TY  - JOUR
KW  - Computer aided instruction; Decision making; Forecasting; Multilayer neural networks; Normal distribution; Uncertainty analysis
KW  -  Aleatoric uncertainty; CMAPPS; Health indices; Neural-networks; Probabilistic neural network; Probabilistics; Remaining useful life predictions; Remaining useful lives; Turbofan; Uncertainty
KW  -  Long short-term memory
N1  - cited By 0; Conference of 7th International Conference on Production, Energy and Reliability, ICPER 2020 ; Conference Date: 14 July 2020 Through 16 July 2020; Conference Code:284729
SP  - 529
ID  - scholars19519
TI  - Demonstrating Aleatoric Uncertainty in Remaining Useful Life Prediction Using LSTM with Probabilistic Layer
Y1  - 2023///
EP  - 544
A1  - Bin Mohd Nor, A.K.
A1  - Pedapati, S.R.
A1  - Muhammad, M.
A1  - Abdul Majid, M.A.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140712927&doi=10.1007%2f978-981-19-1939-8_41&partnerID=40&md5=0e65bc95401b898dd0ecdc244a7d409f
AV  - none
N2  - A Remaining Useful Life prediction with Aleatoric uncertainty is presented in this paper.A Long Short-Term Memory (LSTM) architecture with probabilistic layer is employed where a normal distribution layer is incorporated to produce the predicted Health Index (HI) distribution of turbofan engines.Compared to the performance of other point estimates techniques in the literature, the probabilistic LSTM achieved a competitive performance in predicting the turbofanâ??s RUL and RUL sequence and have the advantage to express the level of uncertainty along its sequence prediction.This work is important as it reflect a real-world deep learning application where uncertainty indication is needed to evaluate prediction for important decision-making process. © 2023, Institute of Technology PETRONAS Sdn Bhd.
JF  - Lecture Notes in Mechanical Engineering
ER  -