Masked Self-Supervision for Remaining Useful Lifetime Prediction in Machine Tools

TL;DR

This paper introduces a masked self-supervised learning approach for predicting the remaining useful lifetime of machine tools, effectively utilizing unlabeled data to improve accuracy over traditional supervised methods.

Contribution

The work develops a novel masked autoencoder-based self-supervised method for RUL prediction, reducing reliance on labeled data from faulty machines.

Findings

Outperforms fully-supervised models in accuracy and effectiveness

Utilizes unlabeled healthy machine data for training

Validated on NASA C-MAPSS dataset

Abstract

Prediction of Remaining Useful Lifetime(RUL) in the modern manufacturing and automation workplace for machines and tools is essential in Industry 4.0. This is clearly evident as continuous tool wear, or worse, sudden machine breakdown will lead to various manufacturing failures which would clearly cause economic loss. With the availability of deep learning approaches, the great potential and prospect of utilizing these for RUL prediction have resulted in several models which are designed driven by operation data of manufacturing machines. Current efforts in these which are based on fully-supervised models heavily rely on the data labeled with their RULs. However, the required RUL prediction data (i.e. the annotated and labeled data from faulty and/or degraded machines) can only be obtained after the machine breakdown occurs. The scarcity of broken machines in the modern manufacturing and automation workplace in real-world situations increases the difficulty of getting sufficient annotated and labeled data. In contrast, the data from healthy machines is much easier to be collected. Noting this challenge and the potential for improved effectiveness and applicability, we thus propose (and also fully develop) a method based on the idea of masked autoencoders which will utilize unlabeled data to do self-supervision. In thus the work here, a noteworthy masked self-supervised learning approach is developed and utilized. This is designed to seek to build a deep learning model for RUL prediction by utilizing unlabeled data. The experiments to verify the effectiveness of this development are implemented on the C-MAPSS datasets (which are collected from the data from the NASA turbofan engine). The results rather clearly show that our development and approach here perform better, in both accuracy and effectiveness, for RUL prediction when compared with approaches utilizing a fully-supervised model.