scholarly journals Mechanical defect identification for gas‐insulated switchgear equipment based on time‐frequency vibration signal analysis

High Voltage ◽  
2020 ◽  
Author(s):  
Yao Zhong ◽  
Jian Hao ◽  
Ruijin Liao ◽  
Xupeng Wang ◽  
Xiping Jiang ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Vibration Signal ◽  
Defect Identification ◽  
Time Frequency ◽  
Gas Insulated Switchgear ◽  
Vibration Signal Analysis ◽  
Mechanical Defect

Marine Diesel Engine Fault Diagnosis by Using an Improved Hilbert Spectrum

2006 ◽  
Vol 50 (04) ◽  
pp. 378-387
Author(s):  
Hongkun Li ◽  
Peilin Zhou ◽  
Xiaojiang Ma
Keyword(s):  
Fault Diagnosis ◽  
Signal Analysis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Injection System ◽  
Distribution Method ◽  
Hilbert Spectrum ◽  
Time Frequency ◽  
Marine Diesel ◽  
Vibration Signal Analysis

Vibration signal analysis is a useful method for recognizing the pattern of a machine's working condition. However, it is difficult to recognize nonstationary and nonlinear vibration signal patterns satisfactorily with the traditional Fourier spectrum method. This paper introduces a novel time-frequency distribution method: an improved Hilbert spectrum (HS) for nonstationary vibration signal analysis, that is, applying a wavelet packet de-noise method as a preprocess of the HS. The HS is developed according to instantaneous frequency analysis by using a new kind of signal analysis method called empirical mode decomposition (EMD), which is highly accurate in analyzing various nonstationary and nonlinear signals. Due to a self-adaptive decomposition process, noise has a great effect on the accuracy of the EMD process and the corresponding HS. This has limited the application of the HS on real vibration analysis. In this study, a wavelet packet de-noising technique is employed as a preprocessing to improve the signal-to-noise ratio and the accuracy of the HS. Experimental data of a marine diesel fuel injection system are used to evaluate the improved methodology for system pattern recognition and fault diagnosis.

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