Name
Abstract | ||
|---|---|---|
A typical assumption in supervised fault detection is that abundant historical data are available prior to model learning, where all types of faults have already been observed at least once. This assumption is likely to be violated in practical settings as new fault types can emerge over time. In this paper we study this often overlooked cold start learning problem in data-driven fault detection, ... |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TII.2012.2231870 | IEEE Transactions on Industrial Informatics |
Keywords | Field | DocType |
Fault detection,Data models,Predictive models,Support vector machines,Semisupervised learning,Principal component analysis,Monitoring | Data mining,Data-driven,Computer science,Fault detection and isolation,Unsupervised learning,Artificial intelligence,Cold start (automotive),Group method of data handling,Machine learning,Model learning | Journal |
Volume | Issue | ISSN |
9 | 4 | 1551-3203 |
Citations | PageRank | References |
10 | 0.69 | 16 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mihajlo Grbovic | 1 | 380 | 24.87 |
| Weichang Li | 2 | 100 | 6.58 |
| Niranjan A. Subrahmanya | 3 | 34 | 2.74 |
| Adam K. Usadi | 4 | 60 | 2.97 |
| Slobodan Vucetic | 5 | 637 | 56.38 |