Bearing Fault Diagnosis Based on Hilbert Marginal Spectrum and Supervised Locally Linear Embedding

Fault diagnosis is a kind of pattern recognition problem and how to extract diagnosis features and improve recognition performance is a difficult problem. Local Linear Embedding (LLE) is an unsupervised non-linear technique that extracts useful features from the high-dimensional data sets with preserved local topology. But the original LLE method is not taking the known class label information of input data into account. A new characteristics similarity-based supervised locally linear embedding (CSSLLE) method for fault diagnosis is proposed in this paper. The CSSLLE method attempts to extract the intrinsic manifold features from high-dimensional fault data by computing Euclidean distance based on characteristics similarity and translate complex mode space into a low-dimensional feature space in which fault classification and diagnosis are carried out easily. The experiments on benchmark data and real fault dataset demonstrate that the proposed approach obtains better performance compared to SLLE, and it is an accurate technique for fault diagnosis.

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Modified Local Linear Embedding Algorithm for Rolling Element Bearing Fault Diagnosis

Applied Sciences ◽

10.3390/app7111178 ◽

2017 ◽

Vol 7 (11) ◽

pp. 1178 ◽

Cited By ~ 5

Author(s):

Beibei Yao ◽

Jia Su ◽

Lifeng Wu ◽

Yong Guan

Keyword(s):

Fault Diagnosis ◽

Rolling Element Bearing ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Local Linear Embedding ◽

Local Linear ◽

Rolling Element ◽

Element Bearing ◽

Linear Embedding

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Supervised locally linear embedding projection (SLLEP) for machinery fault diagnosis

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2011.05.001 ◽

2011 ◽

Vol 25 (8) ◽

pp. 3125-3134 ◽

Cited By ~ 64

Author(s):

Benwei Li ◽

Yun Zhang

Keyword(s):

Fault Diagnosis ◽

Locally Linear Embedding ◽

Linear Embedding ◽

Machinery Fault Diagnosis ◽

Locally Linear

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Machinery fault diagnosis via an improved multi-linear subspace and locally linear embedding

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331217739688 ◽

2018 ◽

Vol 40 (14) ◽

pp. 4014-4026

Author(s):

Yansheng Zhang ◽

Dong Ye ◽

Yuanhong Liu ◽

Yu Cai

Keyword(s):

Fault Diagnosis ◽

Linear Subspace ◽

Vibration Signal ◽

Vector Model ◽

Locally Linear Embedding ◽

Support Vector ◽

Multiple Sensors ◽

Diagnosis Method ◽

Linear Embedding ◽

Locally Linear

Traditional fault diagnosis methods mainly depend on the vector model to describe a signal, which will lead to information loss and the curse of dimensionality. In order to overcome these problems, in this paper an improved multi-linear subspace (MLS) method and locally linear embedding (LLE) are integrated (MLSLLE) to extract significant features. To obtain more information, first it is suggested that multiple sensors should be used to sample the vibration signal of a machine from different positions; then, these data are projected into different subspaces, where each sample is represented as a tensor form, respectively; finally, higher-order singular value decomposition and LLE are introduced to extract significant features. Thus a fault diagnosis method is proposed based on MLSLLE and support vector machines. The advantages of the proposed fault diagnosis method are validated by two real bearing data sets.

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Machinery Fault Diagnosis Based on Supervised Locally Linear Embedding

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.536-537.49 ◽

2014 ◽

Vol 536-537 ◽

pp. 49-52

Author(s):

Xiang Wang ◽

Yuan Zheng

Keyword(s):

Fault Diagnosis ◽

Learning Algorithm ◽

Classification Performance ◽

Fault Classification ◽

Locally Linear Embedding ◽

Diagnosis Method ◽

Linear Embedding ◽

Low Dimensional ◽

Machinery Fault Diagnosis ◽

Locally Linear

Fault diagnosis is essentially a kind of pattern recognition. In this paper propose a novel machinery fault diagnosis method based on supervised locally linear embedding is proposed first. The approach first performs the recently proposed manifold learning algorithm locally linear embedding on the high-dimensional fault signal samples to learn the intrinsic embedded multiple manifold features corresponding to different fault modes. Supervised locally linear embedding not only can map them into a low-dimensional embedded space to achieve fault feature extraction, but also can deal with new fault samples. Finally fault classification is carried out in the embedded manifold space. The ball bearing fault signals are used to validate the proposed fault diagnosis method. The results indicate that the proposed approach obviously improves the fault classification performance and outperforms the other traditional approaches.

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