New Study Improves Fault Diagnosis Accuracy in Machines with Deep Transfer Learning

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.


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


Authors: Bin Yang1,2, Yaguo Lei1, Xiang Li1, Naipeng Li1, and Asoke K. Nandi3

1 Xi’an Jiaotong University, China
2 Hunan University of Science and Technology, China
3 Brunel University London, United Kingdom

Yan Ou
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SOURCE IEEE/CAA Journal of Automatica Sinica