Gear fault feature extraction and diagnosis method under different load excitation based on EMD, PSO-SVM and fractal box dimension

Dongying Han,Na Zhao,Peiming Shi 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.2

Aiming at the problem of gear fault feature extraction and fault classification under different load excitation, we present a new fault diagnosis method that combines three methods, including empirical mode decomposition (EMD), particle swarm optimization support vector machine (PSO-SVM) and fractal box dimension. First, the non-stationary original vibration signal of gear fault is decomposed into several intrinsic mode functions (IMF) by EMD method. Then, the time, frequency, energy characteristic parameters and box dimension are calculated separately from the time domain, frequency domain, energy domain and fractal domain. And then the gear fault characteristics under different load excitation are obtained. Finally, the extracted feature parameters are input into the PSO-SVM model for gear fault classification. The experimental results show that the proposed method can effectively identify gear failure types under different load excitation.

A novel intelligent fault diagnosis method based on dual convolutional neural network with multi-level information fusion

Dongying Han,Jinghui Tian,Peng Xue,Peiming Shi 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.8

Due to the complicacy of mechanical instruments and the noise interference in the working environment, the equipment status information contained in a single sensor is insufficient, and multi-source information contains more complete status information. In order to effectively fuse multi-sensor information and improve the reliability of diagnosis, a multi-level fusion dual convolution neural network (MFDCNN) for fault diagnosis of rotating machinery is proposed in this paper. This approach realizes multi-level fusion of fault information by utilizing the flexibility of the structure of the convolutional neural network. During the training process, the two subnets automatically extract representative features from the multi-sensor timedomain signal and its frequency spectrum in parallel, and then fuse the extracted features for pattern recognition to achieve end-to-end fault diagnosis. Compared with the single sensor diagnosis method and single level information fusion method, this approach has better diagnosis performance.

Yeats’s Self-Projection in “A Prayer for My Daughter”

Zhang Dongying 한국예이츠학회 2019 한국예이츠 저널 Vol.58 No.-

“A Prayer for My Daughter” has been read as one of the most famous poems about daughters in literary history. But is the daughter the true subject of the poem? This paper answers the question by rereading the poem from the perspective of Yeats’s self-projection, discovering a layer of subtext in “A Prayer for My Daughter” about Yeats’s own past, present and future, about his anxiety of aging and about his seeking for wisdom and transcendence through art under the explicit text about the father figure’s expectations on his daughter’s future.

A sparse auto-encoder method based on compressed sensing and wavelet packet energy entropy for rolling bearing intelligent fault diagnosis

Peiming Shi,Xiaoci Guo,Dongying Han,Rongrong Fu 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.4

Improving diagnostic efficiency and shortening diagnostic time is important for improving the reliability and safety of rotating machinery, and has received more and more attention. When using intelligent diagnostic methods to diagnose bearing faults, the increasingly complex working conditions and the huge amount of data make it a great challenge to diagnose fault quickly and effectively. In this paper, a novel fault diagnosis method based on sparse auto-encoder (SAE), combined with compression sensing (CS) and wavelet packet energy entropy (WPEE) for feature dimension reduction is proposed. Firstly, vibration signals of each fault type are projected linearly through compressed sensing to obtain compressed signals, which are merged into a low-dimensional compressed signal matrix of multiple fault types. Secondly, the WPEE of low-dimensional compressed signal matrix of multi-fault type is determined, and the eigenvector matrix of bearing fault diagnosis is formed, which greatly reduces the dimension of the eigenvector matrix. Finally, SAE are constructed by adding sparse penalty to auto-encoder (AE) for high-level feature learning and bearing fault classification, and it not only further learns the high-level features of data, but also reduces the feature dimension. Compared with traditional feature extraction methods and the standard deep learning method, the proposed method not only guarantees high accuracy, but also greatly reduces the diagnosis time.

