Most cited papers in Engineering Diagnostics and Reliability
What are the most cited papers in Engineering Diagnostics and Reliability?
The most cited paper in Engineering Diagnostics and Reliability is "A review on machinery diagnostics and prognostics implementing condition-based maintenance" (Andrew K. S. Jardine, Daming Lin, Dragan Banjević, 2005), with 4,775 citations. Next come "Machinery health prognostics: A systematic review from data acquisition to RUL prediction" (2,574) and "Artificial intelligence for fault diagnosis of rotating machinery: A review" (2,179).
Showing 50 of 100 papers. Every paper with more than 351 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | 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 |
| 2 | 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 |
| 3 | 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 |
| 4 | 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 |
| 5 | Principal components analysis (PCA) Andrzej Adam Mackiewicz, Waldemar Ratajczak · Computers & Geosciences | Mar 1993 | 1,769 |
| 6 | 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 |
| 7 | 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 |
| 8 | 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 |
| 9 | 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 |
| 10 | A recurrent neural network based health indicator for remaining useful life prediction of bearings Liang Guo, Naipeng Li, Feng Ling Jia et al. · Neurocomputing | 22 Feb 2017 | 1,232 |
| 11 | Rotating machinery prognostics: State of the art, challenges and opportunities Aiwina Heng, Sheng Zhang, Andy C.C. Tan et al. · Mechanical Systems and Signal Processing | 2 Jul 2008 | 1,207 |
| 12 | Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography Zhike Peng, F.L. Chu · Mechanical Systems and Signal Processing | 16 Jul 2003 | 1,196 |
| 13 | Estimating the Voltage Stability of a Power System P. Kessel, H. Glavitsch · IEEE Transactions on Power Delivery | 1986 | 1,116 |
| 14 | A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis Long Wen, Liang Gao, Xinyu Li · IEEE Transactions on Systems Man and Cybernetics Systems | 4 Oct 2017 | 1,100 |
| 15 | A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings Akhand Rai, Sanjay H Upadhyay · Tribology International | 2 Jan 2016 | 799 |
| 16 | Statistical Model of Intermediate Structure James J. Griffin · Physical Review Letters | 29 Aug 1966 | 788 |
| 17 | Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis Xiaojie Guo, Liang Chen, Changqing Shen · Measurement | 18 Jul 2016 | 786 |
| 18 | Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification Chen Lü, Zhenya Wang, Wei-Li Qin et al. · Signal Processing | 30 Jul 2016 | 782 |
| 19 | A transfer convolutional neural network for fault diagnosis based on ResNet-50 Long Wen, Xinyu Li, Liang Gao · Neural Computing and Applications | 26 Feb 2019 | 779 |
| 20 | Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals Jaouher Ben Ali, Nader Fnaiech, Lotfi Saïdi et al. · Applied Acoustics | 20 Sep 2014 | 773 |
| 21 | Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches Nima Khakzad, Faisal Khan, Paul R Amyotte · Reliability Engineering & System Safety | 2 Apr 2011 | 765 |
| 22 | Reliability Evaluation of Engineering Systems. J. U. M. Smith, R. Billington, R. N. Allan · Journal of the Royal Statistical Society Series D (The Statistician) | 1994 | 752 |
| 23 | THE RELATIONSHIP BETWEEN SPECTRAL CORRELATION AND ENVELOPE ANALYSIS IN THE DIAGNOSTICS OF BEARING FAULTS AND OTHER CYCLOSTATIONARY MACHINE SIGNALS Robert Bond Randall, Jérôme Antoni, S. Chobsaard · Mechanical Systems and Signal Processing | Sep 2001 | 750 |
| 24 | Bearing fault diagnosis base on multi-scale CNN and LSTM model Xiaohan Chen, Beike Zhang, Dong Qiang Gao · Journal of Intelligent Manufacturing | 5 Jun 2020 | 737 |
| 25 | Current status of machine prognostics in condition-based maintenance: a review Ying Hong Peng, Ming Dong, Ming Jian Zuo · The International Journal of Advanced Manufacturing Technology | 5 Jan 2010 | 735 |
| 26 | A review on data-driven fault severity assessment in rolling bearings Mariela Cerrada, René–Vinicio Sánchez, Chuan Li et al. · Mechanical Systems and Signal Processing | 20 Jun 2017 | 730 |
