arXiv Machine Learning July 21, 2026 By Md Mahamudur Rahaman Shamim, Md. Nuruzzaman, Zannatul Ferdus, Md Rajib Ahmed, Abieer Nwshad Anward, Mohammad Tooneer, Johir Uddin Khan, Khalid Hossen

Leakage-Robust Evaluation and Data-Scale Sensitivity of Attention-Enhanced Multi-Task Learning for Joint Fault Diagnosis and Remaining Useful Life Estimation

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arXiv:2607. 16493v1 Announce Type: new Abstract: Multi-task deep learning models that jointly perform fault classification and remaining useful life (RUL) regression are increasingly used in predictive maintenance, yet reported performance can be strongly affected by how sliding-window sequences are split into training and test sets.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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