An integrated network architecture for data repair and degradation trend prediction

Yang, Qichao; Tang, Baoping; Yang, Shilong; Shen, Yizhe

doi:10.1016/j.ymssp.2023.110610

Mechanical Systems and Signal Processing

2023

DOI: 10.1016/j.ymssp.2023.110610

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An integrated network architecture for data repair and degradation trend prediction

Qichao Yang

Baoping Tang

Shilong Yang

et al.

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Cited by 9 publications

References 42 publications

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Combined improved tuna swarm optimization with graph convolutional neural network for remaining useful life of engine

Yuan,

Yang,

Wang

et al. 2024

Quality & Reliability Eng

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Accurate prediction of the engine's remaining useful life (RUL) is essential to ensure the safe operation of the aircraft because. However, traditional deep‐learning based methods for RUL prediction has been limited by interpretability and adjustment for hyperparameters in practical applications due to the intricate potential relations during the degradation process. To address these dilemmas, an improved multi‐strategy tuna swarm optimization‐assisted graph convolutional neural network (IMTSO‐GCN) is developed to achieve RUL prediction in this work. Specifically, mutual information is used to describe potential relationships among measured parameters so that they could be utilized in building edges for these parameters. Besides, considering that not all relational nodes will positively affect the RUL prediction and the inherent hyperparameters of the GCN are high‐dimensional. Inspired by “No Free Lunch (NFL)”, IMTSO is proposed to reduce the optimization cost of hyperparameters, in which cycle chaotic mapping is employed to achieve initialization of the population for improving the uniformity of the initial population distribution. Besides, a novel adaptive approach is proposed to enhance the exploration and exploitation of tuna swarm optimization (TSO). The CMAPSS dataset was used to validate the effectiveness and advancedness of IMTSO‐GCN, and the experimental results show that the R2 of the IMTSO‐GCN is greater than 0.99, RMSE is less than 3, the Score error is within 1.

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Combined improved tuna swarm optimization with graph convolutional neural network for remaining useful life of engine

Yuan,

Yang,

Wang

et al. 2024

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

WTFormer: RUL prediction method guided by trainable wavelet transform embedding and lagged penalty loss

Yang,

Tang,

Deng

et al. 2024

Advanced Engineering Informatics

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Adaptive early initial degradation point detection and outlier correction for bearings

Yang,

Tang,

Deng

et al. 2025

Computers in Industry

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An integrated network architecture for data repair and degradation trend prediction

Cited by 9 publications

References 42 publications

Combined improved tuna swarm optimization with graph convolutional neural network for remaining useful life of engine

Combined improved tuna swarm optimization with graph convolutional neural network for remaining useful life of engine

WTFormer: RUL prediction method guided by trainable wavelet transform embedding and lagged penalty loss

Adaptive early initial degradation point detection and outlier correction for bearings

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