Condition monitoring and fault diagnosis methods for low-speed and heavy-load slewing bearings: a literature review

Low-speed and heavy-load slewing bearings are applied broadly for major mechanical equipment. Compared with ordinary bearings, large slewing bearings have complex structures and work in variable environments. In order to increase productivity, reduce maintenance costs, and ensure the safety of people and equipment, it is of great importance to monitor and diagnose faults in real time. This paper aims at providing a state-of-the-art review on methods for condition monitoring and fault diagnosis of low-speed and heavy-load slewing bearings, including methods based on vibration analysis, acoustic emission technique, oil condition and temperature variation. Additionally, this paper discusses advantages and disadvantages of different methods. Finally, the current needs and challenges are presented to provide a reference for future research

svdPPCS: an effective singular value decomposition-based method for conserved and divergent co-expression gene module identification

<p>Abstract</p> <p>Background</p> <p>Comparative analysis of gene expression profiling of multiple biological categories, such as different species of organisms or different kinds of tissue, promises to enhance the fundamental understanding of the universality as well as the specialization of mechanisms and related biological themes. Grouping genes with a similar expression pattern or exhibiting co-expression together is a starting point in understanding and analyzing gene expression data. In recent literature, gene module level analysis is advocated in order to understand biological network design and system behaviors in disease and life processes; however, practical difficulties often lie in the implementation of existing methods.</p> <p>Results</p> <p>Using the singular value decomposition (SVD) technique, we developed a new computational tool, named svdPPCS (<b>SVD</b>-based <b>P</b>attern <b>P</b>airing and <b>C</b>hart <b>S</b>plitting), to identify conserved and divergent co-expression modules of two sets of microarray experiments. In the proposed methods, gene modules are identified by splitting the two-way chart coordinated with a pair of left singular vectors factorized from the gene expression matrices of the two biological categories. Importantly, the cutoffs are determined by a data-driven algorithm using the well-defined statistic, SVD-p. The implementation was illustrated on two time series microarray data sets generated from the samples of accessory gland (ACG) and malpighian tubule (MT) tissues of the line W¹¹⁸of <it>M. drosophila</it>. Two conserved modules and six divergent modules, each of which has a unique characteristic profile across tissue kinds and aging processes, were identified. The number of genes contained in these models ranged from five to a few hundred. Three to over a hundred GO terms were over-represented in individual modules with FDR < 0.1. One divergent module suggested the tissue-specific relationship between the expressions of mitochondrion-related genes and the aging process. This finding, together with others, may be of biological significance. The validity of the proposed SVD-based method was further verified by a simulation study, as well as the comparisons with regression analysis and cubic spline regression analysis plus PAM based clustering.</p> <p>Conclusions</p> <p>svdPPCS is a novel computational tool for the comparative analysis of transcriptional profiling. It especially fits the comparison of time series data of related organisms or different tissues of the same organism under equivalent or similar experimental conditions. The general scheme can be directly extended to the comparisons of multiple data sets. It also can be applied to the integration of data sets from different platforms and of different sources.</p

Pricing Warrant Bonds with Credit Risk under a Jump Diffusion Process

This article investigates the pricing of the warrant bonds with default risk under a jump diffusion process. We assume that the stock price follows a jump diffusion model while the interest rate and the default intensity have the feature of mean reversion. By the risk neutral pricing theorem, we obtain an explicit pricing formula of the warrant bond. Furthermore, numerical analysis is provided to illustrate the sensitivities of the proposed pricing model

Relevance of the Pharmacokinetic and Pharmacodynamic Profiles of Puerariae lobatae Radix to Aggregation of Multi-Component Molecules in Aqueous Decoctions

The complexity of traditional Chinese medicines (TCMs) is related to their multi-component system. TCM aqueous decoction is a common clinical oral formulation. Between molecules in solution, there exist intermolecular strong interactions to form chemical bonds or weak non-bonding interactions such as hydrogen bonds and Van der Waals forces, which hold molecules together to form “molecular aggregates”. Taking the TCM Puerariae lobatae Radix (Gegen) as an example, we explored four Gegen decoctions of different concentration of 0.019, 0.038, 0.075, and 0.30 g/mL, named G-1, G-2, G-3, and G-4. In order of molecular aggregate size (diameter) the four kinds of solution were ranked G-1 &lt; G-2 &lt; G-3 &lt; G-4 by Flow Cell 200S IPAC image analysis. A rabbit vertebrobasilar artery insufficiency (VBI) model was set up and they were given Gegen decoction (GGD) at a clinical dosage of 0.82 g/kg (achieved by adjusting the gastric perfusion volume depending on the concentration). The HPLC fingerprint of rabbit plasma showed that the chemical component absorption into blood in order of peak area values was G-1 &lt; G-2 &gt; G-3 &gt; G-4. Puerarin and daidzin are the major constituents of Gegen, and the pharmacokinetics of G-1 and G-2 puerarin conformed with the two compartment open model, while for G-3 and G-4, they conformed to a one compartment open model. For all four GGDs the pharmacokinetics of daidzin complied with a one compartment open model. FQ-PCR assays of rabbits’ vertebrobasilar arterial tissue were performed to determine the pharmacodynamic profiles of the four GGDs. GGD markedly lowered the level of AT1R mRNA, while the AT2R mRNA level was increased significantly vs. the VBI model, and G-2 was the most effective. In theory the dosage was equal to the blood drug concentration and should be consistent; however, the formation of molecular aggregates affects drug absorption and metabolism, and therefore influences drugs’ effects. Our data provided references for the rational use of Chinese medicines in the clinic, such as the best oral preparation and decoction concentration

