A General Regression Neural Network Model for Gearbox Fault Detection using Motor Operating Parameters

Condition monitoring of a gearbox is a very
important activity because of the importance of gearboxes in
power transmission in many industrial processes. 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 on developing gearbox monitoring
methods based on operating parameters which are available in
machine control processes rather than using additional
measurements such as vibration and acoustics used in many
studies. To utilise these parameters for gearbox monitoring,
this paper examines a model based approach in which a data
model has been developed using a General Regression Neural
Network (GRNN) to captures the nonlinear connections
between the electrical current of driving motor and control
parameters such as load settings and temperatures based on a
two stage helical gearbox power transmission system. Using the
model a direct comparison can be made between the measured
and predicted values to find abnormal gearbox conditions of
different gear tooth breakages based on a threshold setup in
developing the model.