A DPCA-based online fault indicator for gear faults using three-direction vibration signals

For online monitoring and identifying gear faults, a new fault indicator is proposed based on a multivariate statistical technique, dynamic principal component analysis (DPCA), under variable load conditions. In this method, a tri-axial vibration sensor is used to acquire the 3-direction vibration signals of gear in the gear box because it can pick up more abundant fault information than a single axis sensor does. By monitoring the value of the fault indicator, the running state of the gear (normal condition or faults) can be directly identified according to the set thresholds without using any other fault classification methods. To verify the effectiveness, the proposed method is applied on the QPZZ-II rotating machinery fault simulation rig in which the root crack and the tooth broken faults are introduced into the gearbox’s driving gear. Experimental results show that the fault indicator not only can effectively reveal the health state of the gear, but also is without being influenced by the load fluctuation. And, the accuracy rate of fault diagnosis is over 96 %

Modeling Viscoelastomers With Nonlinear Viscosity

Consisting of highly mobile and flexible polymer chains, elastomers are known to exhibit viscoelastic behavior. Adopting concepts from the theory of polymer dynamics and finite-deformation viscoelasticity, this work presents a micromacro constitutive model to investigate the viscoelastic behavior of elastomers, in which the material viscosity varies with the macroscopic deformation. The developed model is then applied to study the stress response of elastomers. From the simulation results, it is observed that the developed model exhibits strong capability of capturing the typical response behaviors of elastomers (e.g., strain-softening behavior). A comparison of the stress responses between linear and nonlinear viscosity is also considered in this work. The modeling framework in this paper is expected to provide a general approach and a platform to analyze the viscoelastic behavior of rubber-like materials with nonlinear viscosity

The roller bearing fault diagnosis methods with harmonic wavelet packet and multi-classification relevance vector machine

Roller bearings are widely used elements in rotary machines. How to monitor the working conditions of roller bearings are focus study in the world. Monitoring the vibration signals of roller bearings is important indirect mean for that they reveal the characteristics and feature of roller bearing faults. Therefore, monitor the vibration signals and diagnose the working states of roller bearings are widely used to ensure the safety operation of the machines. This paper studies a novel roller bearing faults discrimination method with harmonic wavelet packet and Multi-classification Relevance Vector Machine (MRVM). Indeed, the fault discrimination is a pattern recognition process including feature extraction and faulty patterns recognition. Therefore, this paper collects vibration signals and decomposes them with harmonic wavelet packet. After the wavelet coefficients of each node are available, compute the vector energy by corresponding coefficients. The feature vector is prepared after the vector energy has been standardized. With MRVM, the paper proposes three fault discrimination methods in order to identify good bearing, bearing with inner race fault, bearing with outer race fault and bearing with roller fault. The Decision Tree (DT) model, One Against Rest (OAR) model and One Against One (OAO) model are used to propose the classification methods respectively. The proposed OAO model is simplified in order to improve the computation efficiency and simplify the architecture of the model. Finally, capture the vibration signal from the roller bearing stand of electric engineering lab and the roller bearing fault simulation stand QPZZ-II to illustrate the proposed methods. The proposed feature extraction method with harmonic wavelet packet is compared with conventional wavelet packet. With the previous feature vectors, the accuracy and efficiency of the three fault discrimination methods are compared. The accuracy and efficiency of three fault discrimination methods are compared under different conditions including developing faults, noise involving and several faults developing simultaneously. Experiment results show that the proposed feature extraction method is more effective than conventional method and the simplified OAO-RVM model possess the best fault discrimination accuracy and DT-RVM model possesses the better computation efficiency

Condition trend prediction of aero-generator based on particle swarm optimization and fuzzy integral

