| Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme W Yu, IIY Kim, C Mechefske Mechanical Systems and Signal Processing 129, 764-780, 2019 | 300 | 2019 |
| An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme W Yu, IIY Kim, C Mechefske Reliability Engineering & System Safety 199, 106926, 2020 | 211 | 2020 |
| Using fuzzy linguistics to select optimum maintenance and condition monitoring strategies CK Mechefske, Z Wang Mechanical Systems and Signal Processing 15 (6), 1129-1140, 2001 | 196 | 2001 |
| Detection of induction motor faults: a comparison of stator current, vibration and acoustic methods W Li, CK Mechefske Journal of vibration and Control 12 (2), 165-188, 2006 | 172 | 2006 |
| Experimental investigation of reflection in guided wave-based inspection for the characterization of pipeline defects X Wang, WT Peter, CK Mechefske, M Hua NDT & e International 43 (4), 365-374, 2010 | 134 | 2010 |
| Objective machinery fault diagnosis using fuzzy logic CK Mechefske Mechanical systems and signal processing 12 (6), 855-862, 1998 | 123 | 1998 |
| A study of vibration and vibration control of ship structures TR Lin, J Pan, PJ O'Shea, CK Mechefske Marine Structures 22 (4), 730-743, 2009 | 121 | 2009 |
| Fault detection using transient machine signals M Timusk, M Lipsett, CK Mechefske Mechanical Systems and Signal Processing 22 (7), 1724-1749, 2008 | 117 | 2008 |
| The effects of spur gear tooth spatial crack propagation on gear mesh stiffness W Yu, Y Shao, CK Mechefske Engineering Failure Analysis 54, 103-119, 2015 | 112 | 2015 |
| Dynamic characteristics of helical gears under sliding friction with spalling defect H Jiang, Y Shao, CK Mechefske Engineering failure analysis 39, 92-107, 2014 | 112 | 2014 |
| Fault detection and diagnosis in low speed rolling element bearings Part I: The use of parametric spectra CK Mechefske, J Mathew Mechanical systems and signal processing 6 (4), 297-307, 1992 | 112 | 1992 |
| Analysis of different RNN autoencoder variants for time series classification and machine prognostics W Yu, IY Kim, C Mechefske Mechanical Systems and Signal Processing 149, 107322, 2021 | 105 | 2021 |
| Hybrid data-driven physics-based model fusion framework for tool wear prediction H Hanachi, W Yu, IY Kim, J Liu, CK Mechefske The International Journal of Advanced Manufacturing Technology 101, 2861-2872, 2019 | 100 | 2019 |
| Optimal damping layout in a shell structure using topology optimization SY Kim, CK Mechefske, IY Kim Journal of Sound and Vibration 332 (12), 2873-2883, 2013 | 95 | 2013 |
| Drive axle housing failure analysis of a mining dump truck based on the load spectrum Y Shao, J Liu, CK Mechefske Engineering Failure Analysis 18 (3), 1049-1057, 2011 | 87 | 2011 |
| Machine condition monitoring and fault diagnostics CK Mechefske Vibration and shock handbook 25, 1-35, 2005 | 87 | 2005 |
| Gradient‐induced acoustic and magnetic field fluctuations in a 4T whole‐body MR imager Y Wu, BA Chronik, C Bowen, CK Mechefske, BK Rutt Magnetic resonance in medicine 44 (4), 532-536, 2000 | 85 | 2000 |
| An analytical model to investigate skidding in rolling element bearings during acceleration W Tu, Y Shao, CK Mechefske Journal of mechanical Science and Technology 26, 2451-2458, 2012 | 83 | 2012 |
| Adaptive variational mode decomposition and its application to multi-fault detection using mechanical vibration signals X He, X Zhou, W Yu, Y Hou, CK Mechefske ISA transactions 111, 360-375, 2021 | 81 | 2021 |
| Analytical modeling of spur gear corner contact effects W Yu, CK Mechefske Mechanism and Machine Theory 96, 146-164, 2016 | 78 | 2016 |
