Most cited papers in Machine Fault Diagnosis Techniques
What are the most cited papers in Machine Fault Diagnosis Techniques?
The most cited paper in Machine Fault Diagnosis Techniques is "Matching pursuits with time-frequency dictionaries" (Stéphane Mallat, Zhifeng Zhang, 1993), with 9,224 citations. Next come "Variational Mode Decomposition" (9,123) and "ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD" (8,819).
Showing 50 of 100 papers. Every paper with more than 750 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Matching pursuits with time-frequency dictionaries Stéphane Mallat, Zhifeng Zhang · IEEE Transactions on Signal Processing | 1993 | 9,224 |
| 2 | Variational Mode Decomposition Konstantin Dragomiretskiy, Dominique Zosso · IEEE Transactions on Signal Processing | 24 Jan 2014 | 9,123 |
| 3 | ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD Zhaohua Wu, Norden Eh Huang · Advances in Adaptive Data Analysis | 17 Oct 2008 | 8,819 |
| 4 | The wavelet transform, time-frequency localization and signal analysis Ingrid C. Daubechies · IEEE Transactions on Information Theory | 1990 | 6,510 |
| 5 | A review on machinery diagnostics and prognostics implementing condition-based maintenance Andrew K. S. Jardine, Daming Lin, Dragan Banjević · Mechanical Systems and Signal Processing | 4 Nov 2005 | 4,775 |
| 6 | Localization of the complex spectrum: the S transform Robert G. Stockwell, Lalu Mansinha, R. P. Lowe · IEEE Transactions on Signal Processing | Apr 1996 | 3,504 |
| 7 | A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches Zhiwei Gao, Carlo Cecati, Steven X. Ding · IEEE Transactions on Industrial Electronics | 27 Mar 2015 | 3,109 |
| 8 | Applications of machine learning to machine fault diagnosis: A review and roadmap Yaguo Lei, Bin Yang, Xinwei Jiang et al. · Mechanical Systems and Signal Processing | 5 Jan 2020 | 2,905 |
| 9 | Rolling element bearing diagnostics—A tutorial Robert Bond Randall, Jérôme Antoni · Mechanical Systems and Signal Processing | 27 Nov 2010 | 2,711 |
| 10 | Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study Wade A. Smith, Robert Bond Randall · Mechanical Systems and Signal Processing | 11 May 2015 | 2,708 |
| 11 | Empirical Mode Decomposition as a Filter Bank Patrick Flandrin, Gabriel Rilling, Paulo Gonçalvès · IEEE Signal Processing Letters | Feb 2004 | 2,601 |
| 12 | Machinery health prognostics: A systematic review from data acquisition to RUL prediction Yaguo Lei, Naipeng Li, Liang Guo et al. · Mechanical Systems and Signal Processing | Dec 2017 | 2,574 |
| 13 | A complete ensemble empirical mode decomposition with adaptive noise María Eugenia Torres, Marcelo A. Colominas, Gastón Schlotthauer et al. | May 2011 | 2,562 |
| 14 | Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool Ingrid C. Daubechies, Jianfeng Lu, Hau‐Tieng Wu · Applied and Computational Harmonic Analysis | 21 Aug 2010 | 2,519 |
| 15 | Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review S. Nandi, Hamid A. Toliyat, Xiaodong Li · IEEE Transactions on Energy Conversion | Dec 2005 | 2,385 |
| 16 | Empirical Wavelet Transform Jérôme Gilles · IEEE Transactions on Signal Processing | 30 May 2013 | 2,222 |
| 17 | A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method Long Wen, Xinyu Li, Liang Gao et al. · IEEE Transactions on Industrial Electronics | 17 Nov 2017 | 2,197 |
| 18 | Artificial intelligence for fault diagnosis of rotating machinery: A review Ruonan Liu, Boyuan Yang, Enrico Zio et al. · Mechanical Systems and Signal Processing | 15 Feb 2018 | 2,179 |
| 19 | Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals B. Boashash · Proceedings of the IEEE | Apr 1992 | 1,964 |
| 20 | A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings Biao Wang, Yaguo Lei, Naipeng Li et al. · IEEE Transactions on Reliability | 14 Dec 2018 | 1,903 |
| 21 | A review on empirical mode decomposition in fault diagnosis of rotating machinery Yaguo Lei, Jing Lin, Zhengjia He et al. · Mechanical Systems and Signal Processing | 18 Oct 2012 | 1,882 |
| 22 | Remaining useful life estimation in prognostics using deep convolution neural networks Xiang Li, Qian Ding, Jian‐Qiao Sun · Reliability Engineering & System Safety | 2 Dec 2017 | 1,758 |
| 23 | A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals Wěi Zhāng, Gaoliang Peng, Chuanhao Li et al. · Sensors | 22 Feb 2017 | 1,747 |
| 24 | A study of the characteristics of white noise using the empirical mode decomposition method Zhaohua Wu, Norden Eh Huang · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences | 5 Apr 2004 | 1,722 |
| 25 | Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data Feng Ling Jia, Yaguo Lei, Jing Lin et al. · Mechanical Systems and Signal Processing | 18 Nov 2015 | 1,717 |
