Rolling bearing fault diagnosis using improved LCD-TEO and softmax classifier

A novel rolling bearing fault diagnosis method based on improved local characteristic-scale decomposition (LCD), Teager energy operator (TEO) and softmax classifier is proposed in this paper. First, vibration signals are decomposed into several intrinsic scale components (ISCs) by using improved LCD; second, TEO and fast Fourier transform (FFT) are respectively used to extract instantaneous amplitude (IA) and frequency spectra of ISC1s, and then FFT is again employed to obtain spectra of IA; third, energy ratio of the resonant frequency band against the total, frequency entropy (FE) in the spectra of ISC1s and several amplitude ratios in the frequency spectra of demodulated ISC1s are extracted as fault feature vectors, and principal components analysis (PCA) is applied for dimensionality reduction; finally, these feature vectors are taken as inputs to train and test softmax classifier. As a new non-stationary signal analysis tool, LCD can decompose adaptively a signal into series of ISCs in different scales and give good results in situations where other methods failed. However, there are two main issues in this method, end effect and mode mixing, possibly leading to unexpected results. In this paper, a slope-based method and noise assisted analysis are applied to restrain the problems respectively. Experimental results show the proposed method performs effectively for bearing fault diagnosis

A centrifugal pump fault diagnosis approach based on LCD-ApEn and PNN

According to the non-stationary and nonlinear characteristics of bearing vibration signals of the centrifugal pumps, a fault diagnosis method based on local characteristic-scale decomposition (LCD)-approximate entropy (ApEn) and probabilistic Neural Network (PNN) is proposed in this paper. First, the vibration signals are decomposed into a finite number of intrinsic scale components (ICSs), after which the ApEn of each ISC is obtained to form the feature vector. Then, the feature vectors are selected as the input vectors of the PNN for fault diagnosis. The classification results achieve a fault recognition rate of 100 % for various centrifugal pump fault patterns, which verifies the effectiveness and feasibility of the proposed method

Identification and Characterization of the PEBP Family Genes in Moso Bamboo (Phyllostachys heterocycla).

Moso bamboo is one of the economically most important plants in China. Moso bamboo is a monocarpic perennial that exhibits poor and slow germination. Thus, the flowering often causes destruction of moso bamboo forestry. However, how control of flowering and seed germination are regulated in moso bamboo is largely unclear. In this study, we identified 5 members (PhFT1-5) of the phosphatidyl ethanolamine-binding proteins (PEBP) family from moso bamboo genome that regulate flowering, flower architecture and germination, and characterized the function of these PEBP family genes further in Arabidopsis. Phylogenetic analysis revealed that 3 (PhFT1, PhFT2 and PhFT3), 1 (PhFT4) and 1 (PhFT5) members belong to the TFL1-like clade, FT-like clade, and MFT-like clade, respectively. These PEBP family genes possess all structure necessary for PEBP gene function. The ectopic overexpression of PhFT4 and PhFT5 promotes flowering time in Arabidopsis, and that of PhFT1, PhFT2 and PhFT3 suppresses it. In addition, the overexpression of PhFT5 promotes seed germination rate. Interestingly, the overexpression of PhFT1 suppressed seed germination rate in Arabidopsis. The expression of PhFT1 and PhFT5 is significantly higher in seed than in tissues including leaf and shoot apical meristem, implying their function in seed germination. Taken together, our results suggested that the PEBP family genes play important roles as regulators of flowering and seed germination in moso bamboo and thereby are necessary for the sustainability of moso bamboo forest

Graphene-enhanced visible-light photocatalysis of large-sized CdS particles for wastewater treatment

The hybrid composites of graphene decorated by large-sized CdS particles (G/M-CdS) were prepared by a one-pot solvothermal route in which the reduction of graphite oxide into graphene was accompanied by the generation of microsized CdS particles. The structure and composition of the obtained nanocomposites were studied by means of X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. The CdS particles with the average sizes of approximately 640 nm were formed on graphene sheets. The as-prepared composite was used as adsorbent to remove dye from wastewater using the organic dye Rhodamine B as the adsorbate. The G/M-CdS composite reveals a high photodegradation rate under visible light irradiation. Our results demonstrate that the G/M-CdS is very promising for removing organic dyes from wastewater

NRAV, a Long Noncoding RNA, Modulates Antiviral Responses through Suppression of Interferon-Stimulated Gene Transcription

SummaryLong noncoding RNAs (lncRNAs) modulate various biological processes, but their role in host antiviral responses is largely unknown. Here we identify a lncRNA as a key regulator of antiviral innate immunity. Following from the observation that a lncRNA that we call negative regulator of antiviral response (NRAV) was dramatically downregulated during infection with several viruses, we ectopically expressed NRAV in human cells or transgenic mice and found that it significantly promotes influenza A virus (IAV) replication and virulence. Conversely, silencing NRAV suppressed IAV replication and virus production, suggesting that reduction of NRAV is part of the host antiviral innate immune response to virus infection. NRAV negatively regulates the initial transcription of multiple critical interferon-stimulated genes (ISGs), including IFITM3 and MxA, by affecting histone modification of these genes. Our results provide evidence for a lncRNA in modulating the antiviral interferon response

The mouse and ferret models for studying the novel avian-origin human influenza A (H7N9) virus.

BackgroundThe current study was conducted to establish animal models (including mouse and ferret) for the novel avian-origin H7N9 influenza virus.FindingsA/Anhui/1/2013 (H7N9) virus was administered by intranasal instillation to groups of mice and ferrets, and animals developed typical clinical signs including body weight loss (mice and ferrets), ruffled fur (mice), sneezing (ferrets), and death (mice). Peak virus shedding from respiratory tract was observed on 2 days post inoculation (d.p.i.) for mice and 3-5 d.p.i. for ferrets. Virus could also be detected in brain, liver, spleen, kidney, and intestine from inoculated mice, and in heart, liver, and olfactory bulb from inoculated ferrets. The inoculation of H7N9 could elicit seroconversion titers up to 1280 in ferrets and 160 in mice. Leukopenia, significantly reduced lymphocytes but increased neutrophils were also observed in mouse and ferret models.ConclusionsThe mouse and ferret model enables detailed studies of the pathogenesis of this illness and lay the foundation for drug or vaccine evaluation

Remaining useful life prognostics of lithium-ion batteries based on a coordinate reconfiguration of degradation trajectory and multiple linear regression

Lithium battery has been widely applied as new energy to cope with pressures in both form environment and energy. The remaining useful life (RUL) prognostics of lithium-ion batteries have become more critical. Convenient battery life prediction allows early detection of performance deficiencies to help maintain the battery system promptly. This paper proposes a RUL prognostics model of lithium-ion batteries based on a coordinate reconfiguration of degradation trajectory and multiple linear regression. First, a new sampling rule is used to reconfigure the coordinates of degradation data of new batteries and truncated similar batteries. Then, the relationship between similar and new lithium-ion batteries is established by using the reconfiguration data. Moreover, a new RUL prognostics model based on a coordinate reconfiguration of degradation trajectory and multiple linear regression is established by considering the influence of time-varying factors, which can improve prediction accuracy with small sample data and significantly reduce product development time and cost