A hybrid prognostic model for multistep ahead prediction of machine condition

Prognostics are the future trend in condition based maintenance. In the current framework a data driven prognostic model is developed. The typical procedure of developing such a model comprises a) the selection of features which correlate well with the gradual degradation of the machine and b) the training of a mathematical tool. In this work the data are taken from a laboratory scale single stage gearbox under multi-sensor monitoring. Tests monitoring the condition of the gear pair from healthy state until total brake down following several days of continuous operation were conducted. After basic pre-processing of the derived data, an indicator that correlated well with the gearbox condition was obtained. Consecutively the time series is split in few distinguishable time regions via an intelligent data clustering scheme. Each operating region is modelled with a feed-forward artificial neural network (FFANN) scheme. The performance of the proposed model is tested by applying the system to predict the machine degradation level on unseen data. The results show the plausibility and effectiveness of the model in following the trend of the timeseries even in the case that a sudden change occurs. Moreover the model shows ability to generalise for application in similar mechanical assets.