Gearbox fault detection using static data and adaptive neurofuzzy inference system

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
detection.