Feature Extraction and Selection for Automatic Fault Diagnosis of Rotating Machinery
نویسندگان
چکیده
In this work we present three feature extraction models used in vibratory data from rotating machinery for bearing fault diagnosis. Vibrations signals are acquired by accelerometers which are then submitted to different feature extraction modules. Our tests suggest that pooling heterogeneous feature sets achieve better results than using a single extraction model. Besides, different classifiers are used for performance optimization, K-Nearest-Neighbor and Support Vector Machine.
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