Generalized MAML for few-shot cross-domain fault diagnosis of bearing driven by heterogeneous signals

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Lin, Jian and Shao, Haidong and Zhou, Xiangdong and Cai, Baoping and Liu, Bin (2023) Generalized MAML for few-shot cross-domain fault diagnosis of bearing driven by heterogeneous signals. Expert Systems with Applications, 230. 120696. ISSN 0957-4174 (https://doi.org/10.1016/j.eswa.2023.120696)

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Abstract

Despite a few recent meta-learning studies have facilitated few-shot cross-domain fault diagnosis of bearing, they are limited to homogenous signal analysis and have challenges to flexibly extract generic diagnostic knowledge for multiple meta-tasks. In order to solve these problems, this paper presents generalized model-agnostic meta-learning (GMAML) for few-shot fault diagnosis of bearings cross various operating conditions driven by heterogeneous signals. The proposed method involves constructing a channel interaction feature encoder using multi-kernel efficient channel attention, which allows for focusing on mutual fault information and enabling effective extraction of general diagnostic knowledge for multiple diagnostic meta-tasks. Additionally, a flexible weight guidance factor is designed to adjust the training strategy and optimize the inner loop weights for different diagnostic meta-tasks, improving the overall generalization performance. This method is applied to analyse the acceleration and acoustic signals of bearings, and its extensiveness and effectiveness are verified through various few-shot cross-domain scenarios.

ORCID iDs

Lin, Jian, Shao, Haidong, Zhou, Xiangdong, Cai, Baoping and Liu, Bin ORCID logoORCID: https://orcid.org/0000-0002-3946-8124;
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