BEIJING, July 5, 2024
/PRNewswire/ -- Maintaining machinery is a time-consuming,
challenging task that not only causes significant downtime but is
also prone to human errors. However, rather than relying solely on
manual inspections, we are moving towards automated diagnosis where
intelligent models can analyze vast amounts of data from sensors
placed on machines to identify potential problems. This shift is
made possible by advancements in deep transfer learning, reducing
the need for extensive data collection and training to build
diagnosis models for each machine. However, for accurate fault
diagnosis, these models require high-quality labeled data from the
source domain, which is challenging to obtain.
To address this issue, researchers from Xi'an Jiaotong
University, Hunan University of
Science and Technology in China,
and Brunel University London in the United Kingdom have proposed a Label Recovery
and Trajectory Designable Network (LRTDN). The paper was published
in the 2024 Issue 4 of the IEEE/CAA Journal of Automatica
Sinica.
"Incorrect label annotation produces two negative effects:
First, the complex decision boundary of diagnosis models lowers the
generalization performance on the target domain, and secondly, the
distribution of target domain samples becomes misaligned with the
false-labeled samples. To overcome these negative effects, we
propose LRTDN," says corresponding author Yaguo Lei, Professor at Xi'an Jiaotong
University.
The LRTDN addresses the issue of incorrect labeling using three
key components: a residual network with dual classifiers, an
annotation check module, and adaptation trajectories. Each
component tackles specific challenges of deep transfer learning to
enhance fault diagnosis.
The residual network with dual classifiers captures the nuances
of features between the source and target domains. By learning to
distinguish these features, the model can adapt to the new patterns
in the data, making it more accurate in diagnosing faults in the
target domain.
The annotation check module identifies and corrects falsely
labeled samples in the source domain. It uses a label anomaly
factor that separates false-labeled samples from pure-labeled ones
based on opposite gradient directions. Furthermore, the adaptation
trajectories prioritize the fault detection model to learn from
accurately labeled samples.
Using the proposed LRTDN method, researchers successfully
diagnosed faults in bearings, even when the data in the source
domain was incorrectly labeled. The LRTDN outperformed other
methods, achieving notably higher accuracy rates.
Such a method can enhance the reliability and safety of
industrial equipment. "The ability to accurately diagnose faults
despite incorrect annotations will lead to more reliable preventive
maintenance strategies. This can prevent unexpected machinery
failures, reducing downtime and maintenance costs," concludes
Prof. Lei.
Reference
Title of original paper: Label Recovery and Trajectory
Designable Network for Transfer Fault Diagnosis of Machines With
Incorrect Annotation
Journal: IEEE/CAA Journal of Automatica Sinica
DOI: https://doi.org/10.1109/JAS.2023.124083
Authors: Bin Yang1,2, Yaguo
Lei1, Xiang
Li1, Naipeng Li1, and Asoke K. Nandi3
Affiliations:
1 Xi'an Jiaotong University, China
2 Hunan University of
Science and Technology, China
3 Brunel University London, United Kingdom
Contact:
Yan Ou
+86 10 82544459
379948@email4pr.com
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SOURCE IEEE/CAA Journal of Automatica Sinica