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| Firearm Fault Diagnosis Based on RBR and CBR Hybrid Inference Methods |
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| DOI：10.7643/issn.1672-9242.2022.06.005 |
| KeyWord:firearm fault diagnosis RBR CBR RETE matter-model expert system |
| Author | Institution |
| LI Yuan-yuan |
Nanjing University of Science and Technology, Nanjing , China |
| WANG Ya-ping |
Nanjing University of Science and Technology, Nanjing , China |
| WANG Jia-hao |
Nanjing University of Science and Technology, Nanjing , China |
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| Abstract: |
| In order to improve the efficiency and accuracy of firearm fault diagnosis, the RBR-CBR hybrid inference model is established for such problems as difficult manual site elimination, low accuracy of single diagnostic method and complex operation process, achieving rapid and accurate diagnosis of gun faults. Aiming at the characteristics of complex causal relationship, large randomness and fast iterative update of firearm faults, a reasoning mode with RBR as the core and CBR as the supplement is proposed. RBR inference is carried out by RETE algorithm to improve the efficiency of rule matching; the matter-element model of gun failure examples is established by extended method; the local similarity of different combinations of numerical and interval types is accurately characterized; and the similarity of examples is analyzed based on the nearest neighboring method. Based on the hybrid inference methods of RBR and CBR, an expert system for firearm fault diagnosis is developed. This expert system effectively guides maintenance support personnel in firearm troubleshooting. The hybrid inference method is used to improve the accuracy of fault diagnosis under the premise of ensuring the speed of firearm fault diagnosis, and the effectiveness of the RBR and CBR-based hybrid reasoning method and the high efficiency of this expert system.are verified. |
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