Prediction of the Remaining Useful Life (RUL) for industrial systems has been facilitated by the acquisition of large amounts of real-time data and the use of deep learning methods. However, the vast majority of these methods rely on the availability of extensive RUL-labeled data, which is not the case for most of real industrial applications. The goal of this paper is to show how unsupervised learning can provide alternative ways to address this issue. The proposed method is essentially made of two steps. First, a Virtual Health Index (VHI) is extracted in an unsupervised manner from the raw sensor data using a Deep Convolutional Neural Network (CNN) autoencoder. Secondly, an Long-Short Term Memory (LSTM) Encoder-Decoder predicts the future values of the VHI, until an End-of-Life (EOL) pattern is recognized (using a sliding window DTW algorithm). The suggested method is tested on the C-MAPSS dataset and offers promising results with a great potential to be applicable on real-life use cases.
How to Cite
Prognostics, Remaining Useful Life, deep learning, unsupervised learning, Deep Convolutional Neural Network, Long-Short Term Memory
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.