A novel method for self-adaptive feature extraction using scaling crossover characteristics of signals and combining with LS-SVM for multi-fault diagnosis of gearbox

Vibration signals of defective gears are usually non-stationary and masked by noise. As a result, the feature extraction of gear fault data is always an intractable problem, especially for multi-fault couple system (two or more fault types simultaneously occur in mechanical systems). Recently, an interesting crossover characteristic of nonlinear data is used to diagnose the different severities of gear faults. Nonetheless, it lacks of self-adaptivity. Consequently, a novel method for self-adaptive feature extraction using scaling crossover characteristics of signals and combining with least square support vector machine (LS-SVM) for multi-fault diagnosis of gearbox is proposed. Firstly, detrended fluctuation analysis (DFA) is introduced to analyze fractal properties and multi-scaling behaviors of vibration signal from multi-fault gearbox. The scale exponents are abrupt changed with the gradual increasing of time scales, which can be observed in the scaling-law curve. Secondly, a criterion based on a Quasi-Monte Carlo algorithm is developed to uncover optimal scaling intervals of scaling-law curve. Several different scaling regions are objectively measured in each of which a single scale exponent can be estimated. Thirdly, a three-dimensional vector, containing three scale exponents which carry definite physical meaning, is used as the feature parameter to describe the underlying dynamic mechanism hidden in gearbox vibration data. Lastly, these vectors are classified by LS-SVM. Moreover, the method of statistical parameters is exploited to classify the multi-fault vibration data which have been investigated by proposed method. The results show that the proposed method is sensitive to multi-fault vibration data of gearbox with similar fault patterns and has a better performance than other methods

On the analysis of a piecewise nonlinear-linear vibration isolator with high-static-low-dynamic-stiffness under base excitation

A piecewise nonlinear vibration isolator with high-static-low-dynamic-stiffness (HSLDS) is presented in this paper. This nonlinear vibration isolator is comprised of a vertical spring and two pre-compressed cam-roller-spring mechanisms used as the stiffness correctors. Firstly, the static analysis of the vibration isolator is analyzed. The primary resonance of the system under harmonic base excitation is derived by applying the averaging method and further verified by the direct numerical integration. The effect of base excitation amplitude and damping ratio on the resonance frequency is considered. The stability analysis of the primary resonance is also studied. Then, the frequency island phenomenon is found and confirmed by numerical method. The parameter analysis on the appearance of frequency island is also considered. Finally, the absolute displacement transmissibility of the vibration isolator is defined and compared with the conventional HSLDS vibration isolator and the equivalent linear one. The results show that it exhibits a wider frequency range of vibration isolation than the equivalent linear vibration isolator. When the base excitation amplitude takes a larger value, the unbounded response which occurs in the conventional HSLDS vibration isolator can also be avoided, then a better isolation performance can be achieved

Response and performance of a nonlinear vibration isolator with high-static-low-dynamic-stiffness under shock excitations

A nonlinear vibration isolator with High-Static-Low-Dynamic-Stiffness (HSLDS) characteristic comprised of vertical spring and horizontal spring is presented in this paper. Response of the nonlinear vibration isolator under three different kinds of base shock excitations is considered, the dynamic motion can be approximately described by the classic Duffing equation. A transformation function and ultra-spherical polynomial approximation method are employed to determine the shock response and compared with numerical method. Then performance of the nonlinear vibration isolator under shock excitations is evaluated by three performance indicies (Maximum Absolute Displacement Ratio (MADR), Maximum Relative Displacement Ratio (MRDR) and Maximum Acceleration Ratio (MAR)), and also compared with a linear one. Results show that the analytic method suits for weak nonlinearity and the performance of the nonlinear vibration isolator under shock excitations is greatly influenced by the input shock magnitude and structural parameters

A Study on Defect Identification of Planetary Gearbox under Large Speed Oscillation

