The pressure signal of the standpipe in the regeneration device for the catalytic cracking reaction is commonly used as a basis for equipment fault diagnosis. The device operates in environments of high temperature, strong vibration, and high flow rate. The standpipe pressure signal is easily interfered with by noise, making it difficult to extract pressure characteristics.
View Article and Find Full Text PDFBackground: Foetal renal dysplasia is still the main cause of adult renal disease. Placenta-derived exosomes are an important communication tool, and they may play an important role in placental (both foetal and maternal) function. We hypothesize that in women with preeclampsia, foetal renal dysplasia is impeded by delivering placenta-derived exosomes to glomerular endothelial cells.
View Article and Find Full Text PDFDeep learning algorithms have the advantages of a powerful time series prediction ability and the real-time processing of massive samples of big data. Herein, a new roller fault distance estimation method is proposed to address the problems of the simple structure and long conveying distance of belt conveyors. In this method, a diagonal double rectangular microphone array is used as the acquisition device, minimum variance distortionless response (MVDR) and long short-term memory network (LSTM) are used as the processing models, and the roller fault distance data are classified to complete the estimation of the idler fault distance.
View Article and Find Full Text PDFAt present, the fault diagnosis methods for rolling bearings are all based on research with fewer fault categories, without considering the problem of multiple faults. In practical applications, the coexistence of multiple operating conditions and faults can lead to an increase in classification difficulty and a decrease in diagnostic accuracy. To solve this problem, a fault diagnosis method based on an improved convolution neural network is proposed.
View Article and Find Full Text PDFA voiceprint signal as a non-contact test medium has a broad application prospect in power-transformer operation condition monitoring. Due to the high imbalance in the number of fault samples, when training the classification model, the classifier is prone to bias to the fault category with a large number of samples, resulting in poor prediction performance of other fault samples, and affecting the generalization performance of the classification system. To solve this problem, a method of power-transformer fault voiceprint signal diagnosis based on Mixup data enhancement and a convolution neural network (CNN) is proposed.
View Article and Find Full Text PDFBackground: Fetal lung underdevelopment (FLUD) is associated with neonatal and childhood severe respiratory diseases, among which gestational diabetes mellitus (GDM) play crucial roles as revealed by recent prevalence studies, yet mechanism underlying GDM-induced FLUD, especially the role of trophoblasts, is not all known.
Methods: From the perspective of trophoblast-derived exosomes, we established in vitro, ex vivo, in vivo and GDM trophoblast models. Utilizing placenta-derived exosomes (NUB-exos and GDMUB-exos) isolated from normal and GDM umbilical cord blood plasma and trophoblast-derived exosomes (NC-exos and HG-exos) isolated from HTR8/SVneo trophoblasts medium with/without high glucose treatment, we examined their effects on fetal lung development and biological functions.
Background: Early onset preeclampsia (EOSP, PE) is characterized by hypertension, proteinuria, and endothelial dysfunction. Oxidative stress-induced trophoblast dysfunction is a major pathology in PE. Placental exosomes are extracellular vesicles that are involved in "mother-placenta-foetal communication" and can regulate the biological functions of endothelial cells.
View Article and Find Full Text PDFThe mechanism of parturition is still unclear. Evidence has shown that delivery is associated with cellular senescence of the amniotic membrane. We isolated fetal lung-associated exosomes from the amniotic fluid from term labor (TL-exos) and verified that the exosomes can cause primary human amniotic epithelial cell (hAEC) senescence and apoptosis and can release higher levels of senescence-associated secretory phenotype (SASP)-related molecules and proinflammatory damage-associated molecular patterns (DAMPs) than exosomes isolated from the amniotic fluid from term not in labor (TNIL-exos).
View Article and Find Full Text PDFObstetric antiphospholipid syndrome (OAPS) is mediated by antiphospholipid antibodies (aPLs, and anti-β2 glycoprotein I antibody is the main pathogenic antibody), and recurrent abortion, preeclampsia, foetal growth restriction and other placental diseases are the main clinical characteristics of placental pathological pregnancy. It is a disease that seriously threatens the health of pregnant women. Hydroxychloroquine (HCQ) was originally used as an anti-malaria drug and has now shown benefit in refractory OAPS where conventional treatment has failed, with the expectation of providing protective clinical benefits for both the mother and foetus.
