Masked Self-Supervision for Remaining Useful Lifetime Prediction in
Machine Tools
- URL: http://arxiv.org/abs/2207.01219v1
- Date: Mon, 4 Jul 2022 06:08:01 GMT
- Title: Masked Self-Supervision for Remaining Useful Lifetime Prediction in
Machine Tools
- Authors: Haoren Guo, Haiyue Zhu, Jiahui Wang, Vadakkepat Prahlad, Weng Khuen
Ho, Tong Heng Lee
- Abstract summary: Prediction of Remaining Useful Lifetime (RUL) in the modern manufacturing workplace is essential in Industry 4.0.
With the availability of deep learning approaches, the great potential and prospect of utilizing these for RUL prediction have resulted in several models.
This is designed to seek to build a deep learning model for RUL prediction by utilizing unlabeled data.
- Score: 3.175781028910441
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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