A comparison between extreme learning machine and artificial neural network for remaining useful life prediction

Given the difficulty of developing physics-based degradation process models in practice, data-driven prognostics approaches are preferred in several industrial applications. Among data-driven approaches, one can distinguish between (i) degradation-based approaches that predict the future evolution of the equipment degradation and (ii) direct Remaining Useful Life (RUL) prediction approaches which directly predict the equipment RUL. In this work, we compare two direct RUL prediction approaches one based on Back Propagation-Artificial Neural Networks (BP-ANN) and the other one on Extreme Learning Machines (ELM). The two approaches are compared on data from turbofan engines. We consider different prognostic metrics such as RMSE, Accuracy Index, Steadiness Index, a-I metric and the time necessary to train and execute the model. The obtained results show that the ELM-based model is performing only slightly worse than the BP-ANN-based model in terms of accuracy and stability, but it requires a considerably shorter training time