FRAN-X: An improved diagnostic transfer learning approach with application to ball bearings fault diagnosis
Contributo in Atti di convegno
Data di Pubblicazione:
2023
Citazione:
(2023). FRAN-X: An improved diagnostic transfer learning approach with application to ball bearings fault diagnosis . Retrieved from https://hdl.handle.net/10446/260098
Abstract:
Data-driven diagnostic methods are attractive from an industrial and practical perspective due to their limited amount of required prior knowledge about the process or component under monitoring. However, these methods usually require a large amount of healthy and possibly faulty labeled data. Often, gathering and manually labeling a vast dataset is not feasible in real scenarios. Transfer learning has emerged as an answer to the labeling problem, exploiting the idea that the diagnostic knowledge could be reused across multiple different, but related, machines and operating conditions. In this work, we introduce several improvements to the Feature Representation and Alignment Network (FRAN) architecture described in (Chen et al., 2020) devised with the diagnostic transfer learning purpose. Our approach, named FRAN-X, presents improved transfer and diagnostics performance between identical machines in different operating conditions, and it is computationally lighter than its original counterpart. The FRAN-X approach is evaluated on the CWRU-bearing dataset and on experimental data collected from a Computerized Numerical Control (CNC) workcenter machine.
Tipologia CRIS:
1.4.01 Contributi in atti di convegno - Conference presentations
Elenco autori:
Pitturelli, Leandro; Mazzoleni, Mirko; Rillosi, Luca; Previdi, Fabio
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Link al Full Text:
Titolo del libro:
22nd IFAC World Congress. Yokohama, Japan, July 9-14, 2023 Proceedings
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