| 26 | Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning Siyu Shao, Stephen McAleer, Ruqiang Yan et al. · IEEE Transactions on Industrial Informatics | 10 Aug 2018 | 1,647 |
| 27 | Support vector machine in machine condition monitoring and fault diagnosis Achmad Widodo, Bo‐Suk Yang · Mechanical Systems and Signal Processing | 16 Jan 2007 | 1,645 |
| 28 | Linear and quadratic time-frequency signal representations Franz Hlawatsch, G.F. Boudreaux-Bartels · IEEE Signal Processing Magazine | Apr 1992 | 1,639 |
| 29 | Fast computation of the kurtogram for the detection of transient faults Jérôme Antoni · Mechanical Systems and Signal Processing | 31 Jan 2006 | 1,606 |
| 30 | A review of induction motors signature analysis as a medium for faults detection Mohamed Benbouzid · IEEE Transactions on Industrial Electronics | 2000 | 1,600 |
| 31 | Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications Jay K. Lee, Fangji Wu, Wenyu Zhao et al. · Mechanical Systems and Signal Processing | 29 Aug 2013 | 1,587 |
| 32 | A systematic literature review of machine learning methods applied to predictive maintenance Thyago Peres Carvalho, Fabrizzio Alphonsus Alves de Melo Nunes Soares, Roberto Oliveira Vita et al. · Computers & Industrial Engineering | 5 Sep 2019 | 1,544 |
| 33 | COMPLEMENTARY ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOVEL NOISE ENHANCED DATA ANALYSIS METHOD Jia‐Rong Yeh, Jiann-Shing Shieh, Norden Eh Huang · Advances in Adaptive Data Analysis | Apr 2010 | 1,521 |
| 34 | Improving the readability of time-frequency and time-scale representations by the reassignment method François Auger, Patrick Flandrin · IEEE Transactions on Signal Processing | May 1995 | 1,501 |
| 35 | Improved complete ensemble EMD: A suitable tool for biomedical signal processing Marcelo A. Colominas, Gastón Schlotthauer, María Eugenia Torres · Biomedical Signal Processing and Control | 16 Jul 2014 | 1,498 |
| 36 | Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics Hai Fei Qiu, Jay K. Lee, Jing Lin et al. · Journal of Sound and Vibration | Jun 2005 | 1,479 |
| 37 | Wavelets for fault diagnosis of rotary machines: A review with applications Ruqiang Yan, Robert X. Gao, Xuefeng Chen · Signal Processing | 22 Apr 2013 | 1,444 |
| 38 | Deep Residual Shrinkage Networks for Fault Diagnosis Minghang Zhao, Shisheng Zhong, Xuyun Fu et al. · IEEE Transactions on Industrial Informatics | 26 Sep 2019 | 1,436 |
| 39 | The spectral kurtosis: a useful tool for characterising non-stationary signals Jérôme Antoni · Mechanical Systems and Signal Processing | 15 Dec 2004 | 1,430 |
| 40 | Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks Türker İnce, Serkan Kıranyaz, Levent Eren et al. · IEEE Transactions on Industrial Electronics | 29 Jun 2016 | 1,417 |
| 41 | A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load Wěi Zhāng, Chuanhao Li, Gaoliang Peng et al. · Mechanical Systems and Signal Processing | 4 Aug 2017 | 1,413 |
| 42 | A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings Naresh Tandon, Achintya Choudhury · Tribology International | Aug 1999 | 1,353 |
| 43 | Sliding mode observers for fault detection and isolation Christopher A. Edwards, Sarah K. Spurgeon, Ron J. Patton · Automatica | Apr 2000 | 1,339 |
| 44 | On empirical mode decomposition and its algorithms Gabriel Rilling, Patrick Flandrin, Paulo Gonçalvès | Jun 2003 | 1,329 |
| 45 | The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines Jérôme Antoni, Robert Bond Randall · Mechanical Systems and Signal Processing | 9 Nov 2004 | 1,325 |
| 46 | Loss of Life Transformer Prediction Based on Stacking Ensemble Improved by Genetic Algorithm By IJISRT Rosena Shintabella, Catur Edi Widodo, Adi Wibowo · International Journal of Innovative Science and Research Technology (IJISRT) | 26 Mar 2024 | 1,320 |
| 47 | Model-Based Compressive Sensing Richard G. Baraniuk, Volkan Cevher, Marco F. Duarte et al. · IEEE Transactions on Information Theory | 24 Mar 2010 | 1,307 |
| 48 | Improved time-frequency representation of multicomponent signals using exponential kernels Heejun Choi, William J. Williams · IEEE Transactions on Acoustics Speech and Signal Processing | Jun 1989 | 1,302 |
| 49 | Condition Monitoring of Bearing Damage in Electromechanical Drive Systems by Using Motor Current Signals of Electric Motors: A Benchmark Data Set for Data-Driven Classification Christian Lessmeier, James Kuria Kimotho, Detmar Zimmer et al. · PHM Society European Conference | 5 Jul 2016 | 1,263 |
| 50 | Convolutional Neural Network Based Fault Detection for Rotating Machinery Olivier Janssens, Viktor Slavkovikj, Bram Vervisch et al. · Journal of Sound and Vibration | 25 May 2016 | 1,262 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.