ABSTRACT
Because of the operating environment and load, the main fault form of flywheel bearing is the friction fault between the cage and the rolling elements, which often lead to an increase in the friction torque of the bearing and even to the failure of the flywheel. However, due to the complex mechanism of the friction fault, the characteristic frequencies often used to indicate cage failure are not obvious, which makes it difficult to monitor and quantitatively judge such faults. Therefore, this paper studies the mechanism of the friction fault of the flywheel bearing cage and establishes its fault feature identification method. Firstly, the basic dynamic model of the bearing is established in this paper, and the friction between the cage and the rolling elements is simulated by the variable stiffness. The influence law of the bearing vibration response reveals the relationship between the periodic fluctuation of cage-rolling element friction failure and the bearing load. After analyzing the envelope spectrum of the vibration data, it was found that when a friction fault occurred between the cage and the rolling element, the rotation frequency component of the cage modulated the rotational frequency component of the rolling element, that is, the side frequency components appeared on both sides of the characteristic frequency of the rolling element (with the characteristic frequency of the cage as the interval). In addition, the modulation frequency components of the cage and rolling element changed with the severity of the fault. Then, a modulation sideband ratio method based on envelope spectrum was proposed to qualitatively diagnose the severity of the cage-rolling element friction faults. Finally, the effectiveness of the presented method was verified by experiments.
ABSTRACT
Finite element model updating precision depends heavily on sufficient vibration feature extraction. However, adequate amount of sample collection is generally time-consuming in frequency response (FR) model updating. Accurate vibration feature extraction with insufficient data has become a significant challenge in FR model updating. To update the finite element model with a small dataset, a novel approach based on transfer learning is firstly proposed in this paper. A readily available fault diagnosis dataset is selected as ancillary knowledge to train a high-precision mapping from FR data to updating parameters. The proposed transfer learning network is constructed with two branches: source and target domain feature extractor. Considering about the cross-domain feature discrepancy, a domain adaptation method is designed by embedding the extracted features into a shared feature space to train a reliable model updating framework. The proposed method is verified by a simulated satellite example. The comparison results manifest that sample amount dependency has prominently lessened this method and the updated model outperforms the method without transfer learning in accuracy with the small dataset. Furthermore, the updated model is validated through dynamic response out of the training set.
ABSTRACT
Silk fibroin (SF) is a natural protein polymer and promising biomaterial. Chemical modifications have attracted growing interest in expanding SF applications. However, the majority of amino acid residues in SF are non-reactive and most of the reactive ones are in the crystalline region. Herein, a modification was conducted to investigate the possibility of direct modification on the surface of natural SF by a reagent with a mild reactivity, the type and quantity of the residues involved in the reactions, and the structural changes upon modification. Infrared spectrum, 1H NMR, titration and amino acid analyses, X-ray diffraction, and hemolysis test were used to analyze the materials. The results showed that sulfonic acid groups were grafted onto SF and the reaction occurred mainly at serine residues through hydroxyl groups. In total, 0.0958 mmol/g of residues participated in the modification with a modification efficiency of 7.6%. Moreover, the crystallinity and the content of ß-sheet structure in SF increased upon modification. The modified material had good blood-compatibility. In conclusion, surface modification on native SF through serine residues was practicable and had the advantage of increased ß-sheet structure. This will provide an alternative way for the modification of fibroin for the desired application in the biomedical field.
ABSTRACT
The ever-increasing worldwide energy demand and the limited resources of fossil have forced the urgent adoption of renewable energy sources. Additionally, concerns over CO2 emissions and potential increases in fuel prices have boosted technical efforts to make hybrid and electric vehicles more accessible to the public. Rechargeable batteries are undoubtedly a key player in this regard, especially lithium ion batteries (LIBs), which have high power capacity, a fast charge/discharge rate, and good cycle stability, while their further energy density improvement has been severely limited, because of the relatively low theoretical capacity of the graphite anode material which is mostly used. Among various high-capacity anode candidates, tin (II) sulfide (SnS2) has been attracted remarkable attention for high-energy LIBs due to its enormous resource and simplicity of synthesis, in addition to its high theoretical capacity. However, SnS2 has poor intrinsic conductivity, a big volume transition, and a low initial Coulombic efficiency, resulting in a short lifespan. SnS2/carbon composites have been considered to be a most promising approach to addressing the abovementioned issues. Therefore, this review summarizes the current progress in the synthesis of SnS2/carbon anode materials and their Li-ion storage properties, with special attention to the developments in Li-based technology, attributed to its immense current importance and promising prospects. Finally, the existing challenges within this field are presented, and potential opportunities are discussed.