Rotational speed of a reference shaft is the key information for planetary gearbox condition monitoring under nonstationary conditions. As the time-variant speed and load of planetary gearboxes result in time-variant characteristic frequencies as well as vibration magnitudes, the conventional methods tracking time-frequency ridge perform a poor robustness, especially for large speed variations. In this paper, two schemes, time-frequency ridge fusion and logarithm transformation, are proposed to track the targeted ridge curve reliably. Meanwhile, the identified ridge curve by logarithm scheme can be further refined by the time-frequency ridge fusion scheme. Hence, a procedure involving the proposed ridge estimation methods is presented to diagnose the planetary gearbox defects. Two simulation signals and a vibration signal collected from a planetary gearbox in practical engineering (provided by the conference on condition monitoring of machinery in nonstationary operations (CMMNO)) are used to verify the proposed methods. It is validated that the proposed methods can well-track the targeted ridge curve compared with two conventional methods. As a result, the characteristic frequency of each component in the planetary gearbox is clearly demonstrated and the inner race defect of one of the planet bearings is successfully discovered in the order spectrum depending on the derived expression of planet bearing fault frequency

Reduced expression of Metastasis Suppressor-1 (MTSS1) accelerates progression of human bladder uroepithelium cell carcinoma

Background: Metastasis suppressor 1 (MTSS1) is a multi-functional cytoskeletal protein. Recent research showed that MTSS1 is a potential tumor suppressor in many types of cancer cells, including kidney and bladder cancer cells. However, the clinical implication of MTSS1 in human bladder uroepithelium cell carcinoma (BUCC) and its potential in suppressing BUCC tumorigenesis remains undetermined. In the present study, the expression of MTSS1 in human BUCC tissue samples, and correlations between MTSS1 and pathological grade and stage of the tumors were examined in BUCC specimens. The function of MTSS1 in BUCC progression was explored. Materials and Methods: The mRNA and protein expression of MTSS1 were examined in 68 BUCC tissue samples with matching adjacent normal bladder tissues using quantitative real-time PCR and western blotting. Furthermore, the bladder cancer cell line 5637 was used to determine the anticancer effect of MTSS1. Results: Lower MTSS1 mRNA expression was recorded in BUCC tissues compared to normal bladder tissues. A lower MTSS1 mRNA level was observed in tumors with high clinical stage and with high pathological nuclear grade. Likewise, MTSS1 protein expression in normal bladder tissue was significantly higher than that in BUCC tissue. The protein level of MTSS1 significantly negatively correlated with clinical stage and pathological nuclear grade of BUCC. Cumulative survival curves indicated that MTSS1 expression was negatively correlated with survival time: patients with a high level of MTSS1 had significantly longer survival time than those with a low level of MTSS1 (p<0.001). Overexpression of MTSS1 reduced BUCC cell proliferation, cell-cycle progression and colony formation, but had no influence on BUCC cell apoptosis. Conclusion: Overexpression of MTSS1 suppresses BUCC development, providing a novel perspective for BUCC tumorigenesis and a potential therapeutic target for BUCC

DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue

Different from Visual Question Answering task that requires to answer only one question about an image, Visual Dialogue involves multiple questions which cover a broad range of visual content that could be related to any objects, relationships or semantics. The key challenge in Visual Dialogue task is thus to learn a more comprehensive and semantic-rich image representation which may have adaptive attentions on the image for variant questions. In this research, we propose a novel model to depict an image from both visual and semantic perspectives. Specifically, the visual view helps capture the appearance-level information, including objects and their relationships, while the semantic view enables the agent to understand high-level visual semantics from the whole image to the local regions. Futhermore, on top of such multi-view image features, we propose a feature selection framework which is able to adaptively capture question-relevant information hierarchically in fine-grained level. The proposed method achieved state-of-the-art results on benchmark Visual Dialogue datasets. More importantly, we can tell which modality (visual or semantic) has more contribution in answering the current question by visualizing the gate values. It gives us insights in understanding of human cognition in Visual Dialogue.Comment: Accepted by the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2020

