Name
Abstract | ||
|---|---|---|
As an important part of artificial intelligence, expert systems have widely used in the field of mechanical fault diagnosis. But with the development of the large data and cloud computing, some systems' hardware scale have inflated, which makes the energy consumption become a problem to be solved in the expert system. However the traditional database system is hard to satisfy the semantics need of the knowledge repository management, and it spends a lot of time and energy to complete the data management and reasoning. For this reason, the paper presents an approach to construct the fault diagnosis system based on semantic networks, and focus on the research of semantic knowledge organization, management, inference mechanism and knowledge acquisition. In the experiments, we built the model of locomotive's diagnosis expert system. Compared with the relationship database, the proposed approach was more accurate and robust than other method. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IMIS.2016.76 | 2016 10th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS) |
Keywords | Field | DocType |
sematic network,locomotive running gear,fault diagnosis,expert system | Relational database,Inference,Computer security,Computer science,Expert system,Semantic network,Data management,Knowledge acquisition,Legal expert system,Cloud computing | Conference |
ISBN | Citations | PageRank |
978-1-5090-0985-5 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Tao | 1 | 0 | 0.34 |
| Yilun Liu | 2 | 34 | 3.07 |
| Yiping Wen | 3 | 25 | 8.59 |
| Jiahui Su | 4 | 0 | 0.34 |