A Two-Stage Data-Driven-Based Prognostic Approach for Bearing Degradation Problem

By Yu Wang, Yizhen Peng, Yanyang Zi, Xiaohang Jin, Kwok Leung Tsui. IEEE Transactions on Industrial Informatics, 2016

312 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/09382310f28088fb8fad59336c084baa10c16340

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yaguo Lei
2. Biao Wang
3. Naipeng Li
4. Liang Guo
5. Dong Wang
6. Ningbo Li
7. Tao Yan
8. Kwok Leung Tsui

Citations in the corpus

• A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings (2020, methodology): “Then, statistical estimation techniques, such as Kalman filtering (KF) [20], [21], extended KF (EKF)”
• Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators (2018, methodology): “[127] extracted 14 time-domain features and then used the Mahalanobis distance calculated from healthy bearing data to fuse these time-domain features to form a bearing health indicator.”
• Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, methodology): “[71] fused multiple time-domain features by calculating their Mahalanobis distance from the healthy stage.”