A Critical Overview of the “Filterbank-Feature-Decision” Methodology in Machine Condition Monitoring
出版年份 2021 全文链接
标题
A Critical Overview of the “Filterbank-Feature-Decision” Methodology in Machine Condition Monitoring
作者
关键词
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出版物
Acoustics Australia
Volume -, Issue -, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2021-04-29
DOI
10.1007/s40857-021-00232-7
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- New fault diagnosis approaches for detecting the bearing slight degradation
- (2020) Saeed Nezamivand Chegini et al. MECCANICA
- 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
- A Koopman operator approach for machinery health monitoring and prediction with noisy and low-dimensional industrial time series
- (2020) Cheng Cheng et al. NEUROCOMPUTING
- The sum of weighted normalized square envelope: A unified framework for kurtosis, negative entropy, Gini index and smoothness index for machine health monitoring
- (2020) Dong Wang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A comprehensive review on convolutional neural network in machine fault diagnosis
- (2020) Jinyang Jiao et al. NEUROCOMPUTING
- A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
- (2019) Wei et al. Entropy
- Physics-Based Convolutional Neural Network for Fault Diagnosis of Rolling Element Bearings
- (2019) Mohammadkazem Sadoughi et al. IEEE SENSORS JOURNAL
- A Step Toward Fault Type and Severity Characterization in Spur Gears
- (2019) I. Dadon et al. JOURNAL OF MECHANICAL DESIGN
- Self-running bearing diagnosis based on scalar indicator using fast order frequency spectral coherence
- (2019) Souhayb Kass et al. MEASUREMENT
- The spectral amplitude modulation: A nonlinear filtering process for diagnosis of rolling element bearings
- (2019) Ali Moshrefzadeh et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- 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
- Blind deconvolution based on cyclostationarity maximization and its application to fault identification
- (2018) Marco Buzzoni et al. JOURNAL OF SOUND AND VIBRATION
- Spectral L2 / L1 norm: A new perspective for spectral kurtosis for characterizing non-stationary signals
- (2018) Dong Wang MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Artificial intelligence for fault diagnosis of rotating machinery: A review
- (2018) Ruonan Liu et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Dynamic modeling of gearbox faults: A review
- (2018) Xihui Liang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings
- (2018) Dong Wang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Improved Dynamic Mode Decomposition and Its Application to Fault Diagnosis of Rolling Bearing
- (2018) Zhang Dang et al. SENSORS
- Adaptive online dictionary learning for bearing fault diagnosis
- (2018) Yanfei Lu et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- A statistical methodology for the design of condition indicators
- (2018) Jérôme Antoni et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Fault Diagnosis for a Wind Turbine Generator Bearing via Sparse Representation and Shift-Invariant K-SVD
- (2017) Boyuan Yang et al. IEEE Transactions on Industrial Informatics
- CS2 analysis in presence of non-Gaussian background noise – Effect on traditional estimators and resilience of log-envelope indicators
- (2017) P. Borghesani et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Fast computation of the spectral correlation
- (2017) Jérôme Antoni et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Adaptive Mode Decomposition Methods and Their Applications in Signal Analysis for Machinery Fault Diagnosis: A Review With Examples
- (2017) Zhipeng Feng et al. IEEE Access
- The spectral analysis of cyclo-non-stationary signals
- (2016) D. Abboud et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data
- (2016) Feng Jia et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- 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
- Understanding deep convolutional networks
- (2016) Stéphane Mallat PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram
- (2016) Xianglong Chen et al. SENSORS
- On the use of cyclostationary indicators in IC engine quality control by cold tests
- (2015) S. Delvecchio et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study
- (2015) Wade A. Smith et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A Review of Planetary and Epicyclic Gear Dynamics and Vibrations Research
- (2014) Christopher G. Cooley et al. Applied Mechanics Reviews
- Variational Mode Decomposition
- (2014) Konstantin Dragomiretskiy et al. IEEE TRANSACTIONS ON SIGNAL PROCESSING
- Early damage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine
- (2014) A. Tabrizi et al. MECCANICA
- Selection of informative frequency band in local damage detection in rotating machinery
- (2014) Jakub Obuchowski et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- The design of a new sparsogram for fast bearing fault diagnosis: Part 1 of the two related manuscripts that have a joint title as “Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement – Parts 1 and 2”
- (2013) Peter W. Tse et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- The relationship between kurtosis- and envelope-based indexes for the diagnostic of rolling element bearings
- (2013) P. Borghesani et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Wavelets for fault diagnosis of rotary machines: A review with applications
- (2013) Ruqiang Yan et al. SIGNAL PROCESSING
- Modeling of probability distribution functions for automatic threshold calculation in condition monitoring systems
- (2012) A. Jablonski et al. MEASUREMENT
- Detection of signal component modulations using modulation intensity distribution
- (2012) Jacek Urbanek et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- 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
- Hilbert transform in vibration analysis
- (2011) Michael Feldman MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- The synchronous (time domain) average revisited
- (2011) S. Braun MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Application of an improved kurtogram method for fault diagnosis of rolling element bearings
- (2011) Yaguo Lei et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Natural computing for mechanical systems research: A tutorial overview
- (2010) Keith Worden et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Rolling element bearing diagnostics—A tutorial
- (2010) Robert B. Randall et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram
- (2010) Tomasz Barszcz et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Indicators of cyclostationarity: Theory and application to gear fault monitoring
- (2007) Amani Raad et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
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