View Article and Find Full Text PDFIntroduction: Our study aimed to distinguish patients with placenta accreta (crete, increta, and percreta) from those with placenta previa using maternal plasma levels of soluble fms-like tyrosine kinase-1 (sFlt-1) and placental growth factor (PLGF) and the sFlt-1/PLGF ratio.
Methods: We obtained maternal plasma from 185 women in late pregnancy and sorted them into three groups: 72 women with normal placental imaging results (control group), 50 women with placenta previa alone (PP group), and 63 women with placenta previa and placenta accreta (PAS group). The concentrations of sFlt-1 and PLGF in the maternal plasma were measured using ELISA kits and the sFlt-1/PLGF ratio was calculated.
Feature extraction is an essential process in the intelligent fault diagnosis of rotating machinery. Although existing feature extraction methods can obtain representative features from the original signal, domain knowledge and expert experience are often required. In this article, a novel diagnosis approach based on evolutionary learning, namely, automatic feature extraction and construction using genetic programming (AFECGP), is proposed to automatically generate informative and discriminative features from original vibration signals for identifying different fault types of rotating machinery.
View Article and Find Full Text PDFIn recent years, vibration-based intelligent fault diagnosis of high-voltage circuit breakers (HVCBs) exhibits excellent performance. It requires a reliable machine learning method to develop an automatically diagnostic model to recognize the mechanical state from vibration signals. However, the traditional machine learning methods tend to produce unstable diagnostic results under the case of sampling asynchrony caused by the fluctuation of the control voltage.
View Article and Find Full Text PDFEarly fault information of rolling bearings is weak and often submerged by background noise, easily leading to misdiagnosis or missed diagnosis. In order to solve this issue, the present paper puts forward a fault diagnosis method on the basis of adaptive frequency window (AFW) and sparse coding shrinkage (SCS). The proposed method is based on the idea of determining the resonance frequency band, extracting the narrowband signal, and envelope demodulating the extracted signal.
View Article and Find Full Text PDFAiming at the problem that the weak faults of rolling bearing are difficult to recognize accurately, an approach on the basis of swarm decomposition (SWD), morphology envelope dispersion entropy (MEDE), and random forest (RF) is proposed to realize effective detection and intelligent recognition of weak faults in rolling bearings. The proposed approach is based on the idea of signal denoising, feature extraction and pattern classification. Firstly, the raw signal is divided into a group of oscillatory components through SWD algorithm.
View Article and Find Full Text PDFAs high-voltage circuit breakers (HVCBs) are directly related to the safety and the stability of a power grid, it is of great significance to carry out fault diagnoses of HVCBs. To accurately identify operating states of HVCBs, a novel mechanical fault diagnosis method of HVCBs based on multi-feature entropy fusion (MFEF) and a hybrid classifier is proposed. MFEF involves the decomposition of vibration signals of HVCBs into several intrinsic mode functions using variational mode decomposition (VMD) and the calculation of multi-feature entropy by the integration of three Shannon entropies.
View Article and Find Full Text PDFKurtogram can adaptively select the resonant frequency band, and then the characteristic fault frequency can be obtained by analyzing the selected band. However, the kurtogram is easily affected by random impulses and noise. In recent years, improvements to kurtogram have been concentrated on two aspects: (a) the decomposition method of the frequency band; and (b) the selection index of the optimal frequency band.
View Article and Find Full Text PDFMechanical fault diagnosis of a circuit breaker can help improve the reliability of power systems. Therefore, a new method based on multiscale entropy (MSE) and the support vector machine (SVM) is proposed to diagnose the fault in high voltage circuit breakers. First, Variational Mode Decomposition (VMD) is used to process the high voltage circuit breaker's vibration signals, and the reconstructed signal can eliminate the effect of noise.
View Article and Find Full Text PDFThe fast spectrum kurtosis (FSK) algorithm can adaptively identify and select the resonant frequency band and extract the fault feature via the envelope demodulation method. However, the FSK method has some limitations due to its susceptibility to noise and random knocks. To overcome this shortage, a new method is proposed in this paper.
View Article and Find Full Text PDFScientificWorldJournal
April 2015
The complex process planning problem is modeled as a combinatorial optimization problem with constraints in this paper. An ant colony optimization (ACO) approach has been developed to deal with process planning problem by simultaneously considering activities such as sequencing operations, selecting manufacturing resources, and determining setup plans to achieve the optimal process plan. A weighted directed graph is conducted to describe the operations, precedence constraints between operations, and the possible visited path between operation nodes.
View Article and Find Full Text PDF