Advertisement
Published in:
2014 | OriginalPaper | Chapter
10. Fault Diagnosis Systems
Author : Cesare Alippi
Published in: Intelligence for Embedded Systems
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Abstract
Sensors and real apparatus are prone to faults that, in turn, affect the quality of retrieved data. Detection of faults or erroneous behaviors in sensor data stream must be anticipated to prevent drastic side effects (recall we make decisions out of incoming data). Cognitive fault diagnosis systems aim at detecting, identifying, and isolating the occurrence of faults without assuming that the process generating the data is known. It is shown that little can be done at the single sensor level unless strong hypotheses are made. However, the situation is different if the embedded system mounts a rich sensor platform or is inserted in a sensor network. In such a case, redundancy in the information content and functional dependencies among sensors can be exploited to classify a change as fault, a change in the environment or an inefficiency of the change detection method (model bias).