Convolutional Neural Net and Bearing Fault Analysis
نویسندگان
چکیده
There has been immense success on the application of Convolutional Neural Nets (CNN) to image and acoustic data analysis. In this paper, rather than preprocessing vibration signals to denoise or extract features, we investigate the usage of CNNs on raw signals; in particular, we test the accuracy of CNNs as classifiers on bearing fault data, by varying the configurations of the CNN from one-layer up to a deep three-layer model. We inspect the convolution filters learned by the CNN, and show that the filters detect unique features of every classification category. We also study the effectiveness of the various CNN models when the input signals are corrupted with noise.
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