Seasonal Variation and Sources of Heavy Metals in Urban Street Dusts and River Sediments of Dongluo River Watershed in Ji’nan City, China

Tingting Duan,Shanjun Liu,Dongying Wang,Jiashen Feng,Ningning Song,Yanxin Yu,Yingxia Li 대한토목학회 2020 KSCE Journal of Civil Engineering Vol.24 No.5

Due to intense human activities, urban street dusts and river sediments are usually polluted by heavy metals. This study investigated the seasonal variations (summer and winter) of pollution level, spatial distribution and potential sources of heavy metals in street dusts (SD) and river sediments (RS) of Dongluo River watershed, Ji’nan, China. Results showed cadmium (Cd), chromium (Cr), copper (Cu), lead (Pb) and zinc (Zn) contents were 3.5 (8.6), 3.0 (1.4), 4.9 (2.6), 4.1 (1.1) and 3.9 (4.0)-fold of their geochemical soil background values (BGV) of Shandong Province, China in summer (winter). Mercury (Hg) and arsenic (As) contents were lower than their background values in both summer and winter. The differences of Cd, Cr, Cu, Pb, Zn, Mn and As contents in river sediments between winter and summer were not statistically significant. The correlation coefficients between metal contents and the distance between sampling sites and the east-west boundary of study area were higher in summer than those in winter. Based on principal component analysis (PCA) and multivariate linear regression (MLR), coal combustion and traffic-related sources were the main sources in winter, and agricultural activities and traffic-related sources were the main sources in summer.

Effect of Discharge Energy of Magnetic Pulse Compaction on the Powder Compaction Characteristics and Spring Back Behavior of Copper Compacts

Junjia Cui,Xushi Huang,Dongying Dong,Guangyao Li 대한금속·재료학회 2021 METALS AND MATERIALS International Vol.27 No.9

Magnetic pulse compaction (MPC) technology had unique compaction advantages compared to traditional powder compactionmethods. In this study, the pure copper compacts have been consolidated by MPC technique. The effect of dischargeenergy on the microstructures, relative density, micro hardness, strain and stress of copper compacts were analyzed viaoptical microscopy, scanning electron microscopy, hardness tester and FEM simulation. The relationship between dischargeenergy and spring back was analyzed by numerical calculation. Results showed that the MPC method had the advantages torefine powder particles. The relative density of copper compacts reached 96% when the discharge energy was 9 kJ. Stressconcentration was occurred at the upper edge of the powder body, and propagated to the upper center, lower edge and middleposition of the powder body. The powder body could have a uniform strain distribution in a short period of time whenthe discharge energy was greater than 7 kJ. There was a linear relationship between the relative density and the logarithm ofVickers hardness. The axial and radial spring back both increased with the increase of discharge energy. When the dischargeenergy was 9 kJ, the axial and radial spring back was 2.36% and 0.42%.

Hypoglycemic and Antioxidant Activities of Paeonol and Its Beneficial Effect on Diabetic Encephalopathy in Streptozotocin-Induced Diabetic Rats

Jiping Liu,Shuyuan Wang,Liang Feng,Dongying Ma,Qiang Fu,Yu Song,Xiaobin Jia,Shiping Ma 한국식품영양과학회 2013 Journal of medicinal food Vol.16 No.7

Diabetic encephalopathy (DE) is one of the severe complications in patients with diabetes mellitus. Paeonol, an active compound isolated from the root bark of Paeonia suffruticosa, has significant antidiabetic activity in vivo. However, its underlying beneficial effects on DE were unclear. In the present study, the protective activity of paeonol on DE was evaluated in streptozotocin (STZ)-induced diabetic rats. Paeonol at 50 and 100mg/kg significantly increased body weight and decreased blood glucose levels, glycosylated serum proteins, and serum advanced glycation end products (AGEs) levels. Immunohistochemistry assays and Western blot analysis revealed a significant decrease in expressions on receptor for advanced glycation end products (RAGE) and nuclear factor kappa B (NF-jB) in hippocampus and cerebral cortical neurons after paeonol treatment. Furthermore, paeonol significantly increased glutathione content and remarkedly decreased induced nitric oxide synthase activity in hippocampus tissue. Our findings indicated that paeonol could improve the pathological damage of DE in STZ-induced diabetic rats. It might be associated with the modulating AGEs/RAGE/NF-jB pathway. This study suggested that paeonol might be a promising candidate for the prevention and treatment of DE.

