Nothing Special   »   [go: up one dir, main page]

Fong et al., 2019 - Google Patents

Mean shift clustering-based analysis of nonstationary vibration signals for machinery diagnostics

Fong et al., 2019

View PDF
Document ID
7445791429191510298
Author
Fong S
Harmouche J
Narasimhan S
Antoni J
Publication year
Publication venue
IEEE Transactions on Instrumentation and Measurement

External Links

Snippet

Vibration analysis is a powerful tool for condition monitoring of rotating machinery. In the nonstationary case, this analysis often involves denoising and extraction of the time-varying harmonic components buried within the vibration signal. However, the complexity of many …
Continue reading at hal.science (PDF) (other versions)

Similar Documents

Publication Publication Date Title
Fong et al. Mean shift clustering-based analysis of nonstationary vibration signals for machinery diagnostics
Li et al. Application of EEMD and improved frequency band entropy in bearing fault feature extraction
Liu et al. Fault diagnosis of industrial wind turbine blade bearing using acoustic emission analysis
Yongbo et al. Review of local mean decomposition and its application in fault diagnosis of rotating machinery
Li et al. Research on test bench bearing fault diagnosis of improved EEMD based on improved adaptive resonance technology
Qu et al. A new acoustic emission sensor based gear fault detection approach
Yu et al. Sparse coding shrinkage in intrinsic time-scale decomposition for weak fault feature extraction of bearings
Dou et al. A rule-based intelligent method for fault diagnosis of rotating machinery
CN104655423A (en) Rolling bearing fault diagnosis method based on time-frequency domain multidimensional vibration feature fusion
CN105651504A (en) Rotary machinery fault feature extraction method based on self-adaptive wavelet energy
Li New approach for bearing fault diagnosis based on fractional spatio-temporal sparse low rank matrix under multichannel time-varying speed condition
CN107563403B (en) Working condition identification method for high-speed train operation
Puchalski et al. Stable distributions and fractal diagnostic models of vibration signals of rotating systems
Mo et al. Conditional empirical wavelet transform with modified ratio of cyclic content for bearing fault diagnosis
Su et al. Fault diagnosis of rotating machinery based on wavelet domain denoising and metric distance
Zhao et al. Underdetermined blind source extraction of early vehicle bearing faults based on EMD and kernelized correlation maximization
Sun et al. A novel rolling bearing vibration impulsive signals detection approach based on dictionary learning
Zhao et al. Blind source extraction based on EMD and temporal correlation for rolling element bearing fault diagnosis
CN117571316A (en) Composite fault diagnosis method and system
Wei et al. Fault diagnosis of bearings in multiple working conditions based on adaptive time-varying parameters short-time Fourier synchronous squeeze transform
Lv et al. Longitudinal synchroextracting transform: A useful tool for characterizing signals with strong frequency modulation and application to machine fault diagnosis
Xin et al. A new fault feature extraction method for non-stationary signal based on advanced synchrosqueezing transform
Jahagirdar et al. Cumulative distribution sharpness profiling based bearing fault diagnosis framework under variable speed conditions
CN104089778A (en) Water turbine vibration fault diagnosis method
Liu et al. Integrated method of generalized demodulation and artificial neural network for robust bearing fault recognition