In order to improve and enhance the prediction accuracy and efficiency of aero-generator running trend, grasp its running condition, and avoid accidents happening, in this paper, auto-regressive and moving average model (ARMA) and least squares support vector machine (LSSVM) which are used to predict its running trend have been optimized using particle swarm optimization (PSO) based on using features found in real aero-generator life test, which lasts a long period of time on specialized test platform and collects mass data that reflects aero-generator characteristics, to build new models of PSO-ARMA and PSO-LSSVM. And we use fuzzy integral methodology to carry out decision fusion of the predicted results of these two new models. The research shows that the prediction accuracy of PSO-ARMA and PSO-LSSVM has been much improved on that of ARMA and LSSVM, and the results of decision fusion based on fuzzy integral methodology show further substantial improvement in accuracy than each particle swarm optimized model. Conclusion can be drawn that the optimized model and the decision fusion method presented in this paper are available in aero-generator condition trend prediction and have great value of engineering application

UMIFormer: Mining the Correlations between Similar Tokens for Multi-View 3D Reconstruction

In recent years, many video tasks have achieved breakthroughs by utilizing the vision transformer and establishing spatial-temporal decoupling for feature extraction. Although multi-view 3D reconstruction also faces multiple images as input, it cannot immediately inherit their success due to completely ambiguous associations between unstructured views. There is not usable prior relationship, which is similar to the temporally-coherence property in a video. To solve this problem, we propose a novel transformer network for Unstructured Multiple Images (UMIFormer). It exploits transformer blocks for decoupled intra-view encoding and designed blocks for token rectification that mine the correlation between similar tokens from different views to achieve decoupled inter-view encoding. Afterward, all tokens acquired from various branches are compressed into a fixed-size compact representation while preserving rich information for reconstruction by leveraging the similarities between tokens. We empirically demonstrate on ShapeNet and confirm that our decoupled learning method is adaptable for unstructured multiple images. Meanwhile, the experiments also verify our model outperforms existing SOTA methods by a large margin. Code will be available at https://github.com/GaryZhu1996/UMIFormer.Comment: Accepted by ICCV 202

Boosting with intranasal dendrimeric Aβ1–15 but not Aβ1–15 peptide leads to an effective immune response following a single injection of Aβ1–40/42 in APP-tg mice

BACKGROUND: Immunotherapy for Alzheimer's disease (AD) is emerging as a potential treatment. However, a clinical trial (AN1792) was halted after adverse effects occurred in a small subset of subjects, which may have been caused by a T cell-mediated immunological response. In general, aging limits the humoral immune response, therefore, immunogens and vaccination regimes are required that induce a strong antibody response with less potential for an adverse immune response. METHOD: In the current study, we immunized both wildtype and J20 APP-tg mice with a priming injection of Aβ1–40/42, followed by multiple intranasal boosts with the novel immunogen dAβ1–15 (16 copies of Aβ1–15 on a lysine tree), Aβ1–15 peptide or Aβ1–40/42 full length peptide. RESULTS: J20 APP-tg mice primed with Aβ1–40/42 subcutaneously and subsequently boosted intranasally with Aβ1–15 peptide did not generate a cellular or humoral immune response. In contrast, J20 APP-tg mice boosted intranasally with dAβ1–15 or full length Aβ1–40/42 produced high levels of anti-Aβ antibodies. Splenocyte proliferation was minimal in mice immunized with dAβ1–15. Wildtype littermates of the J20 APP-tg mice produced higher amounts of anti-Aβ antibodies compared to APP-tg mice but also had low T cell proliferation. The anti-Aβ antibodies were mainly composed of IgG2b and directed to an epitope within the Aβ1–7 region, regardless of the immunogen. Examination of the brain showed a significant reduction in Aβ plaque burden in the J20 APP-tg mice producing antibodies compared to controls. Biochemically, Aβ40 or Aβ42 were also reduced in brain homogenates and elevated in plasma but the changes did not reach significance. CONCLUSION: Our results demonstrate that priming with full length Aβ40/42 followed by boosting with dAβ1–15 but not Aβ1–15 peptide led to a robust humoral immune response with a minimal T cell response in J20 APP-tg mice. In addition, Aβ plaque burden was reduced in mice producing anti-Aβ antibodies. Interestingly, wildtype mice produced higher levels of anti-Aβ antibodies, indicating that immune tolerance may be present in J20 APP-tg mice. Together, these data suggest that dAβ1–15 but not Aβ1–15 peptide may be useful as a boosting immunogen in an AD vaccination regime