Combined Failure Diagnosis of Slewing Bearings Based on MCKD-CEEMD-ApEn

Large-size and heavy-load slewing bearings, which are mainly used in heavy equipment, comprise a subgroup of rolling bearings. Owing to the complexity of the structures and working conditions, it is quite challenging to effectively diagnose the combined failure and extract fault features of slewing bearings. In this study, a method was proposed to denoise and classify the combined failure of slewing bearings. First, after removing the mean, the vibration signals were denoised by maximum correlated kurtosis deconvolution. The signals were then decomposed into several intrinsic mode functions (IMFs) by complementary ensemble empirical mode decomposition (CEEMD). Appropriate IMFs were selected based on the correlation coefficient and kurtosis. The approximate entropy values of the selected IMFs were regarded as the characteristic vectors and then inputted into the support vector machine (SVM) based on multiclass classification for training. The practical combined failure signals of the 3 conditions were finally recognized and classified using SVMs. The study also compared the proposed method with 5 other methods to demonstrate the superiority and effectiveness of the proposed method

CRISPR/Cas9-Mediated Mutagenesis of <i>GmFAD2-1A</i> and/or <i>GmFAD2-1B</i> to Create High-Oleic-Acid Soybean

Soybean (Glycine max (L.) Merr.) oil is an important source of vegetable oil for supporting the human diet. However, the high level of polyunsaturated fatty acids in natural soybean oil renders the oil unstable and thus susceptible to the development of unpalatable flavors and trans fatty acids. Therefore, reducing the content of polyunsaturated fatty acids and increasing the content of monounsaturated fatty acids is a longstanding and important target for soybean breeding. However, soybean varieties with a high oleic acid content are rare in soybean germplasm resources, which introduces substantial difficulties in the cultivation of high-oleic-acid soybeans. In this study, CRISPR/Cas9-mediated gene-editing technology was used to create targeted knockout of the soybean fatty acid desaturase encoding genes GmFAD2-1A and GmFAD2-1B that contribute to the formation of polyunsaturated fatty acids. We obtained fad2-1a, fad2-1b, and fad2-1a/fad2-1b homozygous mutants using two sgRNAs. We found that the oleic acid content increased from 11% to 40-50% in the fad2-1a and fad2-1b mutants and to 85% in the fad2-1a/fad2-1b mutants. We also generated transgene-free double mutants that conferred higher oleic acid, and the fad2-1a/fad2-1b mutant had no adverse phenotyping compared with the wild type. Our study provided new materials for the selection and breeding of high-oleic-acid soybean varieties

Autonomous bolt loosening detection using deep learning

Machine vision-based structural health monitoring is gaining popularity due to the rich information one can extract from video and images. However, the extraction of characteristic parameters from images often requires manual intervention, thereby limiting its scalability and effectiveness. In contrast, deep learning overcomes the aforementioned shortcoming in that it can autonomously extract feature parameters (e.g. structural damage) from image datasets. Therefore, this study aims to validate the use of machine vision and deep learning for structural health monitoring by focusing on a particular application of detecting bolt loosening. First, a dataset that contains 300 images was collected. The dataset includes two bolt states, namely, tight and loosened. Second, a faster region-based convolutional neural network was trained and evaluated. The test results showed that the average precision of bolt damage detection is 0.9503. Thereafter, bolts were loosened to various screw heights, and images obtained from different angles, lighting conditions, and vibration conditions were identified separately. The trained model was then employed to validate that bolt loosening could be detected with sufficient accuracy using various types of images. Finally, the trained model was connected with a webcam to realize real-time bolt loosening damage monitoring

Berberine Alleviates Amyloid β-Induced Mitochondrial Dysfunction and Synaptic Loss

Synaptic structural and functional damage is a typical pathological feature of Alzheimer’s disease (AD). Normal axonal mitochondrial function and transportation are vital to synaptic function and plasticity because they are necessary for maintaining cellular energy supply and regulating calcium and redox signalling as well as synaptic transmission and vesicle release. Amyloid-β (Aβ) accumulation is another pathological hallmark of AD that mediates synaptic loss and dysfunction by targeting mitochondria. Therefore, it is important to develop strategies to protect against synaptic mitochondrial damage induced by Aβ. The present study examined the beneficial effects of berberine, a natural isoquinoline alkaloid extracted from the traditional medicinal plant Coptis chinensis, on Aβ-induced mitochondrial and synaptic damage in primary cultured hippocampal neurons. We demonstrate that berberine alleviates axonal mitochondrial abnormalities by preserving the mitochondrial membrane potential and preventing decreases in ATP, increasing axonal mitochondrial density and length, and improving mitochondrial motility and trafficking in cultured hippocampal neurons. Although the underlying protective mechanism remains to be elucidated, the data suggest that the effects of berberine were in part related to its potent antioxidant activity. These findings highlight the neuroprotective and specifically mitoprotective effects of berberine treatment under conditions of Aβ enrichment