| # | Title | Journal | Year | Citations |
|---|
| 1 | A review on machinery diagnostics and prognostics implementing condition-based maintenance | Mechanical Systems and Signal Processing | 2006 | 3,334 |
| 2 | Rolling element bearing diagnostics—A tutorial | Mechanical Systems and Signal Processing | 2011 | 1,812 |
| 3 | Deep learning and its applications to machine health monitoring | Mechanical Systems and Signal Processing | 2019 | 1,616 |
| 4 | A review on empirical mode decomposition in fault diagnosis of rotating machinery | Mechanical Systems and Signal Processing | 2013 | 1,401 |
| 5 | Artificial intelligence for fault diagnosis of rotating machinery: A review | Mechanical Systems and Signal Processing | 2018 | 1,401 |
| 6 | Machinery health prognostics: A systematic review from data acquisition to RUL prediction | Mechanical Systems and Signal Processing | 2018 | 1,397 |
| 7 | Applications of machine learning to machine fault diagnosis: A review and roadmap | Mechanical Systems and Signal Processing | 2020 | 1,338 |
| 8 | Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study | Mechanical Systems and Signal Processing | 2015 | 1,295 |
| 9 | Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data | Mechanical Systems and Signal Processing | 2016 | 1,280 |
| 10 | Support vector machine in machine condition monitoring and fault diagnosis | Mechanical Systems and Signal Processing | 2007 | 1,148 |
| 11 | Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications | Mechanical Systems and Signal Processing | 2014 | 1,138 |
| 12 | Fast computation of the kurtogram for the detection of transient faults | Mechanical Systems and Signal Processing | 2007 | 1,135 |
| 13 | REFERENCE-BASED STOCHASTIC SUBSPACE IDENTIFICATION FOR OUTPUT-ONLY MODAL ANALYSIS | Mechanical Systems and Signal Processing | 1999 | 1,110 |
| 14 | 1D convolutional neural networks and applications: A survey | Mechanical Systems and Signal Processing | 2021 | 1,005 |
| 15 | The spectral kurtosis: a useful tool for characterising non-stationary signals | Mechanical Systems and Signal Processing | 2006 | 989 |
| 16 | The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines | Mechanical Systems and Signal Processing | 2006 | 978 |
| 17 | Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography | Mechanical Systems and Signal Processing | 2004 | 954 |
| 18 | Past, present and future of nonlinear system identification in structural dynamics | Mechanical Systems and Signal Processing | 2006 | 912 |
| 19 | Rotating machinery prognostics: State of the art, challenges and opportunities | Mechanical Systems and Signal Processing | 2009 | 888 |
| 20 | A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load | Mechanical Systems and Signal Processing | 2018 | 854 |
| 21 | A review on the application of deep learning in system health management | Mechanical Systems and Signal Processing | 2018 | 749 |
| 22 | Prognostic modelling options for remaining useful life estimation by industry | Mechanical Systems and Signal Processing | 2011 | 747 |
| 23 | Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples | Mechanical Systems and Signal Processing | 2013 | 727 |
| 24 | The sensitivity method in finite element model updating: A tutorial | Mechanical Systems and Signal Processing | 2011 | 678 |
| 25 | A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing | Mechanical Systems and Signal Processing | 2005 | 669 |
| 26 | ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES | Mechanical Systems and Signal Processing | 2003 | 591 |
| 27 | Nonlinear normal modes, Part I: A useful framework for the structural dynamicist | Mechanical Systems and Signal Processing | 2009 | 571 |
| 28 | A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications | Mechanical Systems and Signal Processing | 2021 | 569 |
| 29 | An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings | Mechanical Systems and Signal Processing | 2019 | 561 |
| 30 | Bearing fault diagnosis based on wavelet transform and fuzzy inference | Mechanical Systems and Signal Processing | 2004 | 554 |
| 31 | THE RELATIONSHIP BETWEEN SPECTRAL CORRELATION AND ENVELOPE ANALYSIS IN THE DIAGNOSTICS OF BEARING FAULTS AND OTHER CYCLOSTATIONARY MACHINE SIGNALS | Mechanical Systems and Signal Processing | 2001 | 553 |
| 32 | Hilbert transform in vibration analysis | Mechanical Systems and Signal Processing | 2011 | 536 |
| 33 | A novel deep autoencoder feature learning method for rotating machinery fault diagnosis | Mechanical Systems and Signal Processing | 2017 | 500 |
| 34 | Cyclostationarity by examples | Mechanical Systems and Signal Processing | 2009 | 497 |
| 35 | A review on data-driven fault severity assessment in rolling bearings | Mechanical Systems and Signal Processing | 2018 | 493 |
| 36 | A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram | Mechanical Systems and Signal Processing | 2011 | 488 |
| 37 | Development in vibration-based structural damage detection technique | Mechanical Systems and Signal Processing | 2007 | 478 |
| 38 | Application of the EEMD method to rotor fault diagnosis of rotating machinery | Mechanical Systems and Signal Processing | 2009 | 470 |
| 39 | Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection | Mechanical Systems and Signal Processing | 2012 | 467 |
| 40 | The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis | Mechanical Systems and Signal Processing | 2007 | 464 |
| 41 | Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert–Huang transform | Mechanical Systems and Signal Processing | 2007 | 451 |
| 42 | Gear fault detection using artificial neural networks and support vector machines with genetic algorithms | Mechanical Systems and Signal Processing | 2004 | 448 |
| 43 | Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings | Mechanical Systems and Signal Processing | 2016 | 443 |
| 44 | Vibration-based structural health monitoring using output-only measurements under changing environment | Mechanical Systems and Signal Processing | 2008 | 441 |
| 45 | On acoustic emission for failure investigation in CFRP: Pattern recognition and peak frequency analyses | Mechanical Systems and Signal Processing | 2011 | 440 |
| 46 | Vibration based structural health monitoring of an arch bridge: From automated OMA to damage detection | Mechanical Systems and Signal Processing | 2012 | 438 |
| 47 | The infogram: Entropic evidence of the signature of repetitive transients | Mechanical Systems and Signal Processing | 2016 | 438 |
| 48 | Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods | Mechanical Systems and Signal Processing | 2007 | 436 |
| 49 | Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization | Mechanical Systems and Signal Processing | 2018 | 430 |
| 50 | Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings | Mechanical Systems and Signal Processing | 2005 | 427 |
