Name
Title | ||
|---|---|---|
Auto-adaptive and Dynamical Clustering for Double Open-Circuit Fault Diagnosis of Power Inverters |
Abstract | ||
|---|---|---|
This paper extends the authors' previous work on a diagnosis algorithm for single open-circuit faults in inverters to the double fault case. The proposed approach is entirely data-driven, using measurements of the stator currents. The first contribution of the present work resides in new choices for the feature variables which allow us to detect double open-circuit faults. These feature data are clustered to different classes corresponding to faulty modes using Auto-adaptive and Dynamical Clustering (AUDyC). These classes are then labelled appropriately which allows us to determine conditions for the fault detection and isolation. Moreover, the values of the algorithm parameters and their influence on the detection time are presented. The proposed improvements are validated on simulation data. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SYSTOL.2019.8864777 | 2019 4th Conference on Control and Fault Tolerant Systems (SysTol) |
Keywords | Field | DocType |
fault detection,simulation data,double open-circuit fault diagnosis,power inverters,diagnosis algorithm,single open-circuit faults,double fault case,stator currents,feature data,auto-adaptive and dynamical clustering,AUDyC,faulty modes,fault isolation | Fault detection and isolation,Computer science,Algorithm,Feature extraction,Stator,Cluster analysis,Double fault,Feature data | Conference |
ISSN | ISBN | Citations |
2162-1195 | 978-1-7281-0381-5 | 0 |
PageRank | References | Authors |
0.34 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thanh Hung Pham | 1 | 0 | 0.34 |
| Sanda Lefteriu | 2 | 0 | 0.34 |
| Eric Duviella | 3 | 23 | 11.69 |
| Stéphane Lecoeuche | 4 | 6 | 1.46 |