Manifold Learning Using Linear Local Tangent Space Alignment (LLTSA) Algorithm for Noise Removal in Wavelet Filtered Vibration Signal
Published 2016 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Manifold Learning Using Linear Local Tangent Space Alignment (LLTSA) Algorithm for Noise Removal in Wavelet Filtered Vibration Signal
Authors
Keywords
Wavelet transform (WT), Manifold learning, Linear local tangent space alignment (LLTSA), Centrifugal Pump and Bearing defect
Journal
JOURNAL OF NONDESTRUCTIVE EVALUATION
Volume 35, Issue 3, Pages -
Publisher
Springer Nature
Online
2016-08-18
DOI
10.1007/s10921-016-0366-4
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Distributed bearing fault diagnosis based on vibration analysis
- (2016) Boštjan Dolenc et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Fault diagnosis of rolling bearing based on second generation wavelet denoising and morphological filter
- (2015) Lingjie Meng et al. Journal of Mechanical Science and Technology
- Rolling element bearing fault detection using PPCA and spectral kurtosis
- (2015) Jiawei Xiang et al. MEASUREMENT
- Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform
- (2015) Wangpeng He et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A hybrid fault diagnosis method using morphological filter–translation invariant wavelet and improved ensemble empirical mode decomposition
- (2015) Lingjie Meng et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis
- (2015) Yi Wang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis
- (2014) Hicham Talhaoui et al. ISA TRANSACTIONS
- Rolling bearing fault diagnosis approach using probabilistic principal component analysis denoising and cyclic bispectrum
- (2014) Bingzhen Jiang et al. JOURNAL OF VIBRATION AND CONTROL
- Support vector machine based optimization of multi-fault classification of gears with evolutionary algorithms from time–frequency vibration data
- (2014) D.J. Bordoloi et al. MEASUREMENT
- The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform
- (2014) Renping Shao et al. MEASUREMENT
- Defect detection in deep groove ball bearing in presence of external vibration using envelope analysis and Duffing oscillator
- (2012) V.N. Patel et al. MEASUREMENT
- Outer race defect width measurement in taper roller bearing using discrete wavelet transform of vibration signal
- (2012) Rajesh Kumar et al. MEASUREMENT
- Rolling element bearing fault diagnosis using autocorrelation and continuous wavelet transform
- (2011) PK Kankar et al. JOURNAL OF VIBRATION AND CONTROL
- Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network
- (2011) G.F. Bin et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- EEMD method and WNN for fault diagnosis of locomotive roller bearings
- (2010) Yaguo Lei et al. EXPERT SYSTEMS WITH APPLICATIONS
- Detection and Advancement Monitoring of Distributed Pitting Failure in Gears
- (2010) Hasan Ozturk et al. JOURNAL OF NONDESTRUCTIVE EVALUATION
- Rolling element bearing diagnostics—A tutorial
- (2010) Robert B. Randall et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started