Rolling element bearing diagnostics in run-to-failure lifetime testing

By T. Williams, X. Ribadeneira, S. A. Billington, T. Kurfess. 2001

264 citations recorded by the source corpus.

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Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, background): “This behavior is due to the ‘‘self-healing” nature of the damage [82].”
Blind deconvolution based on cyclostationarity maximization and its application to fault identification (2018, unknown): “[30], after the defect appearance, the propagation mechanism of bearing faults is composed by consecutive 560 propagation and smoothing effects.”
Bearing performance degradation assessment using locality preserving projections (2011, background): “More advanced prognostics focus on performance degradation assessment, so that failures can be predicted and prevented (Williams et al., 2001).”
Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators (2018, methodology): “Kurtosis and root mean square [28] are two of the most commonly used statistical parameters and are directly used to quantify the collected bearing run-to-failure vibration signals.”
A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing (2005, methodology): “To detect the faults in bearings, hitherto, many kinds of methods have been developed [1–5], and, without exception, their cores are various signal analysis techniques.”
Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods (2007, background): “[4,10].”