Deep learning-based anomaly-onset aware remaining useful life estimation of bearings

Remaining Useful Life (RUL) estimation of rotating machinery based on their degradation data is vital for machine supervisors. Deep learning models are effective and popular methods for forecasting when rotating machinery such as bearings may malfunction and ultimately break down. During healthy fun...

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Autores principales:	Pooja Vinayak Kamat, Rekha Sugandhi, Satish Kumar
Formato:	article
Lenguaje:	EN
Publicado:	PeerJ Inc. 2021
Materias:	Deep learning Predictive maintenance Anomaly detection Remaining useful life Bearing LSTM Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/352093102f5a464598971be0a57f886a
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id	oai:doaj.org-article:352093102f5a464598971be0a57f886a
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spelling	oai:doaj.org-article:352093102f5a464598971be0a57f886a2021-11-28T15:05:21ZDeep learning-based anomaly-onset aware remaining useful life estimation of bearings10.7717/peerj-cs.7952376-5992https://doaj.org/article/352093102f5a464598971be0a57f886a2021-11-01T00:00:00Zhttps://peerj.com/articles/cs-795.pdfhttps://peerj.com/articles/cs-795/https://doaj.org/toc/2376-5992Remaining Useful Life (RUL) estimation of rotating machinery based on their degradation data is vital for machine supervisors. Deep learning models are effective and popular methods for forecasting when rotating machinery such as bearings may malfunction and ultimately break down. During healthy functioning of the machinery, however, RUL is ill-defined. To address this issue, this study recommends using anomaly monitoring during both RUL estimator training and operation. Essential time-domain data is extracted from the raw bearing vibration data, and deep learning models are used to detect the onset of the anomaly. This further acts as a trigger for data-driven RUL estimation. The study employs an unsupervised clustering approach for anomaly trend analysis and a semi-supervised method for anomaly detection and RUL estimation. The novel combined deep learning-based anomaly-onset aware RUL estimation framework showed enhanced results on the benchmarked PRONOSTIA bearings dataset under non-varying operating conditions. The framework consisting of Autoencoder and Long Short Term Memory variants achieved an accuracy of over 90% in anomaly detection and RUL prediction. In the future, the framework can be deployed under varying operational situations using the transfer learning approach.Pooja Vinayak KamatRekha SugandhiSatish KumarPeerJ Inc.articleDeep learningPredictive maintenanceAnomaly detectionRemaining useful lifeBearingLSTMElectronic computers. Computer scienceQA75.5-76.95ENPeerJ Computer Science, Vol 7, p e795 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Deep learning Predictive maintenance Anomaly detection Remaining useful life Bearing LSTM Electronic computers. Computer science QA75.5-76.95
spellingShingle	Deep learning Predictive maintenance Anomaly detection Remaining useful life Bearing LSTM Electronic computers. Computer science QA75.5-76.95 Pooja Vinayak Kamat Rekha Sugandhi Satish Kumar Deep learning-based anomaly-onset aware remaining useful life estimation of bearings
description	Remaining Useful Life (RUL) estimation of rotating machinery based on their degradation data is vital for machine supervisors. Deep learning models are effective and popular methods for forecasting when rotating machinery such as bearings may malfunction and ultimately break down. During healthy functioning of the machinery, however, RUL is ill-defined. To address this issue, this study recommends using anomaly monitoring during both RUL estimator training and operation. Essential time-domain data is extracted from the raw bearing vibration data, and deep learning models are used to detect the onset of the anomaly. This further acts as a trigger for data-driven RUL estimation. The study employs an unsupervised clustering approach for anomaly trend analysis and a semi-supervised method for anomaly detection and RUL estimation. The novel combined deep learning-based anomaly-onset aware RUL estimation framework showed enhanced results on the benchmarked PRONOSTIA bearings dataset under non-varying operating conditions. The framework consisting of Autoencoder and Long Short Term Memory variants achieved an accuracy of over 90% in anomaly detection and RUL prediction. In the future, the framework can be deployed under varying operational situations using the transfer learning approach.
format	article
author	Pooja Vinayak Kamat Rekha Sugandhi Satish Kumar
author_facet	Pooja Vinayak Kamat Rekha Sugandhi Satish Kumar
author_sort	Pooja Vinayak Kamat
title	Deep learning-based anomaly-onset aware remaining useful life estimation of bearings
title_short	Deep learning-based anomaly-onset aware remaining useful life estimation of bearings
title_full	Deep learning-based anomaly-onset aware remaining useful life estimation of bearings
title_fullStr	Deep learning-based anomaly-onset aware remaining useful life estimation of bearings
title_full_unstemmed	Deep learning-based anomaly-onset aware remaining useful life estimation of bearings
title_sort	deep learning-based anomaly-onset aware remaining useful life estimation of bearings
publisher	PeerJ Inc.
publishDate	2021
url	https://doaj.org/article/352093102f5a464598971be0a57f886a
work_keys_str_mv	AT poojavinayakkamat deeplearningbasedanomalyonsetawareremainingusefullifeestimationofbearings AT rekhasugandhi deeplearningbasedanomalyonsetawareremainingusefullifeestimationofbearings AT satishkumar deeplearningbasedanomalyonsetawareremainingusefullifeestimationofbearings
_version_	1718407825770676224

Deep learning-based anomaly-onset aware remaining useful life estimation of bearings

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