Locally Linear Embedding based on Rank-order Distance

Sun, Xiuhong; Lu, Yonggang

doi:10.5220/0005658601620169

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…The sensor is an acceleration sensor; the sensor is placed on the motor, torque sensor, and dynamometer, and then the sensor collects the bearing vibration data set. In this experiment, the vibration signal of the drive end bearing with the sampling frequency of 12 kHZ and speed of 1750 r min −1 was selected [36][37][38][39]. Four vibration samples were collected as normal samples, inner ring fault, outer ring fault, and steel ball defect fault.…”

Section: Experiments On the Cwru Datasetmentioning

confidence: 99%

A fault diagnosis method based on label-wise density-domain space learning

Su,

Hou,

Zhu

et al. 2024

Meas. Sci. Technol.

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Nonlinear space learning of fault samples is a category of common fault diagnosis methods, which usually use Euclidean distances to describe manifold structures among fault samples. However, in the nonlinear space learning, Euclidean distances lead to a potential manifold loss problem, and density structures that can constrain relationships from different viewpoints. Aiming these issues, we propose a novel fault diagnosis method based on label-wise density-domain space learning, which can learn a label-wise density-domain space with more intrinsic manifold structures. Density-constrained order graphs constructed by our method integrates different discriminative relationships from original fault samples with the help of density-domain information, and the density-domain information can effectively capture potential density information and global structure between fault samples. By density Laplacian of the graphs, we further construct a label-wise density-domain manifold space learning model, and the analytical solutions of space projections can be obtained by solving the model. Fault features directly obtained by the space projections possess well class separability. Extensive experiments on the Case Western Reserve University fault dataset and a roll bearing fault dataset from our roll bearing test platform shows the effectiveness and robustness of our method.

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Section: Experiments On the Cwru Datasetmentioning

confidence: 99%

A fault diagnosis method based on label-wise density-domain space learning

Su,

Hou,

Zhu

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

Local quasi-linear embedding based on kronecker product expansion of vectors

Niu

2021

IFS

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Locally Linear Embedding (LLE) is honored as the first algorithm of manifold learning. Generally speaking, the relation between a data and its nearest neighbors is nonlinear and LLE only extracts its linear part. Therefore, local nonlinear embedding is an important direction of improvement to LLE. However, any attempt in this direction may lead to a significant increase in computational complexity. In this paper, a novel algorithm called local quasi-linear embedding (LQLE) is proposed. In our LQLE, each high-dimensional data vector is first expanded by using Kronecker product. The expanded vector contains not only the components of the original vector, but also the polynomials of its components. Then, each expanded vector of high dimensional data is linearly approximated with the expanded vectors of its nearest neighbors. In this way, the proposed LQLE achieves a certain degree of local nonlinearity and learns the data dimensionality reduction results under the principle of keeping local nonlinearity unchanged. More importantly, LQLE does not increase computation complexity by only replacing the data vectors with their Kronecker product expansions in the original LLE program. Experimental results between our proposed methods and four comparison algorithms on various datasets demonstrate the well performance of the proposed methods.

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Locally Linear Embedding based on Rank-order Distance

Cited by 2 publications

References 19 publications

A fault diagnosis method based on label-wise density-domain space learning

A fault diagnosis method based on label-wise density-domain space learning

Local quasi-linear embedding based on kronecker product expansion of vectors

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