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.

(2023). FRAN-X: An improved diagnostic transfer learning approach with application to ball bearings fault diagnosis . Retrieved from https://hdl.handle.net/10446/260098

FRAN-X: An improved diagnostic transfer learning approach with application to ball bearings fault diagnosis

Pitturelli, Leandro;Mazzoleni, Mirko;Previdi, Fabio
2023-01-01

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.
2023
Pitturelli, Leandro; Mazzoleni, Mirko; Rillosi, Luca; Previdi, Fabio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/260098
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