Bearing damage detection via wavelet packet decomposition of the stator current

By Levent Tolga Eren, Michael J. Devaney. IEEE Transactions on Instrumentation and Measurement, 2004

297 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1D Convolutional Neural Networks and Applications: A Survey (2019, methodology): “Figure 17: For the motor fault detection, the average execution times (in msec) of: (1) the 1D CNN method in [55], (2-7) 6 competing algorithms from [75–78].”
Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions (2017, methodology): “Lei et al. [28] proposed a mechanical fault diagnosis model based on WPD and EMD.”
1-D Convolutional Neural Networks for Signal Processing Applications (2019, methodology): “Kiranyaz 28 For the motor fault detection, the average execution times (in msec) of: Leftmost bar plot 1: the 1D CNN method, bar plots and competing algorithms [1-4]”
Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks (2016, methodology): “For comparison with major competing signal processing techniques for current-based bearing fault detection, we implemented wavelet packet decomposition [12], [13] and FFT [8], [18] based feature extraction techniques with three commonly used classifiers from the literature: MLP [18], radial basis fu”