In the mining industry, primary digging units such as wheel loaders are critical components due to their position at the start of the process chain. Consequently, the cost of unexpected downtime is high: this motivates efforts to provide an early warning of faults using remote diagnostics.
Machines are equipped with sensors that measure machine health. Some sensors are highly correlated and a model based on machine learning techniques can leverage such relationships across sensors to detect within-group abnormalities.
Autoencoders are auto-associative artificial neural networks which are trained to compress and rebuild the original input with minimal loss. The information is stored in the lower dimensional hidden layers as an internal coding. This is susceptible to a phenomenon called spillover, where the error in a single input can propagate through the network, corrupting the coding and biasing the entire reconstructed data. A denoising autoencoder is a more robust variation on the traditional autoencoder, trained to remove noise and build an error-free reconstruction.
We created a denoising autoencoder to utilize the noise removal on corrupted inputs, and rebuild from working inputs. While this technique is novel to this problem it remained susceptible to spillover. We show our findings and discuss future anomaly detection techniques in correlated data.
How to Cite
correlated data, denoising autoencoder
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.