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Gearbox fault detection using static data and adaptive neurofuzzy inference system

Baqqar, Mabrouka, Tran, Van Tung, Gu, Fengshou and Ball, Andrew (2013) Gearbox fault detection using static data and adaptive neurofuzzy inference system. In: COMADEM 2013, 11th-13th June /2013, Helsinki, Finland.

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Condition monitoring of a gearbox is a crucial
activity due to its importance in power
transmission for many industrial applications.
Thus, there has always been a constant pressure
to improve measuring techniques and analytical
tools for early detection of faults in gearboxes.
This study forces to develop the gearbox
monitoring methods using the operating
parameters obtained from machine control
processes rather than the traditional
measurements such as vibration and acoustics.
To monitor the gearbox conditions, an adaptive
neuro-fuzzy inference system (ANFIS) is used to
captures the nonlinear connections between the
electrical motor current and control parameters
such as load settings and temperatures. The
predicted values generated by ANFIS model are
then compared with the measured values to
indicate the abnormal condition in gearbox. The
experimental results show that ANFIS model is
adequate and is able to serve as an efficient tool
for gearbox condition monitoring and fault

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering
Related URLs:
Depositing User: Mabrouka Baqqar
Date Deposited: 22 Apr 2014 12:41
Last Modified: 28 Aug 2021 19:15


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