Search

To solve the difficulty in correctly identifying a compound fault of rolling bearing, a method combining variational mode decomposition (VMD) and harmonic fusion vector bispectrum (HFVB) is proposed. Firstly, to achieve adaptive decomposition of signals, the characteristic ability of envelope entropy to represent signal sparsity is utilized. By employing the minimum envelope entropy as the fitness function for the sparrow search algorithm (SSA), the decomposition levels and penalty factors of VMD are adaptively determined. Secondly, root-mean-square value is treated as fault feature index to self-adaptively choose from intrinsic mode function (IMF) which can embody fault features of bearings. Thirdly, to further highlight fault features, HFVB is used to blend chosen IMFs. Finally, faults of bearings were recognized with spectrum of signals. To verify the effectiveness of proposed method, an analysis was given to vibration signals in different situations, and SSA-VMD-HFVB was compared with classical method. The results demonstrate that the proposed SSA-VMD-HFVB method facilitates the adaptive decomposition of VMD, enabling the selection and effective integration of sensitive fault component signals. This approach enhances the accuracy of complex fault diagnosing in rolling bearings. [ABSTRACT FROM AUTHOR]

View from specialist: It is creative, and of certain scientific and educational value. Objective: To investigate the therapeutic effect and mechanism of Maimendong decoction on idiopathic pulmonary fibrosis by utilizing network pharmacology methods and combining experimental research.Methods: TCMSP, ETCM and Swiss ADME were used to screen the active ingredients of Maimendong decoction. Through PubChem, SwissTargetPrediction and Uniport databases, the action targets of various components of traditional Chinese medicine were screened. The GeneCards database and OMIN database were applied to obtain target genes of idiopathic pulmonary fibrosis. Venn diagrams drawing tool was used to screen the intersection targets of traditional Chinese medicine and diseases. The "traditional Chinese medicine active ingredient disease target" network diagram was constructed by using the Cytoscape 3.9.1 software, and the protein interaction network (PPI) was constructed by using the CytoNCA plug-in to screen the core target proteins. DAVID was used for enrichment analysis of GO and KEGG pathways. Finally, we used bleomycin to establish a pulmonary fibrosis model in SD rats, observed the macroscopic specimens and pathological sections of rat lung tissue, and conducted real-time fluorescence quantitative PCR analysis to compare the mRNA expression levels of TGF-β1, α-SMA, IL-4, IFN-γ, LC3-II, and beclin1 between the treatment group and the control group. Results:A total of 402 therapeutic targets of Maimendong decoction for IPF were screened, including 50 core targets, corresponding to 159 active ingredients of 5 traditional Chinese medicines. GO enrichment analysis and KEGG enrichment analysis involved the PI3K-AKT signal pathway, MAPK signal pathway and other signal pathways to treat idiopathic pulmonary fibrosis. The animal experiment results showed that compared with the model group, the Maimendong decoction group could significantly reduce the relative expression level of TGF-β1, α-SMA and IL-4 mRNA (P<0.05), significantly upregulated the relative expression level of mRNA LC3-II, beclin1, IFN-γ (P<0.05) .Conclusion: This study suggests that the key active components of Maimendong decoction may play a role in biological processes such as autophagy, inflammatory reaction, macrophage polarization, and improve pulmonary fibrosis. [ABSTRACT FROM AUTHOR]

When a compound failure occurs in a bearing, the failure information included in vibration signals is featured by being weak, complex and combined. This fact makes it difficult to identify a compound failure precisely. Variational mode extraction (VME) solves the problem of variational mode decomposition (VMD) in being difficult to determine the number of decomposition layers and has certain applications in the identification of bearing failures. However, the initial expected central frequency of VME and option of penalty factor is crucial to the extraction of expected mode. To precisely determine the central frequency and penalty factor of VME and fulfill the correct extraction of feature information of compound failures in bearing, the paper has proposed a method based on synchro squeezing transform (SST) and information entropy to adaptively determine the parameters in VME (SST-VME). Firstly, SST is used to make time-frequency analysis of original vibration signals and characteristic frequency bands with larger energy are chosen from time-frequency spectrum to adaptively determine the expected central frequency of VME. Secondly, as information entropy has representation capacity for information included in the signal, penalty factor of VME was adaptively determined according to information entropy. Thirdly, original signals were subjected to VME according to expected central frequency and penalty factor adaptively determined; the expectation mode obtained was denoised by singular value decomposition algorithm. Finally, the type of compound failures of bearing is determined according to the frequency spectrum of denoised signals. To verify the effectiveness of proposed method, a comparison with VMD algorithm is conducted. As indicated by the result, the proposed method is more precise and comprehensive than VMD algorithm to extract the feature information corresponding to compound faults of bearing, and thereby correctly determines the type of compound failures. [ABSTRACT FROM AUTHOR]

