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Octave-band Filtering for Convolutional Neural Network-based Diagnostics for Rotating Machinery
2020
Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
The performance of a machine learning model depends on the quality of the features used as input to the model. Research into feature extraction methods for convolutional neural network (CNN)-based diagnostics for rotating machinery remains in a developmental stage. In general, the input to CNN-based diagnostics consists of a spectrogram without significant pre-processing. This paper introduces octave-band filtering as a feature extraction method for preprocessing a spectrogram prior to use with
doi:10.36001/phmconf.2020.v12i1.1132
fatcat:eb3hak67tncvtczjubhsbb6tky