VIBRATION SIGNAL ANALYSIS AND FEATURE EXTRACTION BASED ON REASSIGNED WAVELET SCALOGRAM

By Zhike Peng, F. Chu, Yongyong He. 2002

195 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery (2009, methodology): “Among all available time-frequency analysis methods, the wavelet transform maybe the best one [4–6].”
A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing (2005, background): “However, the wavelet transform still has some inevitable deficiencies [7], including the interference terms, border distortion and energy leakage, all of which will generate a lot of small undesired spikes all over the frequency scales and make the results confusing and difficult to be interpreted.”
Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review (2020, methodology): “Many time-frequency strategies like short time Fourier transform (STFT) [21], Wigner-Ville distribution [22], and wavelet transform [12] have been used in machinery fault detection and diagnosis (MFDD).”
Time-frequency feature representation using energy concentration: An overview of recent advances (2009, unknown): “Mechanical signal analysis [188,189,198,204,207, 209,220–222,226,228, 236,255,261] [205,214–216,220,233] [194,212,223]”
General linear chirplet transform (2016, unknown): “TF reassignment method belongs to a post-processing technology, and it can improve the readability of TF representation obviously [6,7].”