Name
Title | ||
|---|---|---|
Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory |
Abstract | ||
|---|---|---|
Engine diagnostics is a typical multi-sensor fusion problem. It involves the use of multi-sensor information such as vibration, sound, pressure and temperature, to detect and identify engine faults. From the viewpoint of evidence theory, information obtained from each sensor can be considered as a piece of evidence, and as such, multi-sensor based engine diagnosis can be viewed as a problem of evidence fusion. In this paper we investigate the use of Dempster-Shafer evidence theory as a tool for modeling and fusing multi-sensory pieces of evidence pertinent to engine quality. We present a preliminary review of Evidence Theory and explain how the multi-sensor engine diagnosis problem can be framed in the context of this theory, in terms of faults frame of discernment, mass functions and the rule for combining pieces of evidence. We introduce two new methods for enhancing the effectiveness of mass functions in modeling and combining pieces of evidence. Furthermore, we propose a rule for making rational decisions with respect to engine quality, and present a criterion to evaluate the performance of the proposed information fusion system. Finally, we report a case study to demonstrate the efficacy of this system in dealing with imprecise information cues and conflicts that may arise among the sensors. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.inffus.2005.07.003 | Information Fusion |
Keywords | Field | DocType |
engine quality,multi-sensor engine diagnosis problem,sensor fusion,information fusion,evidence theory,engine diagnosis,multi-sensor information fusion,mass function,pattern recognition,engine fault,imprecise information cue,engine fault diagnosis,evidence fusion,dempster-shafer evidence theory,engine diagnostics,fault detection and identification,dempster shafer | Data mining,Fault detection and identification,Sensor fusion,Artificial intelligence,Information fusion,Dempster–Shafer theory,Machine learning,Mathematics,Discernment | Journal |
Volume | Issue | ISSN |
8 | 4 | Information Fusion |
Citations | PageRank | References |
85 | 4.37 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Otman Basir | 1 | 435 | 31.33 |
| Xiaohong Yuan | 2 | 169 | 26.72 |