Statistical batch-based bearing fault detection
Journal of Mathematics in Industry
; Heidelberg Vol. 15, Iss. 1, (Dec 2025): 4.
DOI:10.1186/s13362-025-00169-w
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- 3.
Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review
Neupane, D; Seok, J. IEEE Access Vol. 8, .- Times cited 85 on ProQuest
- 4.
An intelligent fault diagnosis method of rolling bearings based on short-time Fourier transform and convolutional neural network
Q Zhang; L Deng. J Fail Anal Prev Vol. 23, Iss. 2, (2023): 795-811.- Times cited 3 on ProQuest
- 6.
The bearing faults detection methods for electrical machines–the state of the art
MA Khan; B Asad; K Kudelina; T Vaimann; A Kallaste. Energies Vol. 16, Iss. 1, (2022).- Times cited 3 on ProQuest
- 7.
Self-supervised signal representation learning for machinery fault diagnosis under limited annotation data
H Wang; Z Liu; Y Ge; D Peng. Knowl-Based Syst Vol. 239, (2022).- Times cited 3 on ProQuest
- 8.
A literature review on one-class classification and its potential applications in big data
N Seliya; A Abdollah Zadeh; TM Khoshgoftaar. J Big Data Vol. 8, (2021): 1-31.- Times cited 3 on ProQuest
- 9.
Deep learning algorithms for bearing fault diagnostics-a comprehensive review
Zhang, S; Zhang, S; Wang, B; Habetler, T G. IEEE Access Vol. 8, (2020): 29857-29881. 2169-3536.- Times cited 56 on ProQuest
- 10.
Rolling bearing fault diagnosis based on convolutional neural network and support vector machine
L Yuan; D Lian; X Kang; Y Chen; K Zhai. IEEE Access Vol. 8, (2020): 137395-137406.- Times cited 13 on ProQuest
- 11.
Remaining useful life prediction and fault diagnosis of rolling bearings based on short-time Fourier transform and convolutional neural network
S Zhou; M Xiao; P Bartos. Shock Vib Vol. 2020, (2020): 1-14.- Times cited 3 on ProQuest
- 12.
Fault detection for rolling-element bearings using multivariate statistical process control methods
X Jin; J Fan; TW Chow. IEEE Trans Instrum Meas Vol. 68, Iss. 9, (2018): 3128-3136.- Times cited 2 on ProQuest
- 13.
A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load
Zhang, W; Li, C; Peng, G; Chen, Y; Zhang, Z. Mech Syst Signal Process Vol. 100, (2018): 439-453.- Times cited 11 on ProQuest
- 14.
Automatic feature extraction of time-series applied to fault severity assessment of helical gearbox in stationary and non-stationary speed operation
D Cabrera; F Sancho; C Li. Appl Soft Comput J Vol. 58, (2017): 53-64.- Times cited 2 on ProQuest
- 15.
Bearing vibration detection and analysis using enhanced fast fourier transform algorithm
H-C Lin; Y-C Ye; B-J Huang; J-L Su. Adv Mech Eng Vol. 8, Iss. 10, (2016).- Times cited 2 on ProQuest
- 16.
Fault detection in rotor bearing systems using time frequency techniques
NH Chandra; AS Sekhar. Mech Syst Signal Process Vol. 72, (2016): 105-133.- Times cited 3 on ProQuest
- 17.
Optimised Spectral Kurtosis for bearing diagnostics under electromagnetic interference
WA Smith; Z Fan; Z Peng; H Li; RB Randall. Mech Syst Signal Process Vol. 75, (2016): 371-394.- Times cited 4 on ProQuest
- 19.
Rolling element bearing diagnostics using the case western reserve university data: a benchmark study
WA Smith; RB Randall. Mech Syst Signal Process Vol. 64, Iss. 65, (2015): 100-131.- Times cited 5 on ProQuest
- 20.
Quality-relevant and process-relevant fault monitoring with concurrent projection to latent structures
SJ Qin; Y Zheng. AICHE J Vol. 59, Iss. 2, (2013): 496-504.- Times cited 6 on ProQuest