Abstract
The ability to determine Remaining Useful Life
(RUL) of a turbofan engine is a foundational ability with regard
to predictive maintenance. In this article, Recurrent Neural
Network (RNN) and Long Short-Term Memory (LSTM) have
been studied in order to estimate the RUL.
The initial RUL is shown to impact RUL predictions. To find
the optimal initial RUL, a better method is needed. The method
suggested in this article involves creating geometric centers with
the data and finding the distance between these centers using the
Manhattan norm. The infliction point of the distances between
the centers is then used to determine the initial RUL.
Furthermore, the LSTM multilayering and the optimal sequence length for the predictions are explored in this paper.
Through experimental data evaluation on the Mean Squared
Error (MSE), Mean Absolute Error (MAE), and the given scoring
function, the Manhattan norm outperforms other methods for
determining the initial RUL by more than 100% with the scoring
function value of 6274.
(RUL) of a turbofan engine is a foundational ability with regard
to predictive maintenance. In this article, Recurrent Neural
Network (RNN) and Long Short-Term Memory (LSTM) have
been studied in order to estimate the RUL.
The initial RUL is shown to impact RUL predictions. To find
the optimal initial RUL, a better method is needed. The method
suggested in this article involves creating geometric centers with
the data and finding the distance between these centers using the
Manhattan norm. The infliction point of the distances between
the centers is then used to determine the initial RUL.
Furthermore, the LSTM multilayering and the optimal sequence length for the predictions are explored in this paper.
Through experimental data evaluation on the Mean Squared
Error (MSE), Mean Absolute Error (MAE), and the given scoring
function, the Manhattan norm outperforms other methods for
determining the initial RUL by more than 100% with the scoring
function value of 6274.
| Original language | English |
|---|---|
| Title of host publication | 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) |
| Number of pages | 7 |
| Publisher | IEEE |
| Publication date | Sept 2023 |
| ISBN (Electronic) | 979-8-3503-2297-2, 979-8-3503-2298-9 |
| DOIs | |
| Publication status | Published - Sept 2023 |
| Event | The International Conference on Electrical, Computer, Communications and Mechatronics Engineering: IEEE - Hotel Escuela Santa Cruz , Tenerifr, Canary Islands, Spain Duration: 19 Jul 2021 → 21 Jul 2023 Conference number: 3 https://hk.aconf.org/conf_186431 |
Conference
| Conference | The International Conference on Electrical, Computer, Communications and Mechatronics Engineering |
|---|---|
| Number | 3 |
| Location | Hotel Escuela Santa Cruz |
| Country/Territory | Spain |
| City | Tenerifr, Canary Islands |
| Period | 19/07/2021 → 21/07/2023 |
| Internet address |
Keywords
- LSTM
- Manhattan Distances
- Predictive Maintenance,
- RNN
- RUL
- Turbofan Engine