Intelligent Bearing Fault Diagnosis Based on Open Set Convolutional Neural Network

Bo Zhang, Caicai Zhou, Wei Li (), Shengfei Ji, Hengrui Li, Zhe Tong and See-Kiong Ng
Additional contact information
Bo Zhang: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
Caicai Zhou: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
Wei Li: School of Mechanical Engineering, China University of Mining and Technology, Xuzhou 221116, China
Shengfei Ji: School of Mechanical Engineering, China University of Mining and Technology, Xuzhou 221116, China
Hengrui Li: School of Mechanical Engineering, China University of Mining and Technology, Xuzhou 221116, China
Zhe Tong: School of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China
See-Kiong Ng: Institute of Data Science, National University of Singapore, Singapore 117602, Singapore

Mathematics, 2022, vol. 10, issue 21, 1-22

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

Keywords: open set fault diagnosis; attenuation probability model; extreme value theory; convolution neural network; open space risk (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/21/3953/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/21/3953/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:21:p:3953-:d:951981

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().