Gears are important machine elements that transfer motion by the meshing of teeth. In this modern era gearbox has become an essential component in industry as well common man day to day life. In most of the industrial conditions gear boxes are subjected to continuous operation, which in many cases evades the maintenance activities. In such scenario the gear box may experience an unexpected failure, which will lead to shut down of the specific unit. Considering the significance of the gearbox, condition monitoring of gear box becomes essential. The helical gear box consists vital components like, helical gears of different ratios, bearing, shaft, gear shifting rod, plumber block etc. But the component which is prone to frequent failure must be prioritized and condition monitored, to ensure a continuous operation of the gearbox. That gives a scope for classification problem using machine learning algorithms. An experimental set was fabricated, and the vibration signals are acquired using a accelerometer sensor for the various faulty conditions like helical gear running in Good Condition (GC), Helical Gear with Tooth Crack Condition, (TCC), gear in Scuffed Condition (SC) combination of both in good condition (GC) and Scuffed Condition (SC). The signal acquisition system acquires the vibration signal under these conditions and fed into machine learning algorithms Naive Bayes and Support vector Machine (SVM). The Training Accuracy of signals of all three Sets under different Load conditions were acquired and accuracy of Naive Bayes was found to be around 92% which is far superior than any SVM Algorithm


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Real-Time Condition Monitoring of Multi-Component High Torque Helical Gearbox in Coal Handling Belt Conveyor System Using Machine Learning – A Statistical Approach


    Additional title:

    Sae Int. J. Adv. and Curr. Prac. in Mobility


    Contributors:

    Conference:

    International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility ; 2022



    Publication date :

    2022-12-23


    Size :

    9 pages




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Real-Time Gearbox Defect Detection Using IIoT-Based Condition Monitoring System

    Ajayan, Adarsh / Prabhu, M.K. / Ilakiya, P. et al. | SAE Technical Papers | 2023


    A Survey of Fuzzy Logic Systems in Condition Monitoring of Conveyor Gearbox

    Kgatwe, Calvinia K. / Madushele, Nkosinathi / Olatunji, Obafemi O. et al. | IEEE | 2022


    Chamber conveyor belt machine

    BRUNNER MAXIMILIAN / HÄRING RAINER / MÖSSNANG KONRAD | European Patent Office | 2018

    Free access

    GEARBOX TORQUE MEASUREMENT SYSTEM

    LARSON LOWELL VAN LUND / DEAKE JEREMY JASON | European Patent Office | 2022

    Free access

    CHAMBER CONVEYOR BELT MACHINE

    BRUNNER MAXIMILIAN / HÄRING RAINER / MÖSSNANG KONRAD | European Patent Office | 2017

    Free access