Recovering missing data to improve data quality is of great importance for prognostics and health management (PHM) of structures and machinery. Existing missing data reconstruction methods are generally limited by strong assumptions such as signal sparsity, coupled with heavy computational burdens, making them poorly adaptable to PHM data. To obtain more reliable PHM results, a novel algorithm is developed to handle missing data based on compressive sensing and enhanced context encoders (CS-ECE). The CS-ECE can reconstruct the detailed characteristics of signals in both the time and frequency domains. The effectiveness of the proposed method is validated using real-world data collected from a high-speed train. The results demonstrate that CS-ECE can obtain realistic and accurate recovered results, thereby providing a high-quality dataset for data-driven PHM studies.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Sparsity-Free Compressed Sensing Method for PHM Data Quality Assurance Using Generative Adversarial Network


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Qin, Yong (editor) / Jia, Limin (editor) / Yang, Jianwei (editor) / Diao, Lijun (editor) / Yao, Dechen (editor) / An, Min (editor) / Xie, Qinglin (author) / Wang, Jing (author) / Tao, Gongquan (author) / Xie, Chenxi (author)

    Conference:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2023 ; Beijing, China October 19, 2023 - October 21, 2023



    Publication date :

    2024-02-03


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Traffic accident data quality enhancement method based on generative adversarial network

    ZHOU BEI / ZHANG SHENGRUI / ZHANG YING et al. | European Patent Office | 2023

    Free access



    Generative adversarial network enriched driving simulation

    SONG HAO / PENG JUN / DENG NENGXIU et al. | European Patent Office | 2022

    Free access

    Regularized sparsity variable step-size adaptive matching pursuit algorithm for compressed sensing

    Liu, Haoqiang / Zhao, Hongbo / Feng, Wenquan | British Library Online Contents | 2017