Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition

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Remaining Useful Life Estimation of Critical Components With Application to Bearings (2012, background): “Interested readers can find more details in [12], [23]–[25].”
Wavelets for fault diagnosis of rotary machines: A review with applications (2014, methodology): “extracted a frequency spectrum growth index (FSGI) from optimal detail coefficients of the DWT and used it to characterize machine health condition quantitatively [89].”
Stochastic modelling and analysis of degradation for highly reliable products (2015, methodology): “[94] analysed gearbox vibration signal and used discrete wavelet transform to retain a frequency band covering gear meshing frequency and modulating frequencies around the gear meshing frequency.”
Gearbox Fault Identification and Classification with Convolutional Neural Networks (2015, background): “The gearbox conditions can be reflected by such measurements as vibratory, acoustic, thermal, electrical, and oil-based signals [7–12].”
Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators (2018, methodology): “[74] used discrete wavelet transform to quickly evaluate gear condition degradation over time and found that the energy of the signal filtered by discrete wavelet transform is insensitive to different wavelet decomposition levels and wavelet mother functions.”