Name
Abstract | ||
|---|---|---|
Cognitive fault detection and diagnosis systems are systems able to provide timely information about possibly occurring faults without requiring any a priori knowledge about the process generating the data or the possible faults. This ability is crucial in sensor network scenarios where a priori information about the data generating process, the noise level or the dictionary of the possibly occurring faults is generally hard to obtain. We here present a novel cognitive fault detection and isolation system for sensor networks. The proposed solution relies on the modeling of spatial and temporal relationships present in the acquired datastreams and an ensemble of Hidden Markov Model change-detection tests working in the space of estimated parameters for fault detection and isolation purposes. The effectiveness of the proposed solution has been evaluated on both synthetically generated and real datasets. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1142/S0129065716500477 | INTERNATIONAL JOURNAL OF NEURAL SYSTEMS |
Keywords | Field | DocType |
Cognitive fault detection and diagnosis, hidden Markov model change detection test, ensemble model | Data mining,Ensemble forecasting,Computer science,Fault detection and isolation,A priori and a posteriori,Noise level,Artificial intelligence,Hidden Markov model,Cognition,Wireless sensor network,Machine learning | Journal |
Volume | Issue | ISSN |
27 | 3 | 0129-0657 |
Citations | PageRank | References |
5 | 0.40 | 17 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manuel Roveri | 1 | 272 | 30.19 |
| Francesco Trovò | 2 | 43 | 7.58 |