEM reconstruction for lunar mapping applications

A Fault Diagnosis Approach for Electromechanical Actuators with Simulating Model under Small Experimental Data Sample Condition

International audienceElectromechanical actuators (EMAs) have shown a high efficiency in flight surface control with the development of more electric aircraft. In order to identify the abnormalities and potential failures of EMA, a methodology for fault diagnosis is developed. A simulating model of EMA is first built to perform different working states. Based on the modeling of EMA, the corresponding faults are then simulated to re-generate the fault data. Afterwards, a gated recurrent unit (GRU) and co-attention-based fault diagnosis approach is proposed to classify the working states of EMA. Experiments are conducted and a satisfying classification accuracy on simulated data is obtained. Furthermore, fault diagnosis on an actual working system is performed. The experimental results demonstrate that the proposed method has a high efficiency

Nuclear translocation of HIF-1α induced by influenza A (H1N1) infection is critical to the production of proinflammatory cytokines

Infection with the influenza A (H1N1) virus is a major challenge for public health because it can cause severe morbidity and even mortality in humans. The over-secretion of inflammatory cytokines (cytokine storm) is considered to be a key contributor to the severe pneumonia caused by H1N1 infection. It has been reported that hypoxia-inducible factor 1-alpha (HIF-1α) is associated with the production of proinflammatory molecules, but whether HIF-1α participates in the acute inflammatory responses against H1N1 infection is still unclear. To investigate the role of HIF-1α in H1N1 infection, the expression and nuclear translocation of HIF-1α in A549 and THP-1 cell lines infected with H1N1 virus were observed. The results showed that without altering the intracellular mRNA or protein expression of HIF-1α, H1N1 infection only induced nuclear translocation of HIF-1α under normal oxygen concentrations. The use of 2-methoxyestradiol (2ME2), a HIF-1α inhibitor that blocks HIF-1α nuclear accumulation, in H1N1-infected cells decreased the mRNA and protein expression of tumor necrosis factor-alpha (TNF-α) and interleukin (IL)-6 and increased the levels of IL-10. In contrast, H1N1-infected cells under hypoxic conditions had increased HIF-1α nuclear accumulation, increased expression of TNF-α and IL-6 and decreased levels of IL-10. In conclusion, our data implied that in vitro H1N1 infection induced nuclear translocation of HIF-1α without altering the expression of HIF-1α, which may promote the secretion of proinflammatory cytokines during H1N1 infection.Emerging Microbes & Infections (2017) 6, e39; doi:10.1038/emi.2017.21; published online 24 May 201

Localization of the Chang’e-5 Lander Using Radio-Tracking and Image-Based Methods

Chang’e-5, China’s first unmanned lunar sample-return mission, was successfully landed in Northern Oceanus Procellarum on 1 December 2020. Determining the lander location precisely and timely is critical for both engineering operations and subsequent scientific research. Localization of the lander was performed using radio-tracking and image-based methods. The lander location was determined to be (51.92°W, 43.06°N) by both methods. Other localization results were compared for cross-validation. The localization results greatly contributed to the planning of the ascender lifting off from the lander and subsequent maneuvers, and they will contribute to scientific analysis of the returned samples and in situ acquired data