Bearing is the most vulnerable key part in rotating machine and bears important influence on the safety of equipment. Weakness and complexity are the two features of fault characteristic information carried by signals in the early stage of fault. For that, a fault is difficult to be recognized correctly. To identify a compound failure of bearing, the paper has brought forward a new self-adaptive option method for component signals that are sensitive to failure feature information of bearing. The sensitivity of kurtosis to bearing failure is exploited and the influence of signal complexity on the extraction of failure feature information is taken seriously, the paper has proposed the self-adaptive option method for component signals that are sensitive to failure feature by combined kurtosis with Complexity parameter included in Hjorth parameters. Furthermore, as the mid-value represents the general level of signal and is not affected by larger or smaller data, with the mid-values of kurtosis and Complexity parameter as the boundary, the paper has chosen the component signals which can more comprehensively show the failure features of bearing. Additionally, by principal component analysis (PCA), component signals selected are blended and reconstructed. Finally, by the Hilbert envelope spectrum of signals reconstructed, failure types of bearing are identified. To verify the effectiveness of presented method, the presented method is compared with conventional method on the basis of the exactly consistent data. The result indicates that the proposed method is superior to the traditional one in extracting fault information and identifying the multiple failure types of bearing. [ABSTRACT FROM AUTHOR]

A series of molecular dynamics simulations were performed using the ReaxFF reactive force field to investigate the pyrolysis of Longkou oil shale kerogen in the presence of methanol. The roles of methanol in kerogen pyrolysis were deeply explored to reveal the microscopic mechanism of methanolysis by analyzing the distribution of products, the consumption of methanol, and the formation processes of typical products at different temperatures. The results demonstrate that the yield of shale oil in the presence of methanol is consistently higher than that of kerogen direct pyrolysis at the same temperature, which is consistent with the trend of oil yield with temperature reported by previous methanolysis experiments. Methanol supplies a large number of small radicals under the pyrolysis condition, which can facilitate the breakage of weak bonds and take part in the formation of products through combining with the radical fragments generated by the pyrolysis of kerogen. This study will provide theoretical guidance for understanding the methanolysis process of oil shale. [ABSTRACT FROM AUTHOR]

For effective identification of rub-impact faults between rotor and stator of equipment, the paper has contributed the method integrating dual complexity parameters and variational mode decomposition (VMD). Firstly, to effectively separate the fault characteristics involved in signals, VMD algorithm was applied to decompose vibration signals and the component signals were obtained; secondly, taking account of the large difference of fault feature information involved in different component signals and in order to make options of sensitive fault component signals and reduce the loss of fault features, multi-scale Lempel–Ziv complexity and complexity parameter in Hjorth parameters were brought. Starting from 2 different perspectives of complexity evaluation, choose from the sensitive component signals containing more fault characteristics with these 2 complexity parameters; thirdly, signals were reconstructed based on selected sensitive component signals, and meanwhile, singular value difference spectrum algorithm was used to denoise reconstructed signals to further lessen the influence of noises; finally, the rub-impact fault between rotor and stator was identified by square demodulation spectrum (SDS) of denoised signal. The effectiveness of the proposed method has been proved by comparative analysis with other approaches as well as validation analysis of rub-impact fault signals in multiple situations. [ABSTRACT FROM AUTHOR]

Inter-shaft bearing is an important component of dual-rotor aeroengine and it is difficult to extract fault characteristics and identify a fault type. Therefore, the paper has proposed a method (CCF–Complexity–VMD–SVD) combining information fusion, Complexity parameter of Hjorth parameters, variational mode decomposition (VMD), and singular value decomposition (SVD). Firstly, to fully embody fault characteristic information, the method has established the cross-correlation function (CCF) of homologous acceleration information from the casing of one of the aeroengines (acceleration signals collected from the same section at the same moment in orthogonal directions) to blend homologous information, reinforce fault characteristic components, and lessen the influence of noise. Secondly, considering the great influence of signal complexity on the extraction of fault characteristics and the Complexity parameter of Hjorth parameters can express the complexity of signals better, the Complexity parameter of Hjorth parameters is placed into the fault characteristic extraction of inter-shaft bearing. The Complexity parameter is applied to optimize the number of layers and central frequency of VMD. Thirdly, signals are denoised through SVD to further lessen the influence of noise on characteristic extraction. Finally, frequency spectrum (FS) of signal denoised is used to make characteristic extraction and fault identification of compound faults of inter-shaft bearing. The result of comparative analysis with other methods has further illustrated the proposed method CCF–Complexity–VMD–SVD that can actualize the effective characteristic extraction of compound faults and precise identification in the early stage of fault based on vibration signals from casing of aeroengine. The performance of engineering appliance of the proposed method has been further verified through fault analysis of disassembly for the aeroengine. [ABSTRACT FROM AUTHOR]