Deep learning-based anomaly-onset aware remaining useful life estimation of bearings
| Autor: | Satish Kumar, Pooja Kamat, Rekha Sugandhi |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Computer science Remaining useful life Data Mining and Machine Learning Anomaly detection Predictive maintenance law.invention Artificial Intelligence law Cluster analysis K-means Bearing (mechanical) business.industry Deep learning Data Science k-means clustering Pattern recognition Autoencoder QA75.5-76.95 Algorithms and Analysis of Algorithms Electronic computers. Computer science Bearing Artificial intelligence Anomaly (physics) LSTM business clustering |
| Zdroj: | PeerJ Computer Science, Vol 7, p e795 (2021) PeerJ Computer Science |
| ISSN: | 2376-5992 |
| DOI: | 10.7717/peerj-cs.795 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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