Anomaly Detection and Remaining Useful Life Prediction for Turbofan Engines with a Key Point-Based Approach to Secure Health Management
Yuntao Duan, Tao Zhang, Dunhuang Shi, Cong Wu, Jing Chen, Yebo Feng, Xianhao Chen · 2024
Read Anomaly Detection and Remaining Useful Life Prediction for Turbofan Engines with a Key Point-Based Approach to Secure Health Management by Yuntao Duan, Tao Zhang, Dunhuang Shi, Cong Wu, Jing Chen, Yebo Feng, Xianhao Chen (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11679743.txt.
Description
Yuntao Duan and colleagues propose a key-point method for watching turbofan engine health, in a paper from twenty twenty-four. Turbofans are complex, failure-prone machines where safety counts most. Safe running demands anomaly detection and life prediction. The method pulls two lifecycle key points from engine feature data.
Convolution steps, feature fusion, and derivative analysis produce those points. Counts-based analysis then builds health plans and remaining-life estimates around them. Test-set checks supported the approach. The authors argue key points simplify a messy watch problem. Engine sensor streams are long and tangled. Tying analysis to two key moments could focus upkeep decisions. They present the method as promising for secure engine health care.
Catalog record
| CREATOR | Yuntao Duan, Tao Zhang, Dunhuang Shi, Cong Wu, Jing Chen, Yebo Feng, Xianhao Chen |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 50 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |