A new method for fault feature extraction of a complex electromechanical system

2015 ◽  
Author(s):  
S. Kai
Keyword(s):  
Feature Extraction ◽  
Electromechanical System ◽  
New Method ◽  
Fault Feature Extraction

A new method for weak fault feature extraction based on piecewise mixed stochastic resonance

2020 ◽  
Vol 68 ◽  
pp. 87-99
Author(s):  
Jiachen Tang ◽  
Boqiang Shi ◽  
Huiru Bao ◽  
Zhixing Li
Keyword(s):  
Feature Extraction ◽  
Stochastic Resonance ◽  
New Method ◽  
Weak Fault ◽  
Fault Feature Extraction

An improved Laplacian Eigenmaps method for machine nonlinear fault feature extraction

2017 ◽  
Vol 232 (21) ◽  
pp. 3833-3842 ◽  
Author(s):  
Quansheng Jiang ◽  
Qixing Zhu ◽  
Wei Liu ◽  
Bangfu Wang ◽  
Fengyu Xu
Keyword(s):  
Feature Extraction ◽  
Manifold Learning ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Extraction Process ◽  
New Method ◽  
K Nearest Neighbor ◽  
Laplacian Eigenmaps ◽  
Local Linear Embedding ◽  
Fault Feature Extraction

In the feature extraction of mechanical fault detection field, manifold learning is one of the effective nonlinear techniques. In this paper, aiming for the situations of noise sensitivity to manifold learning algorithms, an improved Laplacian Eigenmap (I-LapEig) algorithm is proposed and applied to the process of fault feature extraction. The new method takes advantage of local principal component analysis to eliminate the influence of noise points by reconstructing the neighborhood relation amongst the samples, and maintain the global intrinsic manifold structure, which enhances the performance of the feature extraction. To determine the parameters of I-LapEig algorithm, an adaptive neighborhood choose approach is presented. The K-nearest neighbor classifier is also adopted to implement feature classification and recognition. The experimental results on S-curve, rotor bed data, and compressor fault data show that the new method can effectively improve the performance of noise reduction in the feature extraction process when compared with the conventional local linear embedding and Laplacian Eigenmaps.

Research of Fault Feature Extraction Based on High Order Cyclic Statistics for Reciprocating Compressor Gas Valves

2010 ◽  
Vol 44-47 ◽  
pp. 2094-2098
Author(s):  
Shu Lin Liu ◽  
You Fu Tang ◽  
Ji Cheng Liu ◽  
Ying Hui Liu
Keyword(s):  
Feature Extraction ◽  
New Method ◽  
Reciprocating Compressor ◽  
Third Order ◽  
Vibration Signals ◽  
The Third ◽  
Signal Features ◽  
Fault Features ◽  
Cyclic Statistics ◽  
Fault Feature Extraction

. This paper proposes an approach of fault feature extraction for reciprocating compressor gas valves based on theory of cyclic statistics. First, the strength and weakness of the third-order cyclic statistics in extracting signal features are investigated by simulation signals. Since vibration signals for reciprocating compressor gas valves are of typical cyclic stationary, a new method of fault feature extraction is then proposed based on the simulation results. The method utilizes the cyclic bi-spectrum to extract fault features for the corresponding frequencies. The results show that the cyclic bi-spectrum characteristics for typical faults of gas valves are apparently different, and that the typical faults of reciprocating compressor gas valves can be diagnosed exactly. So the new method proposed in this paper is effective and feasible.

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