A Novel Method for Remaining Useful Life Prediction of RF Circuits Based on the Gated Recurrent Unit-Convolutional Neural Network Model.

Wanyu Yang, Kunping Wu, Bing Long, Shulin Tian

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

Data-Driven Remaining Useful Life Prediction for Pt-Rh Thermocouples Using an Extended Kalman Filter.Sensors (Basel, Switzerland) · 2026
Article

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5 · Who and what money

Authors and funding

4 authors.

Wanyu YangSchool of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China.ORCID 0009-0001-4215-6201

Kunping WuSchool of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China.

Bing LongSchool of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China.

Shulin TianSchool of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China.

Funding

National Natural Science Foundation of China U1830133Shenzhen Science and Technology Plan Project JSGG20220831101402005

6 · The paper itself

Abstract

The remaining useful life (RUL) prediction of RF circuits is an important tool for circuit reliability. Data-driven-based approaches do not require knowledge of the failure mechanism and reduce the dependence on knowledge of complex circuits, and thus can effectively realize RUL prediction. This manuscript proposes a novel RUL prediction method based on a gated recurrent unit-convolutional neural network (GRU-CNN). Firstly, the data are normalized to improve the efficiency of the algorithm; secondly, the degradation of the circuit is evaluated using the hybrid health score based on the Euclidean and Manhattan distances; then, the life cycle of the RF circuits is segmented based on the hybrid health scores; and finally, an RUL prediction is carried out for the circuits at each stage using the GRU-CNN model. The results show that the RMSE of the GRU-CNN model in the normal operation stage is only 3/5 of that of the GRU and CNN models, while the prediction uncertainty is minimized.

Indexed as

convolutional neural network (CNN)gated recurrent unit (GRU)reliabilityremaining useful life (RUL)RF circuit

Identifiers

PMID38732947

DOI10.3390/s24092841

PMCPMC11086388

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