Vibration signal analysis using parameterized time–frequency method for features extraction of varying-speed rotary machinery

2015 ◽  
Vol 335 ◽  
pp. 350-366 ◽  
Author(s):  
Y. Yang ◽  
X.J. Dong ◽  
Z.K. Peng ◽  
W.M. Zhang ◽  
G. Meng
Keyword(s):  
Signal Analysis ◽  
Vibration Signal ◽  
Features Extraction ◽  
Frequency Method ◽  
Time Frequency ◽  
Rotary Machinery ◽  
Vibration Signal Analysis

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.

scholarly journals Research on transformer condition evaluation technology based on vibration signal analysis

2021 ◽  
Vol 645 ◽  
pp. 012036
Author(s):  
Ma Hao ◽  
Yao Chuang ◽  
Duan Minghui ◽  
Wei Jufang ◽  
Zhang Xin ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Vibration Signal ◽  
Condition Evaluation ◽  
Vibration Signal Analysis
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