vigneashpandiyan / tribology-lstm-encoder_decoder

Long short-term memory based semi-supervised encoder-decoder for early prediction of failures in self-lubricating bearings

Tribology-LSTM-Encoder_Decoder

Code for Long short-term memory based semi-supervised encoder-decoder for early prediction of failures in self-lubricating bearings

Journal link

https://link.springer.com/article/10.1007/s40544-021-0584-3

Overview

Wear mechanisms are still inevitable and occur progressively in self-lubricating bearings, as characterized by the loss of the lubrication film and seizure. Our article proposes a methodology for using a long short-term memory (LSTM)-based encoder—decoder architecture on interfacial force signatures to detect abnormal regimes, aiming to provide early predictions of failure in self-lubricating sliding contacts even before they occur. The reconstruction error across the entire force signature showed noticeable patterns in the reconstruction error when temporally decoded before the actual critical failure point, making it possible to be used for early predictions of failure.

Experimental setup

Code

git clone https://github.com/vigneashpandiyan/Tribology-LSTM-Encoder_Decoder
cd Tribology-LSTM-Encoder_Decoder
python Main.py

#Data https://polybox.ethz.ch/index.php/s/LktxpBVaB7x2YQn

Results