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Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management

Rengasamy, Divish; Jafari, Mina; Rothwell, Benjamin; Chen, Xin; Figueredo, Grazziela P.

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Authors

Divish Rengasamy

Mina Jafari

XIN CHEN XIN.CHEN@NOTTINGHAM.AC.UK
Associate Professor



Abstract

Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model’s architecture. However, contributions regarding improvement of different aspects in deep learning, such as custom loss function for prognostic and health management are scarce. There is therefore an opportunity to improve upon the effectiveness of deep learning for the system’s prognostics and diagnostics without modifying the models’ architecture. To address this gap, the use of two different dynamically weighted loss functions, a newly proposed weighting mechanism and a focal loss function for prognostics and diagnostics task are investigated. A dynamically weighted loss function is expected to modify the learning process by augmenting the loss function with a weight value corresponding to the learning error of each data instance. The objective is to force deep learning models to focus on those instances where larger learning errors occur in order to improve their performance. The two loss functions used are evaluated using four popular deep learning architectures, namely, deep feedforward neural network, one-dimensional convolutional neural network, bidirectional gated recurrent unit and bidirectional long short-term memory on the commercial modular aero-propulsion system simulation data from NASA and air pressure system failure data for Scania trucks. Experimental results show that dynamically-weighted loss functions helps us achieve significant improvement for remaining useful life prediction and fault detection rate over non-weighted loss function predictions.

Citation

Rengasamy, D., Jafari, M., Rothwell, B., Chen, X., & Figueredo, G. P. (2021). Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management. Sensors, 20(3), https://doi.org/10.3390/s20030723

Journal Article Type Article
Acceptance Date Jan 20, 2020
Online Publication Date Jan 28, 2020
Publication Date Feb 1, 2021
Deposit Date Jan 31, 2020
Publicly Available Date Mar 29, 2024
Journal Sensors
Print ISSN 1424-8220
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 20
Issue 3
Article Number 723
DOI https://doi.org/10.3390/s20030723
Keywords Electrical and Electronic Engineering; Analytical Chemistry; Atomic and Molecular Physics, and Optics; Biochemistry
Public URL https://nottingham-repository.worktribe.com/output/3839607
Publisher URL https://www.mdpi.com/1424-8220/20/3/723

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