Prognostics of machine condition using energy based monitoring index and computational intelligence

A procedure is presented for monitoring and prognostics of machine conditions using computational intelligence (CI) techniques. The machine condition is assessed through an energy-based feature, termed as 'energy index/, extracted from the vibration signals. The progression of the 'monitoring index/ is predicted using CI techniques, namely, recursive neural network (RNN), adaptive neuro-fuzzy inference system (ANFIS) and support vector regression (SVR). The proposed prediction procedures have been evaluated through benchmark datasets. The prognostic effectiveness of the techniques has been illustrated through vibration dataset of a helicopter drivetrain system gearbox. The performance of SVR was found to be better than RNN and ANFIS for the dataset used. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating feature, the level of damage/degradation and their progression.