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Journal of Intelligent Manufacturing

Erschienen in:

16.07.2020

A novel normalized recurrent neural network for fault diagnosis with noisy labels

verfasst von: Xiaoyin Nie, Gang Xie

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 5/2021

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Abstract

The early fault diagnosis is a kind of important technology to ensure the normal and reliable operation of wind turbines. However, due to the potential presence of noisy labels in health condition dataset and the weakly explanation of the deep neural network decisions, the performance of fault diagnosis is severely limited. In this paper, a framework called normalized recurrent neural network (NRNN) is proposed for noisy label fault diagnosis, in which the normalized long short-term memory is used to improve the training process and the forward crossentropy loss is introduced to handle the negative effect of noisy labels. The effectiveness and superiority of the proposed framework are verified by four datasets with different noisy label proportions. Meanwhile, the layer-wise relevance propagation algorithm is applied to explore the decision of framework and by visualizing the relevances of input samples to framework decisions, the NRNN does not treat samples equally and prefers signal peaks for classification decisions.

Vorheriger Artikel An active safety control method of collision avoidance for intelligent connected vehicle based on driving risk perception

Nächster Artikel Online monitoring and control of a cyber-physical manufacturing process under uncertainty

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Titel: A novel normalized recurrent neural network for fault diagnosis with noisy labels
verfasst von: Xiaoyin Nie
Gang Xie
Publikationsdatum: 16.07.2020
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 5/2021
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-020-01608-8

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