| 27 | A review of the application of deep learning in intelligent fault diagnosis of rotating machinery Zhiqin Zhu, Yangbo Lei, Guanqiu Qi et al. · Measurement | 12 Dec 2022 | 722 |
| 28 | Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study Zhibin Zhao, Tianfu Li, Jingyao Wu et al. · ISA Transactions | 19 Aug 2020 | 687 |
| 29 | An innovations approach to fault detection and diagnosis in dynamic systems R.К. Mehra, J. Peschon · Automatica | Sep 1971 | 645 |
| 30 | Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review Purushottam Gangsar, Rajiv Tiwari · Mechanical Systems and Signal Processing | 24 Apr 2020 | 607 |
| 31 | Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques Humberto Henao, Gérard‐André Capolino, Manes Fernandez-Cabanas et al. · IEEE Industrial Electronics Magazine | Jun 2014 | 602 |
| 32 | A Review of Stator Fault Monitoring Techniques of Induction Motors Aftab Siddique, G.S. Yadava, Bhim Singh · IEEE Transactions on Energy Conversion | 22 Feb 2005 | 594 |
| 33 | Application of the EEMD method to rotor fault diagnosis of rotating machinery Yaguo Lei, Zhengjia He, Yanyang Zi · Mechanical Systems and Signal Processing | 26 Nov 2008 | 592 |
| 34 | The prediction theory of multivariate stochastic processes: I. The regularity condition Norbert Wiener, P. R. Masani · Acta Mathematica | 1957 | 588 |
| 35 | Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert–Huang transform Vertika Rai, Amiya Ranjan Mohanty · Mechanical Systems and Signal Processing | 31 Dec 2006 | 578 |
| 36 | Fault diagnosis of machines via parameter estimation and knowledge processing—Tutorial paper Rolf Isermann · Automatica | Jul 1993 | 576 |
| 37 | A Definition of Stable Inelastic Material Daniel C. Drucker · Journal of Applied Mechanics | Mar 1959 | 574 |
| 38 | A comprehensive review on convolutional neural network in machine fault diagnosis Jinyang Jiao, Ming Jie Zhao, Jing Lin et al. · Neurocomputing | 31 Jul 2020 | 573 |
| 39 | A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery Xin Zhang, Qiang Miao, Heng Zhang et al. · Mechanical Systems and Signal Processing | 22 Feb 2018 | 561 |
| 40 | Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system Yanxue Wang, Richard Markert, Jiawei Xiang et al. · Mechanical Systems and Signal Processing | 7 Mar 2015 | 558 |
| 41 | Residual Life Predictions From Vibration-Based Degradation Signals: A Neural Network Approach Nagi Gebraeel, Mark Lawley, Runzi Liu et al. · IEEE Transactions on Industrial Electronics | Jun 2004 | 556 |
| 42 | Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings Meng Gan, Cong Wang, Changan Zhu · Mechanical Systems and Signal Processing | 30 Nov 2015 | 554 |
| 43 | A novel time–frequency Transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings Yifei Ding, Minping Jia, Qiuhua Miao et al. · Mechanical Systems and Signal Processing | 3 Dec 2021 | 546 |
| 44 | Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings Dejie Yu, Junsheng Cheng, Yu Yang · Mechanical Systems and Signal Processing | 15 Oct 2003 | 532 |
| 45 | Review of condition monitoring of rotating electrical machines P.J. Tavner · IET Electric Power Applications | 25 Jun 2008 | 530 |
| 46 | Understanding and Learning Discriminant Features based on Multiattention 1DCNN for Wheelset Bearing Fault Diagnosis Huan Wang, Zhiliang Liu, Dandan Peng et al. · IEEE Transactions on Industrial Informatics | 25 Nov 2019 | 523 |
| 47 | Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review Jinglong Chen, Zipeng Li, Jun Hui Pan et al. · Mechanical Systems and Signal Processing | 11 Sep 2015 | 520 |
| 48 | Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture André Listou Ellefsen, Emil Bjørlykhaug, Vilmar Æsøy et al. · Reliability Engineering & System Safety | 26 Nov 2018 | 519 |
| 49 | A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines Feng Ling Jia, Yaguo Lei, Liang Guo et al. · Neurocomputing | 21 Jul 2017 | 518 |
| 50 | Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction Meng Ma, Zhu Mao · IEEE Transactions on Industrial Informatics | 7 May 2020 | 510 |
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.