Sparse auto encoder(SAE) can reduces information loss and extract the meaningful feature by learning the deep structure of complex data. This paper presents a novel SAE based semi-supervised feature learning method for fault diagnosis of batch process which includes two stages, namely, unsupervised pre-training stage and supervised tuning stage. At the unsupervised pre-training stage, denoising SAE(DSAE) is utilized by introducing denoising auto encoder into SAE to improve the robustness of network. At the supervised tuning stage, the pretrained DSAE netwrok is optimized using back propagation algorithm to improve the accuracy of classification. The proposed method is validated on penicillin fermentation simulation experiment and Escherichia coli fermentation experiment. Experimental results show that the proposed approach achieves good fault diagnostic performance and is superirior to the traditional fault diagnosis method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Fault diagnosis of batch process based on denoising sparse auto encoder


    Contributors:
    Gao, Xuejin (author) / Wang, Hao (author) / Gao, Huihui (author) / Wang, Xichang (author) / Xu, Zidong (author)


    Publication date :

    2018-05-01


    Size :

    657348 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Airborne converter electrical fault diagnosis method based on heap-embedded denoising automatic encoder

    LUO YUNHU / LYU QIHANG / CHEN WENMING | European Patent Office | 2023

    Free access

    Bearing Fault Diagnosis Method of Bearing Based on LSTM Auto-Encoder

    Lu, Zhencong / Qin, Yong / Cheng, Xiaoqing et al. | Springer Verlag | 2022


    Bearing Fault Diagnosis Method of Bearing Based on LSTM Auto-Encoder

    Lu, Zhencong / Qin, Yong / Cheng, Xiaoqing et al. | British Library Conference Proceedings | 2022


    Bearing Fault Diagnosis Method of Bearing Based on LSTM Auto-Encoder

    Lu, Zhencong / Qin, Yong / Cheng, Xiaoqing et al. | TIBKAT | 2022


    Fault diagnosis system and method combining convolution auto-encoder and logistic regression

    DONG WEI / ZHAI SHOUCHAO / SUN XINYA et al. | European Patent Office | 2020

    Free access