A new iterative near-field coherent subspace method for rub-impact fault localization using AE technique

Jing Li,Aidong Deng,Yong Yang,Xinmin Cheng,Dongying Liu,Li Zhao 대한기계학회 2017 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.31 No.5

Acoustic emission (AE) localization is an important method to detect defects in bearing of rotatory machine for faults maintenance. However, only the faults near the sensor array can be detected due to severe attenuation in the recorded AE signals. Therefore, we propose a highly reliable new Iterative near-field Coherent subspace method (IN-CSM) for multiple rub-impact faults localization. The proposed approach contains four improved processes: Modal plate wave theory (MPWT) analysis for the multi-modes decomposition and group velocity revision; Discrete wavelet transform (DWT) for the useful narrow band extraction; Near field Multiple signal classification (N-MUSIC) method for the preliminary position estimations; the IN-CSM algorithm for the multiple coherent sources separation and the precise localizations. The simulations based on N-MUSIC and IN-CSM methods were compared by rubbing teston the test rig of rotation machinery. The results indicate that the proposed method can effectively locate multiple coherent rubbing faults at once. Thus, it is an effective analysis tool for rub-impact fault detection.

A Defect in Zinc Finger Protein Double B-box 1a (DBB1a) Causes Abnormal Floral Development in Arabidopsis

Qiming Wang,Xiaoju Tu,Keqin Deng,Jianxin Zeng,Xiaoying Zhao,Dongying Tang,Xuanming Liu 한국식물학회 2009 Journal of Plant Biology Vol.52 No.6

The double B-box (DBB) type zinc finger protein has thus far been shown to be involved in photomorphegenesis in Arabidopsis thaliana. Here, we show that DBB1a is expressed in the embryo, cytolden, and flower. Misexpression of DBB1a in mutant plants resulted in abnormal numbers and patterns of floral organs. We further show that DBB1a could regulate expression of several floral homeotic genes, including APETALA 2, APETALA 3, PISTILLATA, and AGAMOUS. Interestingly, expression of the microRNA gene MiR172, which is involved in organ boundary establishment, was also misregulated in the dbb1a mutant plants. Our study identified a previously uncharacterized role of DDB1a in regulation of expression of floral homeotic genes and miR172, which is important for understanding of floral pattern formation.

Across-fault distributions of radon concentrations in soil gas for different tectonic environments

Xiaolong Sun,Pengtao Yang,Yang Xiang,Xueyun Si,Dongying Liu 한국지질과학협의회 2018 Geosciences Journal Vol.22 No.2

The radon concentration in soil gas at the crustal surface is closely related to the development of local fractures. Therefore, the spatial variation in soil-gas radon concentrations across faults differs between fault zones located in different tectonic environments. This study investigates the Beiluntai thrust fault and the Haiyuan strike-slip fault in west China, which are assumed to be typical examples of the different types of faults. Radon concentrations in soil gas were measured at various locations around these faults, and the spatial variation in these concentrations was characterized along fault-perpendicular profiles. For normal fault, our results indicate that concentrations are usually highest at the fault, and decrease gradually away from the fault in either direction. For thrust, however, concentrations increase in the hanging wall approaching the fault, but decrease sharply at the fault. For strike-slip fault with extension across the fault, soil-gas radon concentrations are relatively high at the fault, and decrease gradually with distance from the fault. For strike-slip fault with contraction across the fault, soil-gas radon concentrations are relatively low at the fault, and increase gradually with distance from the fault.