Effect of particle packing structure on the elastic modulus of wet powder compacts analyzed by persistent homology

Ishihara, Shingo; Franks, George V.; Kano, Junya

doi:10.1016/j.apt.2022.103874

Cited by 5 publications

(1 citation statement)

References 51 publications

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“…Persistent (co)homology is a powerful tool in the field of topological data analysis (TDA), which has shown success in various domains . It has been employed in the analysis of neural data and their topology, − molecular representation schemes, identification of warning signals for financial crises, analysis of powder compacts, and the study of a complex and dynamical system. , In the study by Ando et al, 4D-CT scanning and persistent homology were utilized to analyze the pore connection mechanism in the duct structure of pure iron. This approach allowed them to predict and quantify changes in pore connection, demonstrating the application of persistent homology in studying the pore network .…”

Section: Introductionmentioning

confidence: 99%

Topological Characteristics of the Pore Network in the Tight Sandstone Using Persistent Homology

Wang,

Yan,

Wang

et al. 2024

ACS Omega

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Tight sandstone reservoirs have become important areas for unconventional reservoir development, and their pore network is a key feature for identifying tight sandstone, which affects fluid migration path and reservoir development efficiency. However, the connectivity characteristics of the pore network at different scales have remained unclear owing to the numerous pores and uneven pore shape. Here, using pore size distributions from many hundreds of tight sandstone samples and subsequent topological data analysis, we construct the topological structure of the pore network in the Yanchang Formation tight sandstone of the Ordos Basin in China and visualize the topological characteristics of the pore network with distances. We show that there are three connected groups within the pore structure of the tight sandstone. The topology of the pore network resides on a trident ring manifold, suggesting that the pore network in the tight sandstone encompasses three obvious dominant connection paths. One prominent bar on the H 0 dimension in the barcode indicates a twopoint connection from nanoscale to microscale in the pore network. Three prominent bars with varying durations on the H 1 dimension indicate the presence of three separate multipoint connections within a limited extent in the pore network. Connectivity of combined pores is good and controlled by the topological structure of the pore network. This demonstration of pore connections on a trident ring manifold provides a population-level visualization of the pore network in the tight sandstone.

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Section: Introductionmentioning

confidence: 99%

Topological Characteristics of the Pore Network in the Tight Sandstone Using Persistent Homology

Wang,

Yan,

Wang

et al. 2024

ACS Omega

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Dynamic simulation and experimental study of the effect of one-dimensional vibration on the packing of wood powder particles

Chen,

He,

Chen

et al. 2024

Particuology

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Intelligent fault diagnosis of high-speed train axle box bearings under parameter fluctuation working conditions using continuous cohomology-based meta-transfer learning framework

Li,

Meng,

et al. 2024

Structural Health Monitoring

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Due to the challenges associated with acquiring fault data from high-speed train axle box bearings operating under parameter fluctuation working conditions, the diagnostic accuracy of data-driven fault diagnosis methods often suffers from insufficient fault information. To address this issue, we propose an intelligent fault diagnosis strategy utilizing a novel continuous cohomology-based meta-transfer learning (CCMTL) framework. This approach aims to enhance fault identification accuracy by developing comprehensive feature representations despite limited data availability. Initially, acoustic emission data are transformed into a barcode chart using the continuous cohomology mechanism, from which fault topology features are derived. Subsequently, these features are input into a specially designed meta-transfer model for multi-task learning, thus forming a pre-trained model. This pre-training model undergoes further refinement both externally and internally to optimize its ability to adapt quickly to few-shot tasks. According to carry out the abovementioned steps, the proposed method can effectively overcome the challenges (e.g., data scarcity, difficulty in fault feature extraction, low robustness, and generalization ability) in the fault diagnosis of high-speed train axle box bearings under parameter fluctuation working conditions. Compared with existing fault diagnosis methods, the proposed CCMTL algorithm has three obvious advantages: (1) improve the fault diagnosis robustness of high-speed train axle box bearings, (2) reduce the cost of data annotation, and (3) adapt to a wide range of working conditions and parameter fluctuations. Experimental findings validate the effectiveness and feasibility of the proposed approach in scenarios requiring few-shot fault diagnosis. Specifically, when compared with current advanced diagnostic methods, the CCMTL method demonstrates superior diagnostic accuracy for fault diagnosis in high-speed train axle box bearings under parameter fluctuation working conditions. In conclusion, the practicality and generalization ability of the proposed CCMTL-based fault diagnosis approach are thoroughly demonstrated through comprehensive ablation studies. On the other hand, the designed CCMTL framework is not limited to specific machinery or operational conditions. With proper training and adjustments, the model can be applied to other industrial equipment with similar fault patterns and data characteristics. In summary, although this article emphasizes a specific application scenario (e.g., parameter fluctuation working conditions), the proposed method possesses broad applicability and can be extended to other similar industrial fault diagnosis tasks.

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Effect of particle packing structure on the elastic modulus of wet powder compacts analyzed by persistent homology

Cited by 5 publications

References 51 publications

Topological Characteristics of the Pore Network in the Tight Sandstone Using Persistent Homology

Topological Characteristics of the Pore Network in the Tight Sandstone Using Persistent Homology

Dynamic simulation and experimental study of the effect of one-dimensional vibration on the packing of wood powder particles

Intelligent fault diagnosis of high-speed train axle box bearings under parameter fluctuation working conditions using continuous cohomology-based meta-transfer learning framework

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