SGBRT: An Edge-Intelligence Based Remaining Useful Life Prediction Model for Aero-Engine Monitoring System

In this paper, we develop an
edge intelligence
based aero-engine performance monitoring system. The proposed approach can effectively predict the
remaining useful life
of aero-engines, which is the main focus within the
prognostics and health management
framework – thus it provides support for optimal operation planning and maintenance decisions. The proposed model, which we term SGBRT, follows a hybrid machine learning approach, combining a
self-organizing mapping
network with a
gradient boosting regression tree
model. In particular, the SGBRT computes the
remaining useful life
of an aero-engine in two steps: it first employs a
self-organizing map
to cluster the sample data; and then it fits each cluster by way of a
gradient boosting regression tree
. Detailed simulation results with the C-MAPSS dataset show that this method achieves a higher prediction accuracy and better generalization than other conventional approaches; the compared methods range from classical approaches such as a
switching Kalman filter
to state-of-the-art deep learning models.