首页|Simultaneous Fault Diagnosis and Size Estimation Using Multitask Federated Incremental Learning

Simultaneous Fault Diagnosis and Size Estimation Using Multitask Federated Incremental Learning

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  • NETL
  • NSTL
  • IEEE
Federated learning (FL)-based fault diagnosis is being widely developed. However, most of the existing FL methods may suffer from two drawbacks: 1) they are limited to a single diagnosis task, and this may be insufficient when comprehensive health status information is needed and 2) most of them work offline, thus neglecting the useful information contained in newly collected operation data. For this end, this article proposes a multitask federated incremental learning (multitask-FIL) framework. First of all, a multitask feature sharing network is established by assigning the extracted general features to different downstream tasks, so that the joint loss function is obtained for subsequent collaborative training. Then, Q-learning algorithm is used to select the incremental sequences for all the parties from real-time running data, which can facilitate the model performance by involving additional data information and preferred parties. After that, the incremental weight of each party is dynamically adjusted according to the loss depth and sample size in each round of communication, so that the effects of different parties can be quantified throughout the model iteration and aggregation process. Finally, experiments on three challenging cases are performed to show that the proposed method has strong multitask collaboration capability.

Fault diagnosisData modelsTask analysisServersQ-learningEstimationLogic gates

Kai Zhong、Zhengping Ding、Haifeng Zhang、Hongtian Chen、Enrico Zio

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Institutes of Physical Science and Information Technology, Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education, Anhui University, Hefei, China

Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Mathematical Science, Anhui University, Hefei, China

Department of Automation, Shanghai Jiao Tong University, Shanghai, China

Centre de Recherche sur les Risques et les Crises (CRC), Mines Paris-PSL, Sophia Antipolis, France|Department of Energy, Politecnico di Milano, Milan, Italy

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2025

10.1109/TR.2024.3402308

IEEE transactions on reliability

IEEE transactions on reliability

ISSN：
年,卷(期)：2025.74(1)
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