Wang et al., 2020 - Google Patents
Subband averaging kurtogram with dual-tree complex wavelet packet transform for rotating machinery fault diagnosisWang et al., 2020
- Document ID
- 12785240573430435609
- Author
- Wang L
- Liu Z
- Cao H
- Zhang X
- Publication year
- Publication venue
- Mechanical Systems and Signal Processing
External Links
Snippet
This paper presents a method called subband averaging kurtogram (SAK), incorporating with dual-tree complex wavelet packet transform (DTCWPT), to improve performance of the fast kurtogram (FK) for rotating machinery fault diagnosis. The proposed method first …
- 238000003745 diagnosis 0 title abstract description 42
Classifications
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/038—Speech enhancement, e.g. noise reduction or echo cancellation using band spreading techniques
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L19/00—Speech or audio signal analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signal, using source filter models or psychoacoustic analysis
- G10L19/02—Speech or audio signal analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signal, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders
- G10L19/0204—Speech or audio signal analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signal, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders using subband decomposition
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Wang et al. | Subband averaging kurtogram with dual-tree complex wavelet packet transform for rotating machinery fault diagnosis | |
Wang et al. | Complete ensemble local mean decomposition with adaptive noise and its application to fault diagnosis for rolling bearings | |
Cai et al. | Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox | |
Chen et al. | High-accuracy fault feature extraction for rolling bearings under time-varying speed conditions using an iterative envelope-tracking filter | |
Chen et al. | Detecting of transient vibration signatures using an improved fast spatial–spectral ensemble kurtosis kurtogram and its applications to mechanical signature analysis of short duration data from rotating machinery | |
Meng et al. | A hybrid fault diagnosis method using morphological filter–translation invariant wavelet and improved ensemble empirical mode decomposition | |
Yuan et al. | Gear fault detection using customized multiwavelet lifting schemes | |
CN111141520A (en) | Rolling bearing fault diagnosis method based on improved experience wavelet transform | |
CN108152037A (en) | Method for Bearing Fault Diagnosis based on ITD and improvement shape filtering | |
MXPA05002628A (en) | Method for reduction of aliasing introduced by spectral envelope adjustment in real-valued filterbanks. | |
Yang et al. | Trend extraction based on separations of consecutive empirical mode decomposition components in Hilbert marginal spectrum | |
Zhang et al. | Improved local cepstrum and its applications for gearbox and rolling bearing fault detection | |
CN105928702A (en) | Variable working condition gear case bearing fault diagnosis method based on form component analysis | |
CN110426191B (en) | Fault diagnosis method for anti-interference rotating machine | |
CN112180315A (en) | Method, device and system for extracting fault feature of optical fiber current transformer | |
Sharma et al. | Sensitive sub-band selection criteria for empirical wavelet transform to detect bearing fault based on vibration signals | |
CN111769810A (en) | A method for extracting frequency of fluid mechanical modulation based on energy kurtosis spectrum | |
Naima et al. | An improved fast kurtogram based on an optimal wavelet coefficient for wind turbine gear fault detection | |
Zhang et al. | Variable spectral segmentation empirical wavelet transform for noisy signal processing | |
Chen et al. | IESMGCFFOgram: a new method for multicomponent vibration signal demodulation and rolling bearing fault diagnosis | |
CN110987431B (en) | Bearing state monitoring and fault diagnosis method based on TQWT (TQWT-assisted SPC) | |
Wang et al. | Ensefgram: An optimal demodulation band selection method for the early fault diagnosis of high-speed train bearings | |
Liu et al. | Abnormal detection gram (Andgram): An informative frequency band selection method using composite index for bearing incipient fault diagnosis | |
CN109525215A (en) | It is a kind of to compose the experience small wave converting method for determining sub-band boundary using kurtosis | |
CN107941511B (en) | A method for realizing frequency-kurtosis diagram based on signal time-frequency decomposition |