Octave-band Filtering for Convolutional Neural Network-based Diagnostics for Rotating Machinery
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 CNN. This method is an adaptation of a feature extraction method originally developed for speech recognition. The method developed for diagnosis of machinery faults differs from filtering methods applied to speech recognition in its use of octave bands, to which weighting has been applied that is optimal for machinery diagnosis. Through a case study, the effectiveness of octave-band filtering is demonstrated. The method not only improves the accuracy of the CNN-based diagnostics but also reduces the size of the CNN.
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Octave-band filtering, Rotating machinery, Convolutional neural network, Feature extraction
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