Trans-Lighter: A light-weight federated learning-based architecture for Remaining Useful Lifetime prediction

Du, Nguyen Huu; Long, Nguyễn Hoàng; Ha, Kieu Ngan; Hoàng, Nguyễn Viết; Hương, Trương Thu; Tran, Kim Phuc

doi:10.1016/j.compind.2023.103888

Cited by 12 publications

(1 citation statement)

References 28 publications

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“…Guo et al [172] developed an FL method for milling cutter life estimation, in which the cloud server assigns weights to each client based on convolutional autoencoder (CAE) reconstruction errors, and the prognostic model is trained centrally by the server. Considering the weak computing power of edge devices, [173,174] studied the development strategy of lightweight model under FL framework. In FL, the training data remains on local devices, and each device typically has limited data available for RUL prediction.…”

Section: Cutting-edge Methods In DLmentioning

confidence: 99%

A comprehensive survey of machine remaining useful life prediction approaches based on pattern recognition: taxonomy and challenges

Zhou,

Yang,

Qian

et al. 2024

Meas. Sci. Technol.

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Predictive maintenance (PdM) is currently the most cost-effective maintenance method for industrial equipment, offering improved safety and availability of mechanical assets. A crucial component of PdM is the remaining useful life (RUL) prediction for machines, which has garnered increasing attention. With the rapid advancements in industrial Internet of Things (IoT) and artificial intelligence (AI) technologies, RUL prediction methods, particularly those based on pattern recognition (PR) technology, have made significant progress. However, a comprehensive review that systematically analyzes and summarizes these state-of-the-art PR-based prognostic methods is currently lacking. To address this gap, this paper presents a comprehensive review of PR-based RUL prediction methods. Firstly, it summarizes commonly used evaluation indicators based on accuracy metrics, prediction confidence metrics, and prediction stability metrics. Secondly, it provides a comprehensive analysis of typical machine learning methods and deep learning networks employed in RUL prediction. Furthermore, it delves into cutting-edge techniques, including advanced network models and frontier learning theories in RUL prediction. Finally, the paper concludes by discussing the current main challenges and prospects in the field. The intended audience of this article includes practitioners and researchers involved in machinery PdM, aiming to provide them with essential foundational knowledge and a technical overview of the subject matter.

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Section: Cutting-edge Methods In DLmentioning

confidence: 99%