Intelligent Bearing Fault Diagnosis Based on Open Set Convolutional Neural Network

نویسندگان

چکیده

Traditional data-driven intelligent fault diagnosis methods have been successfully developed under the closed set assumption (CSA). CSA-based assumes that types in test are consistent with training set, which can achieve high accuracy, but this is generally not valid real-world industrial applications where collection of data often limited. As it unrealistic to assume will cover all types, application classifier may fail when contains unknown because probability input samples belonging cannot be obtained. To solve problem how accurately identified, paper further studies open (OSA) diagnosis. We propose an convolutional neural network (OS-CNN) method and apply our OS-CNN model improved OpenMax as a deep detect types. The overall performance was significantly able effectively tighten boundary known classes limit open-space risk for based on distance modeling. effectiveness proposed verified by experimental four different bearing datasets. Compared state-of-the-art OSA method, only realize correct classification classes, also classes.

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ژورنال

عنوان ژورنال: Mathematics

سال: 2022

ISSN: ['2227-7390']

DOI: https://doi.org/10.3390/math10213953