Name
Abstract | ||
|---|---|---|
In this paper, we present a unified framework for sensor validation, which is an extremely important module in the engine health management system. Our approach consists of several key ideas. First, we applied nonlinear minor component analysis (NLMCA) to capture the analytical redundancy between sensors. The obtained NLMCA model is data driven, does not require faulty data, and only utilizes sensor measurements during normal operations. Second, practical fault detection and isolation indices based on Squared Weighted Residuals (SWR) are employed to detect and classify the sensor failures. The SWR yields more accurate and robust detection and isolation results as compared to the conventional Squared Prediction Error (SPE). Third, an accurate fault size estimation method based on reverse scanning of the residuals is proposed. Extensive simulations based on a nonlinear prototype non-augmented turbofan engine model have been performed to validate the excellent performance of our approach. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11760191_52 | ISNN (2) |
Keywords | Field | DocType |
conventional squared prediction error,sensor failure,accurate fault size estimation,swr yield,nlmca model,engine health management system,nonlinear minor component analysis,utilizes sensor measurement,squared weighted residuals,sensor validation,faulty data,fault detection and isolation,normal operator,prediction error,health management | Data modeling,Nonlinear system,Square (algebra),Computer science,Redundancy (engineering),Artificial intelligence,Artificial neural network,Data-driven,Pattern recognition,Simulation,Fault detection and isolation,Turbofan,Algorithm | Conference |
Volume | ISSN | ISBN |
3973 | 0302-9743 | 3-540-34482-9 |
Citations | PageRank | References |
1 | 0.41 | 4 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roger Xu | 1 | 111 | 14.71 |
| Guangfan Zhang | 2 | 39 | 4.64 |
| Xiaodong Zhang | 3 | 1 | 0.41 |
| Leonard Haynes | 4 | 26 | 3.40 |
| Chiman Kwan | 5 | 440 | 71.64 |
| Kenneth Semega | 6 | 1 | 0.41 |