Name
Title | ||
|---|---|---|
Data-driven multi-unit monitoring scheme with hierarchical fault detection and diagnosis |
Abstract | ||
|---|---|---|
Various areas see the trend of increasing degree of decentralization and automation. Conventional component or unit based monitoring scheme by analytical modeling could be tedious and costly in dealing with increased number of entities. A holistic data-driven scheme able to monitor multiple units and hierarchically detect and diagnose faults is proposed, in which Multi-way Principal Component Analysis (MPCA) is employed as data analysis algorithm. The proposed scheme is illustrated using data collected from an actual onshore wind farm with multiple wind turbines. The obtained fault detection and diagnosis results are validated using maintenance reports. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/MED.2016.7535852 | 2016 24th Mediterranean Conference on Control and Automation (MED) |
Keywords | Field | DocType |
multiway principal component analysis,data analysis algorithm,onshore wind farm,multiple wind turbines,maintenance reports,data-driven multiunit monitoring scheme,hierarchical fault detection,hierarchical fault diagnosis,MPCA | Data-driven,Computer science,Fault detection and isolation,Automation,Real-time computing,Wind power,Principal component analysis | Conference |
ISSN | ISBN | Citations |
2325-369X | 978-1-4673-8347-9 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yingya Zhou | 1 | 0 | 0.34 |
| Moncef Chioua | 2 | 2 | 2.10 |
| Weidou Ni | 3 | 9 | 3.04 |