ABSTRACT
Published data has shown inconsistent findings about the association of survivin -31 G/C polymorphism with the risk of colorectal cancer (CRC). This meta-analysis quantitatively assesses the results from published studies to provide a more precise estimate of the association between survivin -31 G/C polymorphism as a possible predictor of the risk of CRC. We conducted a literature search in the PubMed, Web of Science, and Cochrane Library databases. Stata 12 software was used to calculate the pooled odds ratios (ORs) with 95% confidence intervals (CIs) based on the available data from each article. Six studies including 1840 cases with CRC and 1804 controls were included in this study. Survivin -31 G/C polymorphism was associated with a significantly increased risk of CRC (OR = 1.78; 95% CI, 1.53-2.07; I(2) = 0%). In the race subgroup analysis, both Asians (OR = 1.72; 95% CI, 1.44-2.05; I(2) = 0%) and Caucasians (OR = 1.93; 95% CI, 1.46-2.55; I(2) = 0%) with survivin -31 G/C polymorphism had increased CRC risk. In the subgroup analysis according to site of CRC, survivin -31 G/C polymorphism was not associated with colon cancer risk (OR = 2.02; 95% CI, 0.79-5.22; I(2) = 82%). However, this polymorphism was significantly associated with rectum cancer risk (OR = 1.98; 95% CI, 1.42-2.74; I(2) = 0%). In the subgroup analysis by clinical stage, both early stage (I+II) and advanced stage (III+IV) were associated with survivin -31 G/C polymorphism (OR = 1.61; 95% CI, 1.20-2.16; I(2) = 0% and OR = 2.30; 95% CI, 1.70-3.13; I(2) = 0%, respectively). In the subgroup analysis by smoke status, both smokers and non-smokers with survivin -31 G/C polymorphism showed increased CRC risk (OR = 1.47; 95% CI, 1.01-2.13; I(2) = 60% and OR = 1.71; 95% CI, 1.28-2.30; I(2) = 0%, respectively). In the subgroup analysis by drink status, both drinkers and non-drinkers with survivin -31 G/C polymorphism showed increased CRC risk (OR = 1.58; 95% CI, 1.06-2.37; I(2) = 8% and OR = 1.61; 95% CI, 1.23-2.11; I(2) = 0%, respectively). In conclusion, this meta-analysis suggested that survivin -31 G/C polymorphism may be associated with the risk of CRC.
ABSTRACT
Previous studies report controversial role of Hedgehog (HH) signaling in the progression of colon cancer. This study aimed to investigate the expressions of smoothened (SMO) and downstream glioma-associated oncogene homology-1 (GLI1) in colon cancer, colonic adenoma and normal tissues. Colon cancer and normal tissue samples were collected from 49 patients with colon cancer while colonic adenoma tissue samples were obtained from 34 patients with colonic adenoma. Then the expressions of SMO and GLI1 were investigated using immunohistochemistry (IHC). For the detection of SMO and GLI1 expression, IHC staining results indicated that SMO was mainly expressed on the membrane while GLI1 was mainly expressed in the cytoplasm. The positive rates of SMO and GLI1 protein expressions were significantly increased in colon cancer tissue and colonic adenoma tissue when compared with normal colon tissue. In contrast, the significant difference was not found in the positive rates of SMO and GLI1 protein expressions between colon cancer tissue and colonic adenoma tissue. More importantly, it was found that SMO and GLI1 expressions possibly increased gradually from the normal colon to colonic adenoma to the colon cancer. Furthermore, no distinct correlations were detected between the expression levels of SMO and GLI1 and clinicopathological parameters, including age, gender, differentiation and Dukes stage. The present results provided some new information to the possible role of HH signaling in colon cancer progression. SMO and GLI1 maybe suggested asbiomarkers to identify colon cancerous, precancerous and normal tissues as well astherapeutic targets for colon cancer treatment.