GARNet: Global-Aware Multi-View 3D Reconstruction Network and the Cost-Performance Tradeoff

Deep learning technology has made great progress in multi-view 3D reconstruction tasks. At present, most mainstream solutions establish the mapping between views and shape of an object by assembling the networks of 2D encoder and 3D decoder as the basic structure while they adopt different approaches to obtain aggregation of features from several views. Among them, the methods using attention-based fusion perform better and more stable than the others, however, they still have an obvious shortcoming -- the strong independence of each view during predicting the weights for merging leads to a lack of adaption of the global state. In this paper, we propose a global-aware attention-based fusion approach that builds the correlation between each branch and the global to provide a comprehensive foundation for weights inference. In order to enhance the ability of the network, we introduce a novel loss function to supervise the shape overall and propose a dynamic two-stage training strategy that can effectively adapt to all reconstructors with attention-based fusion. Experiments on ShapeNet verify that our method outperforms existing SOTA methods while the amount of parameters is far less than the same type of algorithm, Pix2Vox++. Furthermore, we propose a view-reduction method based on maximizing diversity and discuss the cost-performance tradeoff of our model to achieve a better performance when facing heavy input amount and limited computational cost

In vivo clonal expansion and phenotypes of hypocretin-specific CD4(+) T cells in narcolepsy patients and controls

Individuals with narcolepsy suffer from abnormal sleep patterns due to loss of neurons that uniquely supply hypocretin (HCRT). Previous studies found associations of narcolepsy with the human leukocyte antigen (HLA)-DQ6 allele and T-cell receptor alpha (TRA) J24 gene segment and also suggested that in vitro-stimulated T cells can target HCRT. Here, we present evidence of in vivo expansion of DQ6-HCRT tetramer(+)/TRAJ24(+)/CD4(+) T cells in DQ6(+) individuals with and without narcolepsy. We identify related TRAJ24(+) TCRalphabeta clonotypes encoded by identical alpha/beta gene regions from two patients and two controls. TRAJ24-G allele(+) clonotypes only expand in the two patients, whereas a TRAJ24-C allele(+) clonotype expands in a control. A representative tetramer(+)/G-allele(+) TCR shows signaling reactivity to the epitope HCRT87-97. Clonally expanded G-allele(+) T cells exhibit an unconventional effector phenotype. Our analysis of in vivo expansion of HCRT-reactive TRAJ24(+) cells opens an avenue for further investigation of the autoimmune contribution to narcolepsy development

Phenotypic and genotypic characterization of Human Immunodeficiency Virus type 1 CRF07_BC strains circulating in the Xinjiang Province of China

BACKGROUND: HIV-1 CRF07_BC recombinant previously circulated mainly among the intravenous drug users (IDUs) in Xinjiang province of China and is currently spreading in the entire country. The aim of this study is to characterize the genotypic and phenotypic properties of HIV-1 CRF07_BC isolates in comparison with those of the subtype B' (Thailand B) which is prevalent in the former plasma donors (FPDs) in China. RESULTS: Twelve HIV-1 CRF07_BC variants were isolated from the blood of the HIV-1-infected IDUs in Xinjiang province, and 20 subtype B' isolates were obtained from the FPDs in Anhui and Shanxi provinces of China. All the CRF07_BC viruses utilized CCR5 co-receptor, whereas 12 subtype B' viruses were R5-tropic, and the remaining B' isolates were dual (R5X4) tropic. CRF07_BC viruses had lower net charge value in the V3 loop and exhibited slower replication kinetics than subtype B' viruses. The number and location of the potential N-linked glycosylation sites in V1/V2 and the C2 region of the CRF07_BC viruses were significantly different from those of the subtype B' viruses. CONCLUSION: The HIV-1 CRF07_BC recombinant strains with relatively lower net charges in the V3 loop exclusively utilize CCR5 co-receptor for infection and exhibit slow replication kinetics in the primary target cells, suggesting that CRF07_BC may be superior over B' and other HIV-1 subtypes in initiating infection in high-risk population. These findings have molecular implications for the adaptive evolution of HIV-1 circulating in China and the design of tailored therapeutic strategy for treatment of HIV-1 CRF07_BC infection