This paper presents a neural network system-GTNN for identifying the gapping of the main journal bearing of engines. Calculation results are compared with the experiment data, and the error of them is acceptable. Finally the explanation of the calculation result is given.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Neural network identifying system for the gapping of main journal bearing of engine


    Contributors:
    Jiang Xuejun (author) / Tang Fei (author) / Li Zhimin (author) / Qi Baohui (author)


    Publication date :

    1999-01-01


    Size :

    278475 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Neural Network Identifying System for the Gapping of Main Journal Bearing of Engine

    Jiang, X. / Li, Z. / Tang, F. et al. | British Library Conference Proceedings | 1999


    Influence of Journal Alignment on Main Bearing of Large 2-Stroke Marine Diesel Engine

    Sugimoto, Iwao / Baba, Shinji / Yatsuo, Masao et al. | TIBKAT | 2000



    In-Situ Measurement and Numerical Solution of Main Journal Bearing Lubrication in Actual Engine Environment

    Harada, Hironori / Matsumoto, Kenji / Mihara, Yuji et al. | SAE Technical Papers | 2016


    In-Situ Measurement and Numerical Solution of Main Journal Bearing Lubrication in Actual Engine Environment

    Matsumoto, Kenji / Harada, Hironori / Ono, Yuki et al. | British Library Conference Proceedings | 2016