Fault diagnosis of rotating machinery based on time-frequency decomposition and envelope spectrum analysis

In order to raise the working reliability of rotating machinery in real applications and reduce the loss caused by unintended breakdowns, a new method based on improved ensemble empirical mode decomposition (EEMD) and envelope spectrum analysis is proposed for fault diagnosis in this paper. First, the collected vibration signals are decomposed into a series of intrinsic mode functions (IMFs) by the improved EEMD (IEEMD). Then, the envelope spectrums of the selected decompositions of IEEMD are analyzed to calculate the energy values within the frequency bands around speed and bearing fault characteristic frequencies (CDFs) as features for fault diagnosis based on support vector machine (SVM). Experiments are carried out to test the effectiveness of the proposed method. Experimental results show that the proposed method can effectively extract fault characteristics and accurately realize classification of bearing under normal, inner race fault, ball fault and outer race fault

MVF-Net: Multi-View 3D Face Morphable Model Regression

We address the problem of recovering the 3D geometry of a human face from a set of facial images in multiple views. While recent studies have shown impressive progress in 3D Morphable Model (3DMM) based facial reconstruction, the settings are mostly restricted to a single view. There is an inherent drawback in the single-view setting: the lack of reliable 3D constraints can cause unresolvable ambiguities. We in this paper explore 3DMM-based shape recovery in a different setting, where a set of multi-view facial images are given as input. A novel approach is proposed to regress 3DMM parameters from multi-view inputs with an end-to-end trainable Convolutional Neural Network (CNN). Multiview geometric constraints are incorporated into the network by establishing dense correspondences between different views leveraging a novel self-supervised view alignment loss. The main ingredient of the view alignment loss is a differentiable dense optical flow estimator that can backpropagate the alignment errors between an input view and a synthetic rendering from another input view, which is projected to the target view through the 3D shape to be inferred. Through minimizing the view alignment loss, better 3D shapes can be recovered such that the synthetic projections from one view to another can better align with the observed image. Extensive experiments demonstrate the superiority of the proposed method over other 3DMM methods.Comment: 2019 Conference on Computer Vision and Pattern Recognitio

Mx2M: Masked Cross-Modality Modeling in Domain Adaptation for 3D Semantic Segmentation

Existing methods of cross-modal domain adaptation for 3D semantic segmentation predict results only via 2D-3D complementarity that is obtained by cross-modal feature matching. However, as lacking supervision in the target domain, the complementarity is not always reliable. The results are not ideal when the domain gap is large. To solve the problem of lacking supervision, we introduce masked modeling into this task and propose a method Mx2M, which utilizes masked cross-modality modeling to reduce the large domain gap. Our Mx2M contains two components. One is the core solution, cross-modal removal and prediction (xMRP), which makes the Mx2M adapt to various scenarios and provides cross-modal self-supervision. The other is a new way of cross-modal feature matching, the dynamic cross-modal filter (DxMF) that ensures the whole method dynamically uses more suitable 2D-3D complementarity. Evaluation of the Mx2M on three DA scenarios, including Day/Night, USA/Singapore, and A2D2/SemanticKITTI, brings large improvements over previous methods on many metrics

Population Structure and Genetic Diversity in a Rice Core Collection (Oryza sativa L.) Investigated with SSR Markers

The assessment of genetic diversity and population structure of a core collection would benefit to make use of these germplasm as well as applying them in association mapping. The objective of this study were to (1) examine the population structure of a rice core collection; (2) investigate the genetic diversity within and among subgroups of the rice core collection; (3) identify the extent of linkage disequilibrium (LD) of the rice core collection. A rice core collection consisting of 150 varieties which was established from 2260 varieties of Ting's collection of rice germplasm were genotyped with 274 SSR markers and used in this study. Two distinct subgroups (i.e. SG 1 and SG 2) were detected within the entire population by different statistical methods, which is in accordance with the differentiation of indica and japonica rice. MCLUST analysis might be an alternative method to STRUCTURE for population structure analysis. A percentage of 26% of the total markers could detect the population structure as the whole SSR marker set did with similar precision. Gene diversity and MRD between the two subspecies varied considerably across the genome, which might be used to identify candidate genes for the traits under domestication and artificial selection of indica and japonica rice. The percentage of SSR loci pairs in significant (P<0.05) LD is 46.8% in the entire population and the ratio of linked to unlinked loci pairs in LD is 1.06. Across the entire population as well as the subgroups and sub-subgroups, LD decays with genetic distance, indicating that linkage is one main cause of LD. The results of this study would provide valuable information for association mapping using the rice core collection in future

Fault diagnosis of rotating machinery based on time-frequency decomposition and envelope spectrum analysis

In order to raise the working reliability of rotating machinery in real applications and reduce the loss caused by unintended breakdowns, a new method based on improved ensemble empirical mode decomposition (EEMD) and envelope spectrum analysis is proposed for fault diagnosis in this paper. First, the collected vibration signals are decomposed into a series of intrinsic mode functions (IMFs) by the improved EEMD (IEEMD). Then, the envelope spectrums of the selected decompositions of IEEMD are analyzed to calculate the energy values within the frequency bands around speed and bearing fault characteristic frequencies (CDFs) as features for fault diagnosis based on support vector machine (SVM). Experiments are carried out to test the effectiveness of the proposed method. Experimental results show that the proposed method can effectively extract fault characteristics and accurately realize classification of bearing under normal, inner race fault, ball fault and outer race fault

Method for Predicting Transverse Wave Velocity Using a Gated Recurrent Unit Based on Spatiotemporal Attention Mechanism

