A performance enhanced time-varying morphological filtering method for bearing fault diagnosis
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A performance enhanced time-varying morphological filtering method for bearing fault diagnosis
Authors
Keywords
Time-varying morphological filtering, Time-varying structural element, Diagonal slice spectrum, Fault diagnosis, Rolling bearings
Journal
MEASUREMENT
Volume 176, Issue -, Pages 109163
Publisher
Elsevier BV
Online
2021-02-16
DOI
10.1016/j.measurement.2021.109163
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Fault detection for planetary gearbox based on an enhanced average filter and modulation signal bispectrum analysis
- (2020) Junchao Guo et al. ISA TRANSACTIONS
- Blind filters based on envelope spectrum sparsity indicators for bearing and gear vibration-based condition monitoring
- (2020) Cédric Peeters et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Blind deconvolution assisted with periodicity detection techniques and its application to bearing fault feature enhancement
- (2020) Bingyan Chen et al. MEASUREMENT
- A methodology for identifying information rich frequency bands for diagnostics of mechanical components-of-interest under time-varying operating conditions
- (2020) Stephan Schmidt et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A simple and fast guideline for generating enhanced/squared envelope spectra from spectral coherence for bearing fault diagnosis
- (2019) Dong Wang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Research on an enhanced scale morphological-hat product filtering in incipient fault detection of rolling element bearings
- (2019) Xiaoan Yan et al. MEASUREMENT
- A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method
- (2019) Yinsheng Chen et al. Applied Sciences-Basel
- Rotating Machinery Fault Diagnosis Based on Improved Multiscale Amplitude-Aware Permutation Entropy and Multiclass Relevance Vector Machine
- (2019) Chen et al. SENSORS
- Railway bearing and cardan shaft fault diagnosis via an improved morphological filter
- (2019) Yifan Li et al. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
- Adaptive Multipoint Optimal Minimum Entropy Deconvolution Adjusted and Application to Fault Diagnosis of Rolling Element Bearings
- (2019) Yao Cheng et al. IEEE SENSORS JOURNAL
- Transient Feature Extraction by the Improved Orthogonal Matching Pursuit and K-SVD Algorithm With Adaptive Transient Dictionary
- (2019) Yi Qin et al. IEEE Transactions on Industrial Informatics
- An improved singular value decomposition-based method for gear tooth crack detection and severity assessment
- (2019) Yuejian Chen et al. JOURNAL OF SOUND AND VIBRATION
- Optimal demodulation-band selection for envelope-based diagnostics: A comparative study of traditional and novel tools
- (2019) Wade A. Smith et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Sparse time series modeling of the baseline vibration from a gearbox under time-varying speed condition
- (2019) Yuejian Chen et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Fault diagnosis of rolling element bearing using a new optimal scale morphology analysis method
- (2018) Xiaoan Yan et al. ISA TRANSACTIONS
- Blind deconvolution based on cyclostationarity maximization and its application to fault identification
- (2018) Marco Buzzoni et al. JOURNAL OF SOUND AND VIBRATION
- A robust detector for rolling element bearing condition monitoring based on the modulation signal bispectrum and its performance evaluation against the Kurtogram
- (2018) Xiange Tian et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- An enhanced morphology gradient product filter for bearing fault detection
- (2018) Yifan Li et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- The Autogram: An effective approach for selecting the optimal demodulation band in rolling element bearings diagnosis
- (2018) Ali Moshrefzadeh et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Complete ensemble local mean decomposition with adaptive noise and its application to fault diagnosis for rolling bearings
- (2018) Lei Wang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Identification of mechanical compound-fault based on the improved parameter-adaptive variational mode decomposition
- (2018) Yonghao Miao et al. ISA TRANSACTIONS
- Rolling element bearing fault diagnosis under slow speed operation using wavelet de-noising
- (2017) C. Mishra et al. MEASUREMENT
- A new strategy of using a time-varying structure element for mathematical morphological filtering
- (2017) Yifan Li et al. MEASUREMENT
- Self adaptive multi-scale morphology AVG-Hat filter and its application to fault feature extraction for wheel bearing
- (2017) Feiyue Deng et al. MEASUREMENT SCIENCE and TECHNOLOGY
- Fast computation of the spectral correlation
- (2017) Jérôme Antoni et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Diagonal slice spectrum assisted optimal scale morphological filter for rolling element bearing fault diagnosis
- (2017) Yifan Li et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive
- (2017) Zipeng Li et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A Morphological Hilbert-Huang Transform Technique for Bearing Fault Detection
- (2016) Shazali Osman et al. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
- Bearing fault diagnosis based on an improved morphological filter
- (2016) Zhiyong Hu et al. MEASUREMENT
- Detection and recovery of fault impulses via improved harmonic product spectrum and its application in defect size estimation of train bearings
- (2016) Ming Zhao et al. MEASUREMENT
- Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications
- (2016) Yanxue Wang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings
- (2016) Akhand Rai et al. TRIBOLOGY INTERNATIONAL
- Variational Mode Decomposition
- (2014) Konstantin Dragomiretskiy et al. IEEE TRANSACTIONS ON SIGNAL PROCESSING
- Selection principle of mathematical morphological operators in vibration signal processing
- (2014) Aijun Hu et al. JOURNAL OF VIBRATION AND CONTROL
- Application of power spectrum, cepstrum, higher order spectrum and neural network analyses for induction motor fault diagnosis
- (2013) B. Liang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications
- (2013) Jay Lee et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Early Classification of Bearing Faults Using Morphological Operators and Fuzzy Inference
- (2012) A. Santhana Raj et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- An enhanced Kurtogram method for fault diagnosis of rolling element bearings
- (2012) Dong Wang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A review on empirical mode decomposition in fault diagnosis of rotating machinery
- (2012) Yaguo Lei et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis
- (2012) Changqing Shen et al. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
- A weighted multi-scale morphological gradient filter for rolling element bearing fault detection
- (2011) Bing Li et al. ISA TRANSACTIONS
- Gear fault detection using multi-scale morphological filters
- (2011) Bing Li et al. MEASUREMENT
- Application of improved morphological filter to the extraction of impulsive attenuation signals
- (2008) Jing Wang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Multiscale morphology analysis and its application to fault diagnosis
- (2007) Lijun Zhang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now