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.