Anomaly Detection and Fault Prognosis for Bearings
| Autor: | Yi Sun, Tommy W. S. Chow, Xiaohang Jin, Zijun Que, Yu Wang |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Engineering
Bearing (mechanical) business.industry 020208 electrical & electronic engineering 02 engineering and technology Filter (signal processing) Kalman filter Fault (power engineering) law.invention Extended Kalman filter Autoregressive model law Control theory 0202 electrical engineering electronic engineering information engineering Prognostics 020201 artificial intelligence & image processing Anomaly detection Electrical and Electronic Engineering business Instrumentation |
| Zdroj: | IEEE Transactions on Instrumentation and Measurement. 65:2046-2054 |
| ISSN: | 1557-9662 0018-9456 |
| Popis: | In this paper, a new bearing anomaly detection and fault prognosis method is proposed. The method detects bearing anomalies and then predicts its remaining useful life (RUL). To achieve these two goals, an autoregressive model, which is used to filter out fault-unrelated signals, is derived according to healthy bearing vibrational signals. A health index is developed to indicate bearing health conditions. Anomalies of bearings are detected by choosing an appropriate threshold with the aid of a Box–Cox transformation. A nonlinear model is built to track the bearings’ degradation process and an extended Kalman filter is designed to model adaptation and RUL prediction. Finally, PRONOSTIA bearing data are used to demonstrate the effectiveness of the proposed method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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