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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PeerJ Inc.
2021
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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) |
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Deep learning Predictive maintenance Anomaly detection Remaining useful life Bearing LSTM Electronic computers. Computer science QA75.5-76.95 |
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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 |