Fault detection algorithm of inverter based on singular value decomposition nonlinear filtering

2011 ◽  
Author(s):  
Chen Peng ◽  
Zhao Ying ◽  
Dong Enzeng ◽  
Li Xin
Keyword(s):  
Fault Detection ◽  
Singular Value Decomposition ◽  
Nonlinear Filtering ◽  
Detection Algorithm ◽  
Singular Value ◽  
Value Decomposition

scholarly journals Rolling Bearing Incipient Fault Detection Based on a Multi-Resolution Singular Value Decomposition

2019 ◽  
Vol 9 (20) ◽  
pp. 4465 ◽  
Author(s):  
Jiesi Luo ◽  
Shaohui Zhang
Keyword(s):  
Fault Detection ◽  
Singular Value Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Singular Value ◽  
Incipient Fault ◽  
Periodic Impulse ◽  
Value Decomposition ◽  
Impulse Characteristics ◽  
Incipient Fault Detection

The periodic impulse characteristics caused by rolling bearing damage are weak in the incipient failure stage. Thus, these characteristics are always drowned out by background noise and other harmonic interference. A novel approach based on multi-resolution singular value decomposition (MRSVD) is proposed in order to extract the periodic impulse characteristics for incipient fault detection. With the MRSVD method, the vibration signal is first decomposed to obtain a group of approximate signals and detailed signals with different resolutions. The first detail signal is mainly composed of noise and the last approximate signal is mainly composed of harmonic interference. Combined with the kurtosis index, the hidden periodic impulse signal will be extracted from the detail signals (in addition to the first detail signal). Thus, the incipient fault detection of a rolling bearing can be fulfilled according to the envelope demodulation spectrum of the extracted periodic impulse signal. The effectiveness of the proposed method has been demonstrated with both simulation and experimental analyses.

Sign in / Sign up

Export Citation Format

Share Document