Introducing CNN-LSTM network adaptations to improve remaining useful life prediction of complex systems

Introducing CNN-LSTM network adaptations to improve remaining useful life prediction of complex systems

Borst, N.G. (TU Delft Air Transport & Operations)
Verhagen, W.J.C. (TU Delft Air Transport & Operations; Royal Melbourne Institute of Technology University)

2023

Prognostics and Health Management (PHM) models aim to estimate remaining useful life (RUL) of complex systems, enabling lower maintenance costs and increased availability. A substantial body of work considers the development and testing of new models using the NASA C-MAPSS dataset as a benchmark. In recent work, the use of ensemble methods has been prevalent. This paper proposes two adaptations to one of the best-performing ensemble methods, namely the Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) network developed by Li et al. (IEEE Access, 2019, 7, pp 75464-75475)). The first adaptation (adaptable time window, or ATW) increases accuracy of RUL estimates, with performance surpassing that of the state of the art, whereas the second (sub-network learning) does not improve performance. The results give greater insight into further development of innovative methods for prognostics, with future work focusing on translating the ATW approach to real-life industrial datasets and leveraging findings towards practical uptake for industrial applications.

Neural networks
Predictive maintenance
Prognostics
Remaining useful life

http://resolver.tudelft.nl/uuid:0eee41db-eee0-4a49-b9aa-ab96ee09a937

https://doi.org/10.1017/aer.2023.84

0001-9240

The Aeronautical Journal, 127 (1318), 2143-2153

Institutional Repository

journal article

© 2023 N.G. Borst, W.J.C. Verhagen