Transverse wave velocity plays an important role in seismic exploration and reservoir assessment in the oil and gas industry. Due to the lack of transverse wave velocity data from actual production activities, it is necessary to predict transverse wave velocity based on longitudinal wave velocity and other reservoir parameters. This paper proposes a fusion network based on spatiotemporal attention mechanism and gated recurrent unit (STAGRU) due to the significant correlation between the transverse wave velocity and reservoir parameters in the spatiotemporal domain. In the case of tight sandstone reservoirs in the Junggar Basin, the intersection plot technique is used to select four well logging parameters that are sensitive to transverse wave velocity: longitudinal wave velocity, density, natural gamma, and neutron porosity. The autocorrelation technique is employed to analyze the depth-related correlation of well logging curves. The relationship between the spatiotemporal characteristics of these well logging data and the network attention weights is also examined to validate the rationale behind incorporating the spatiotemporal attention mechanism. Finally, the actual measurement data from multiple wells are utilized to analyze the performance of the training set and test set separately. The results indicate that the predictive accuracy and generalization ability of the proposed STAGRU method are superior to the single-parameter fitting method, multiparameter fitting method, Xu-White model method, GRU network, and 2DCNN-GRU hybrid network. This demonstrates the feasibility of the transverse wave velocity prediction method based on the spatiotemporal attention mechanism in the study of rock physics modeling for tight sandstone reservoirs

Study on failure mechanism and application of double-layer structure floor with large buried depth and high confined water

The first mining of nearly whole rock lower protective layer working face in Pingdingshan coal mining area is used to liberate the Ji group coal resources of its upper threatened by the gas outburst. The mining of the rock layer at a depth of nearly 1000 meters is bound to increase the depth of the floor damage. Once the L5 weak water-rich aquifer in the aquifuge is disturbed, the indirect recharge channel of the cold ash water is formed, which affects the safety and stability of the rock floor. Firstly, the theoretical model of plastic slip line of double-layer structure floor is established, and the analytical solution of maximum failure depth of double-layer floor under three working conditions is derived. Then through the self-designed similar simulation experimental platform of pore water pressure (spring) and stratum effective stress (jack), the deformation form and failure characteristics of stope roof and floor are simulated and analyzed based on digital image correlation technology. Finally, the borehole strain measurement method was used to carry out on-site monitoring of floor fracture development morphology in Ji15-31040 nearly whole rock working face of Pingdingshan No.12 Coal Mine. The results show that the maximum failure depth of Ji15-31040 nearly whole rock working face floor is 16.59 m by using the plastic slip line theory of double-layer structure floor. The similar simulation experiment reveals that the floor failure is concentrated at both ends of the open-off cut and the working face, with obvious lagging failure characteristics. The maximum failure depth is 17.8 m. After the working face advances 159.9 m into full mining, the floor stress gradually recovers. The field measurement results show that the floor rock mass has a compression-shear slip failure at 7.9 m in front of the working face. The floor before and after the working face is pushed through the borehole shows compression-shear and tension-shear failure, respectively. The maximum failure depth of the floor is between 16.5 m and 18 m. The results of field measurement are in good agreement with theoretical calculation and similar simulation test

Shear-Wave Velocity Prediction Method via a Gate Recurrent Unit Fusion Network Based on the Spatiotemporal Attention Mechanism

AbstractCompression-wave velocity and shear-wave velocity are important elastic parameters describing deeply tight sandstone. Limited by cost and technical reasons, the conventional logging data generally lack shear-wave velocity. In addition, the existing rock physics theory is difficult to accurately establish the rock physics models due to the complex pore structure of tight sandstone reservoir. With the rapid development of the artificial intelligence, the attention mechanism that can increase the sensitivity of the network to important characteristics has been widely used in machine translation, image processing, and other fields, but it is rarely used to predict shear-wave velocity. Based on the correlation between the shear-wave velocity and the conventional logging data in the spatiotemporal direction, a gate recurrent unit (GRU) fusion network based on the spatiotemporal attention mechanism (STAGRU) is proposed. Compared with the convolutional neural network (CNN) and gate recurrent unit (GRU), the network proposed can improve the sensitivity of the network to important spatiotemporal characteristics using the spatiotemporal attention mechanism. It is analyzed that the relationship between the spatiotemporal characteristics of the conventional logging data and the attention weights of the network proposed to verify the rationality of adding the spatiotemporal attention mechanism. Finally, the training and testing results of the STAGRU, CNN, and GRU networks show that the prediction accuracy and generalization of the network proposed are better than those of the other two networks

Deep learning integrating scale conversion and pedo-transfer function to avoid potential errors in cross-scale transfer

Pedo-transfer functions (PTFs) relate soil/landscape static properties to a wide range of model inputs (e.g., soil hydraulic parameters) that are essential to soil hydrological modeling. Combining PTFs and hydrological models is a powerful strategy allowing the use of soil/landscape static properties for the generalization of large-scale modeling. However, since the spatial scales of soil hydraulic parameters required for model inputs and soil/landscape static properties are often not identical, cross-scale transfer is required, which can be a significant source of errors. Here, we investigate uncertainties in cross-scale transfer and develop an approach that avoids them. The proposed method uses the convolutional neural network (CNN) as a cross-scale transfer approach to directly map soil/landscape static properties to soil hydraulic parameters across different spatial scales. The proposed CNN approach is applied under two different estimation strategies to invert the hydraulic parameters of a soil-water balance model and subsequently the quality of the parameters is assessed. Both synthetical and real-world results around the conterminous United States indicate that in general the employed end-to-end strategy is superior to the two-step strategy. The CNN-based integrated model successfully reduces potential errors in cross-scale transfer and can be applied to other areas lacking information on hydraulic parameters or observations. The proposed method can be extended to improve parameter estimation in earth system models and enhance our understanding of key hydrological processes