Optimisation of deep learning techniques on remaining useful life prediction of complex engineering systems

Abstract

Addepalli, Pavan - Associate SupervisorPredictive maintenance based on performance degradation is a crucial way to reduce the operation and maintenance costs and potential failures in modern complex engineering systems. Reliable remaining useful life (RUL) prediction is the main criterion for predictive maintenance decisions. Data-driven techniques, especially artificial intelligence (AI) such as deep learning (DL) techniques, have attracted more and more attention in the manufacturing sector with the development of Big Data and Internet-of-Things. Different DL techniques recently have been used for RUL prediction and achieved great success. However, in many cases, the RUL prediction results vary greatly due to the measurement noise and selection of model parameters. This PhD research aims to develop a novel framework to optimise the performance of deep learning methods in the context of predicting the RUL of complex engineering systems. The project consists of four stages including literature review, optimisation investigations, construction of the new prognostic framework and validation. In the first stage, state-of-the-art DL-based approaches for RUL prediction are reviewed. Then, the optimisation investigations on different RNN models, feature engineering, model parameters and RUL target functions are carried out trying to improve the RUL prediction accuracy. After that, the prognostic framework is built based on the result of the optimisation investigation. Meanwhile, a novel three-stage feature selection method and a multi-scale RUL prediction approach are proposed in this stage. In addition, to accommodate the multiple operating conditions of complex engineering systems, this thesis presents an operation-based normalisation method to address the different degradation patterns from data. In the last stage, the C-MAPSS dataset is adopted to validate the proposed framework and the prediction performance is compared with the state-of-the-art RUL prediction approaches. A significant improvement can be observed in the RUL prediction performance using the proposed framework on most of the subsets of the C-MAPSS dataset. Therefore, a reliable and flexible RUL prediction strategy can be made based on this DL-based prognostics framework for complex engineering systemsPhD in Manufacturin