Anomaly monitoring improves remaining useful life estimation of industrial machinery. Aydemir, G. & Acar, B. Journal of Manufacturing Systems, 56:463–469, July, 2020. ![Anomaly monitoring improves remaining useful life estimation of industrial machinery [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Estimating remaining useful life (RUL) of industrial machinery based on their degradation data is very critical for various industries. Machine learning models are powerful and very popular tools for predicting time to failure of such industrial machinery. However, RUL is ill-defined during healthy operation. This paper proposes to use anomaly monitoring during both RUL estimator training and deployment to tackle with this problem. In this approach, raw sensor data is monitored and when a statistically significant change is detected, it is taken as the degradation onset point and a data-driven RUL estimation model is triggered. Initial results with a simple anomaly detector, suited for non-varying operating conditions, and multiple RUL estimation models showed that the anomaly triggered RUL estimation scheme enhances the estimation accuracy, on in-house simulation and benchmark C-MAPSS turbofan engine degradation data. The scheme can be employed to varying operating conditions with a suitable anomaly detector.
@article{aydemir_anomaly_2020,
title = {Anomaly monitoring improves remaining useful life estimation of industrial machinery},
volume = {56},
issn = {0278-6125},
url = {https://www.sciencedirect.com/science/article/pii/S0278612520301060},
doi = {10.1016/j.jmsy.2020.06.014},
abstract = {Estimating remaining useful life (RUL) of industrial machinery based on their degradation data is very critical for various industries. Machine learning models are powerful and very popular tools for predicting time to failure of such industrial machinery. However, RUL is ill-defined during healthy operation. This paper proposes to use anomaly monitoring during both RUL estimator training and deployment to tackle with this problem. In this approach, raw sensor data is monitored and when a statistically significant change is detected, it is taken as the degradation onset point and a data-driven RUL estimation model is triggered. Initial results with a simple anomaly detector, suited for non-varying operating conditions, and multiple RUL estimation models showed that the anomaly triggered RUL estimation scheme enhances the estimation accuracy, on in-house simulation and benchmark C-MAPSS turbofan engine degradation data. The scheme can be employed to varying operating conditions with a suitable anomaly detector.},
language = {en},
urldate = {2021-09-28},
journal = {Journal of Manufacturing Systems},
author = {Aydemir, Gurkan and Acar, Burak},
month = jul,
year = {2020},
keywords = {Anomaly detection, Industrial prognostics and health management, Machine learning, Remaining Useful Life (RUL) estimation, sigkdd-rw},
pages = {463--469},
}
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