Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

Abstract Supervised fault diagnosis typically assumes that all the types of machinery failures are known. However, in practice unknown types of defect, i.e., novelties, may occur, whose detection is a challenging task. In this paper, a novel fault diagnostic method is developed for both diagnostics...

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Bibliographic Details
Main Authors: Zhe Yang, Dejan Gjorgjevikj, Jianyu Long, Yanyang Zi, Shaohui Zhang, Chuan Li
Format: Article
Language:English
Published: SpringerOpen 2021-06-01
Series:Chinese Journal of Mechanical Engineering
Subjects:
Deep learning
Fault diagnostics
Novelty detection
Multi-head deep neural network
Sparse autoencoder
Online Access:https://doi.org/10.1186/s10033-021-00569-0

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