Fault diagnosis based on the integration of exponential discriminant analysis and local linear embedding

2017 ◽  
Vol 96 (2) ◽  
pp. 463-483 ◽  
Author(s):  
Ruixuan Wang ◽  
Jing Wang ◽  
Jinglin Zhou ◽  
Haiyan Wu
Keyword(s):  
Fault Diagnosis ◽  
Discriminant Analysis ◽  
Local Linear Embedding ◽  
Local Linear ◽  
Linear Embedding

scholarly journals Fault Diagnosis of Supervision and Homogenization Distance Based on Local Linear Embedding Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Guangbin Wang ◽  
Jun Luo ◽  
Yilin He ◽  
Qinyi Chen
Keyword(s):  
Fault Diagnosis ◽  
Dimension Reduction ◽  
Supervised Learning ◽  
Recognition Performance ◽  
Locally Linear Embedding ◽  
Local Linear Embedding ◽  
Local Linear ◽  
Fault Recognition ◽  
Linear Embedding ◽  
Sample Points

In view of the problems of uneven distribution of reality fault samples and dimension reduction effect of locally linear embedding (LLE) algorithm which is easily affected by neighboring points, an improved local linear embedding algorithm of homogenization distance (HLLE) is developed. The method makes the overall distribution of sample points tend to be homogenization and reduces the influence of neighboring points using homogenization distance instead of the traditional Euclidean distance. It is helpful to choose effective neighboring points to construct weight matrix for dimension reduction. Because the fault recognition performance improvement of HLLE is limited and unstable, the paper further proposes a new local linear embedding algorithm of supervision and homogenization distance (SHLLE) by adding the supervised learning mechanism. On the basis of homogenization distance, supervised learning increases the category information of sample points so that the same category of sample points will be gathered and the heterogeneous category of sample points will be scattered. It effectively improves the performance of fault diagnosis and maintains stability at the same time. A comparison of the methods mentioned above was made by simulation experiment with rotor system fault diagnosis, and the results show that SHLLE algorithm has superior fault recognition performance.

Dimensionality reduction of tensor data based on local linear embedding and mode product

2021 ◽  
pp. 1-18
Author(s):  
Ting Gao ◽  
Zhengming Ma ◽  
Wenxu Gao ◽  
Shuyu Liu
Keyword(s):  
Dimensionality Reduction ◽  
Learning Algorithm ◽  
Vector Data ◽  
Global Features ◽  
Global Mode ◽  
Local Linear Embedding ◽  
Local Linear ◽  
Clustering And Classification ◽  
Linear Embedding ◽  
Tensor Data

There are three contributions in this paper. (1) A tensor version of LLE (short for Local Linear Embedding algorithm) is deduced and presented. LLE is the most famous manifold learning algorithm. Since its proposal, various improvements to LLE have kept emerging without interruption. However, all these achievements are only suitable for vector data, not tensor data. The proposed tensor LLE can also be used a bridge for various improvements to LLE to transfer from vector data to tensor data. (2) A framework of tensor dimensionality reduction based on tensor mode product is proposed, in which the mode matrices can be determined according to specific criteria. (3) A novel dimensionality reduction algorithm for tensor data based on LLE and mode product (LLEMP-TDR) is proposed, in which LLE is used as a criterion to determine the mode matrices. Benefiting from local LLE and global mode product, the proposed LLEMP-TDR can preserve both local and global features of high-dimensional tenser data during dimensionality reduction. The experimental results on data clustering and classification tasks demonstrate that our method performs better than 5 other related algorithms published recently in top academic journals.

scholarly journals A Fast Radio Map Construction Method Merging Self-Adaptive Local Linear Embedding (LLE) and Graph-Based Label Propagation in WLAN Fingerprint Localization Systems

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 767 ◽  
Author(s):  
Yepeng Ni ◽  
Jianping Chai ◽  
Yan Wang ◽  
Weidong Fang
Keyword(s):  
Label Propagation ◽  
Positioning Accuracy ◽  
Local Linear Embedding ◽  
Map Construction ◽  
Local Linear ◽  
Linear Embedding ◽  
Fingerprint Database ◽  
Radio Map ◽  
Localization Systems ◽  
Self Adaptive

Indoor WLAN fingerprint localization systems have been widely applied due to the simplicity of implementation on various mobile devices, including smartphones. However, collecting received signal strength indication (RSSI) samples for the fingerprint database, named a radio map, is significantly labor-intensive and time-consuming. To solve the problem, this paper proposes a semi-supervised self-adaptive local linear embedding algorithm to build the radio map. First, this method uses the self-adaptive local linear embedding (SLLE) algorithm based on manifold learning to reduce the dimension of the high-dimensional RSSI samples and to extract a neighbor weight matrix. Secondly, a graph-based label propagation (GLP) algorithm is employed to build the radio map by semi-supervised learning from a large number of unlabeled RSSI samples to a few labeled RSSI samples. Finally, we propose a k self-adaptive neighbor weight (kSNW) algorithm, used for radio map construction in this paper, to realize online localization. The results of the experiments conducted in a real indoor environment show that the proposed method reduces the demand for large quantities of labeled samples and achieves good positioning accuracy. With only 25% labeled RSSI samples, our system can obtain positioning accuracy of more than 88%, within 3 m of localization errors.

Supervised Locally Linear Embedding for Fault Diagnosis

2010 ◽  
Vol 139-141 ◽  
pp. 2599-2602
Author(s):  
Zheng Wei Li ◽  
Ru Nie ◽  
Yao Fei Han
Keyword(s):  
Fault Diagnosis ◽  
Recognition Performance ◽  
Feature Space ◽  
Difficult Problem ◽  
Fault Classification ◽  
High Dimensional ◽  
Locally Linear Embedding ◽  
Local Linear Embedding ◽  
Linear Embedding ◽  
Locally Linear

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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