An automatic feature extraction method and its application in fault diagnosis

The main challenge of fault diagnosis is to extract excellent fault feature, but these methods usually depend on the manpower and prior knowledge. It is desirable to automatically extract useful feature from input data in an unsupervised way. Hence, an automatic feature extraction method is presented in this paper. The proposed method first captures fault feature from the raw vibration signal by sparse filtering. Considering that the learned feature is high-dimensional data which cannot achieve visualization, t-distributed stochastic neighbor embedding (t-SNE) is further selected as the dimensionality reduction tool to map the learned feature into a three-dimensional feature vector. Consequently, the effectiveness of the proposed method is verified using gearbox and bearing experimental datas. The classification results show that the hybrid method of sparse filtering and t-SNE can well extract discriminative information from the raw vibration signal and can clearly distinguish different fault types. Through comparison analysis, it is also validated that the proposed method is superior to the other methods

Comparative investigations of the crystal structure and photoluminescence property of eulytite-type Ba3Eu(PO4)3 and Sr3Eu(PO4)3

In this study, the Ba3Eu(PO4)3 and Sr3Eu(PO4)3 compounds were synthesized and the crystal structures were determined for the first time by Rietveld refinement using powder X-ray diffraction (XRD) patterns. Ba3Eu-(PO4)3 crystallizes in cubic space group I4¯3d, with cell parameters of a = 10.47996(9) Å, V = 1151.01(3) Å3 and Z = 4; Ba2+ and Eu3+ occupy the same site with partial occupancies of 3/4 and 1/4, respectively. Besides, in this structure, there exists two distorted kinds of the PO4 polyhedra orientation. Sr3Eu(PO4)3 is isostructural to Ba3Eu(PO4)3 and has much smaller cell parameters of a = 10.1203(2) Å, V = 1036.52(5) Å3. The bandgaps of Ba3Eu(PO4)3 and Sr3Eu(PO4)3 are determined to be 4.091 eV and 3.987 eV, respectively, based on the UV–Vis diffuse reflectance spectra. The photoluminescence measurements reveal that, upon 396 nm n-UV light excitation, Ba3Eu(PO4)3 and Sr3Eu(PO4)3 exhibit orange-red emission with two main peaks at 596 nm and prevailing 613 nm, corresponding to the 5D0 → 7F1 and 5D0 → 7F2 transitions of Eu3+, respectively. The dynamic disordering in the crystal structures contributes to the broadening of the luminescence spectra. The electronic structure of the hosphates was calculated by the first-principles method. The analysis elucidats that the band structures are mainly governed by the orbits of phosphorus, oxygen and europium, and the sharp peaks of the europium f-orbit occur at the top of the valence bands

Effect of prodigiosin on the alleviation of the intestinal inflammation of weaned rats based on 1H-NMR spectroscopy study and biochemistry indexes

Weaning results in intestinal dysfunction, mucosal atrophy, transient anorexia, and intestinal barrier defects. In this study, the effect of prodigiosin (PG) on the intestinal inflammation of weaned rats was investigated by using 1 H-NMR spectroscopy and biochemistry indexes to regulate the intestinal metabolism. After administration for 14 days, the body mass of the PG group was increased by 1.29‑ and 1.26-fold compared with those of the control and alcohol groups, respectively, using a dose of 200 μg PG·kg-1 body weight per day. PG increased organic acid content and decreased moisture, pH values, and free ammonia in feces. In addition, PG alleviated the intestinal inflammation of weaned rats. The analysis of 1 H-NMR signal peak attribution and the model validation of metabolic data of feces contents showed that PG significantly affected the metabolism of small molecular compounds in the intestinal tract of weaned rats. This study presents the promising alternative of using PG to alleviate intestinal inflammation effectively in the intestinal tract of weaned rats