The resonance-based signal decomposition algorithm presented in this paper utilizes sparse signal representations, morphological component analysis, and constant-Q (wavelet) transforms with adjustable Q-factor.Ivan W. SelesnickPolytechnic Institute of New York UniversitySignal processing...
A new method of health condition detection for hydraulic pump using enhanced whale optimization-resonance-based sparse signal decomposition and modified ... F Zhou,W Liu,X Yang,... - 《Transactions of the Institute of Measurement & Control》 被引量: 0发表: 2021年 Sea clutter suppression algorit...
In this paper, a novel time-frequency signature using resonance-based sparse signal decomposition (RSSD), phase space reconstruction (PSR), time-frequency distribution (TFD) and manifold learning is proposed for feature extraction of ship-radiated noise, which is called resonance-based time-frequency...
To address these issues, a novel compound fault features extraction technique based on parallel dual-parameter optimized resonance-based sparse signal decomposition (RSSD) and improved multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is proposed in this contribution. Firstly, according to...
Wang, Resonance-Based Sparse Signal Decomposition and its Application in Mechanical Fault Diagnosis: A Review, Sensors, 17, 2017, p.1279.Huang, W.; Sun, H.; Wang, W. Resonance-Based Sparse Signal Decomposition and its Application in M...
In the early fault diagnosis of rolling bearing, the vibration signal is mixed with a lot of noise, resulting in the difficulties in analysis of early fault weak signal. This chapter introduces resonance-based signal sparse decomposition (RSSD) into rolling bearing weak fault diagnosis, and ...
In this paper, a novel decomposition method, optimal resonance-based signal spares decomposition, is applied for the detection of those two types of faults in the rotating machinery. This method is based on the resonance-based signal spares decomposition, which can nonlinearly decompose vibration ...
The resonance-based signal decomposition algorithm presented in this paper utilizes sparse signal representations, morphological component analysis, and constant-Q (wavelet) transforms with adjustable Q-factor. Introduction Frequency-based analysis and filtering are fundamental tools in signal processing. ...
The resonance-based signal decomposition algorithm presented in this paper utilizes sparse signal representations, morphological component analysis, and constant-Q (wavelet) transforms with adjustable Q-factor.Ivan W. SelesnickSignal ProcessingI.W. Selesnick, Resonance-based signal decomposition: a new ...
Resonance-based signal decomposition: A new sparsity-enabled signal analysis methodSparse signal representationConstant- Q transformWavelet transformMorphological component analysisNumerous signals arising from physiological and physical processes, in addition to being non-stationary, are moreover a mixture of ...