Nonlinear fault detection based on locally linear embedding

2013 ◽  
Vol 11 (4) ◽  
pp. 615-622 ◽  
Author(s):  
Aimin Miao ◽  
Zhihuan Song ◽  
Zhiqiang Ge ◽  
Le Zhou ◽  
Qiaojun Wen
Keyword(s):  
Fault Detection ◽  
Locally Linear Embedding ◽  
Linear Embedding ◽  
Locally Linear

A nonlinear method for monitoring industrial process

2020 ◽  
pp. 014233122095923
Author(s):  
Yuan Li ◽  
Chengcheng Feng
Keyword(s):  
Fault Detection ◽  
Objective Function ◽  
Dimensional Space ◽  
Detection Methods ◽  
High Dimensional ◽  
Locally Linear Embedding ◽  
Industrial Case Study ◽  
Laplacian Eigenmaps ◽  
Linear Embedding ◽  
Locally Linear

Aiming at fault detection in industrial processes with nonlinear or high dimensions, a novel method based on locally linear embedding preserve neighborhood for fault detection is proposed in this paper. Locally linear embedding preserve neighborhood is a feature-mapping method that combines Locally linear embedding and Laplacian eigenmaps algorithms. First, two weight matrices are obtained by the Locally linear embedding and Laplacian eigenmaps, respectively. Subsequently, the two weight matrices are combined by a balance factor to obtain the objective function. Locally linear embedding preserve neighborhood method can effectively maintain the characteristics of data in high-dimensional space. The purpose of dimension reduction is to map the high-dimensional data to low-dimensional space by optimizing the objective function. Process monitoring is performed by constructing T2 and Q statistics. To demonstrate its effectiveness and superiority, the proposed locally linear embedding preserve neighborhood for fault detection method is tested under the Swiss Roll dataset and an industrial case study. Compared with traditional fault detection methods, the proposed method in this paper effectively improves the detection rate and reduces the false alarm rate.

scholarly journals Fault Detection Based on Modified Kernel Semi-Supervised Locally Linear Embedding

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 479-487 ◽  
Author(s):  
Yingwei Zhang ◽  
Yuanjian Fu ◽  
Zhenbang Wang ◽  
Lin Feng
Keyword(s):  
Fault Detection ◽  
Locally Linear Embedding ◽  
Linear Embedding ◽  
Locally Linear

Locally Linear Embedding Based on Relative Manifold

2009 ◽  
Vol 20 (9) ◽  
pp. 2376-2386 ◽  
Author(s):  
Gui-Hua WEN ◽  
Ting-Hui LU ◽  
Li-Jun JIANG ◽  
Jun WEN
Keyword(s):  
Locally Linear Embedding ◽  
Linear Embedding ◽  
Locally Linear

Bearing feature extraction using multi-structure locally linear embedding

2021 ◽  
Vol 428 ◽  
pp. 280-290
Author(s):  
Yuanhong Liu ◽  
Zebiao Hu ◽  
Yansheng Zhang
Keyword(s):  
Feature Extraction ◽  
Locally Linear Embedding ◽  
Linear Embedding ◽  
Locally Linear
Sign in / Sign up

Export Citation Format

Share Document