A Data-Driven Approach for Bearing Fault Prognostics
| Autor: | Xiaohang Jin, Yuanjing Guo, Yi Sun, Zijun Que, Wei Qiao |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Computer science 0211 other engineering and technologies 02 engineering and technology Fault (power engineering) Maintenance engineering 01 natural sciences Industrial and Manufacturing Engineering Data-driven law.invention 020901 industrial engineering & automation law 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering 010301 acoustics Downtime 021103 operations research Bearing (mechanical) 020208 electrical & electronic engineering Kalman filter Reliability engineering Control and Systems Engineering Prognostics Anomaly detection Degradation (telecommunications) |
| Zdroj: | IAS |
| Popis: | Bearing is a critical component widely used in rotary machines. Bearing failure can cause damages of other components and lead to a lengthy downtime of the machine and costly maintenance. To reduce the cost and downtime for maintenance of the machines, it is desirable to perform fault prognostics to enable predictive health management for bearings. This paper proposes a new data-driven approach for bearing fault prognostics, which is based on the Kolmogorov–Smirnov test, self-organizing map, and unscented Kalman filter (UKF). The proposed approach has two steps. The first step is to detect bearing's degradation process by learning the historical data and the second step is to predict the remaining useful life (RUL) with the aid of a degradation model and the UKF. The proposed approach is validated by bearing's life data obtained from a run-to-failure experiment. Results show that the proposed approach can detect the bearing degradation process successfully and predict the RUL effectively. |
| Databáze: | OpenAIRE |
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