To fail or not to fail: an exploration of machine learning techniques for predictive maintenance

Samuel Dixon; Timothy Doster; Tegan Emerson

doi:10.1117/12.2619040

6 June 2022 To fail or not to fail: an exploration of machine learning techniques for predictive maintenance

Samuel Dixon, Timothy Doster, Tegan Emerson

Proceedings Volume 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV; 1211320 (2022) https://doi.org/10.1117/12.2619040
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Conference Poster

Abstract

Predictive maintenance refers to the ability to predict when machinery or systems need to be maintained. Making an accurate prediction is quite challenging given the costs for both over-estimating (unnecessary maintenance and reduction in availability of assets) and under-estimating (untimely breakdowns and possible loss of equipment or lives). To address these challenges researchers were able to develop new approaches for analyzing oil samples taken extracting samples from oil-wetted machinery that may provide information critical to developing predictive capabilities. We consider the problem from both supervised (though data limited) and unsupervised approaches and provide a first look into a data driven approach for identification of condition indicators. Through this work we identify a collection of candidate features that can form the basis of condition indicators for both a high level discrimination of failure vs. normal operation as well as a set for potential failure mode identification. Finally, we present an anomaly detection framework for detecting failures which can be a viable solution for an onboard analysis tool in deployed systems.

Citation Download Citation

Samuel Dixon, Timothy Doster, and Tegan Emerson "To fail or not to fail: an exploration of machine learning techniques for predictive maintenance", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 1211320 (6 June 2022); https://doi.org/10.1117/12.2619040

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