Computer Science > Machine Learning
arXiv:2503.11824 (cs)
[Submitted on 14 Mar 2025]
Title:Semi-Supervised Co-Training of Time and Time-Frequency Models: Application to Bearing Fault Diagnosis
View a PDF of the paper titled Semi-Supervised Co-Training of Time and Time-Frequency Models: Application to Bearing Fault Diagnosis, by Tuomas Jalonen and 3 other authors
View PDFHTML (experimental)Abstract:Neural networks require massive amounts of annotated data to train intelligent solutions. Acquiring many labeled data in industrial applications is often difficult; therefore, semi-supervised approaches are preferred. We propose a new semi-supervised co-training method, which combines time and time-frequency (TF) machine learning models to improve performance and reliability. The developed framework collaboratively co-trains fast time-domain models by utilizing high-performing TF techniques without increasing the inference complexity. Besides, it operates in cloud-edge networks and offers holistic support for many applications covering edge-real-time monitoring and cloud-based updates and corrections. Experimental results on bearing fault diagnosis verify the superiority of our technique compared to a competing self-training method. The results from two case studies show that our method outperforms self-training for different noise levels and amounts of available data with accuracy gains reaching from 10.6% to 33.9%. They demonstrate that fusing time-domain and TF-based models offers opportunities for developing high-performance industrial solutions.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Signal Processing (eess.SP) |
| Cite as: | arXiv:2503.11824 [cs.LG] |
| (orarXiv:2503.11824v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2503.11824 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Semi-Supervised Co-Training of Time and Time-Frequency Models: Application to Bearing Fault Diagnosis, by Tuomas Jalonen and 3 other authors
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