Version 1
: Received: 5 July 2019 / Approved: 8 July 2019 / Online: 8 July 2019 (10:08:05 CEST)
How to cite:
Singh, S.; Shiv, P.; Ahmed, A. Outlier Modeling in Gear Bearing Using Autoencoder for Remaining Useful Life Prediction. Preprints2019, 2019070112. https://doi.org/10.20944/preprints201907.0112.v1
Singh, S.; Shiv, P.; Ahmed, A. Outlier Modeling in Gear Bearing Using Autoencoder for Remaining Useful Life Prediction. Preprints 2019, 2019070112. https://doi.org/10.20944/preprints201907.0112.v1
Singh, S.; Shiv, P.; Ahmed, A. Outlier Modeling in Gear Bearing Using Autoencoder for Remaining Useful Life Prediction. Preprints2019, 2019070112. https://doi.org/10.20944/preprints201907.0112.v1
APA Style
Singh, S., Shiv, P., & Ahmed, A. (2019). Outlier Modeling in Gear Bearing Using Autoencoder for Remaining Useful Life Prediction. Preprints. https://doi.org/10.20944/preprints201907.0112.v1
Chicago/Turabian Style
Singh, S., Praneet Shiv and Atif Ahmed. 2019 "Outlier Modeling in Gear Bearing Using Autoencoder for Remaining Useful Life Prediction" Preprints. https://doi.org/10.20944/preprints201907.0112.v1
Abstract
In this paper, we introduce the Prognostics and Health Management of gear bearing system using autoencoder neural networks. Bearings and gears are the most common mechanical components in rotating machines, and their health conditions are of great concern in practice. This study presents an outlier modeling method for forecasting the gear bearing system failure using the health indicators constructed from mechanical signal processing and modeling. Outlier modeling aims to find patterns in data that are significantly different from what is defined as normal. In the unsupervised outlier modeling setting, prior labels about the anomalousness of data points are not available. In such cases, the most common techniques for scoring data points for outlyingness include distance-based methods density-based methods, and linear methods. The conventional outlier modeling methods have been used for a long time to detect anomalous observations in data. However, this paper shows that autoencoders are a very competitive technique compared to other existing methods. The developed method is demonstrated using the IMS bearing data from NASA Acoustics and Vibration Database.
Keywords
autoencoder; prognostics and health management; outlier modeling; remaining useful life; gear bearing
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
