Application of case-based reasoning in a fault detection system on production of drippers

Abstract

Injection molding has been a preferred production process in the fabrication of complex components. In this technique not only the injection machine and mold play important roles, but also different process parameters have strong effects on the quality of the final products. The production process might be stopped because of different types of faults on the production line. In this paper, a case-based reasoning (CBR) methodology is employed to implement an intelligent fault detection system for the production of injection molded drippers. This CBR system utilizes similar occurred faults to solve particular new problems. Case retrieval and similarity measurements are defined based on fault occurrence weight of features (fault’s causes). Application and accuracy of the proposed system are experimentally tested and validated through analyzing the current case study. The obtained results indicated that the implemented CBR system is able to detect the faults on the injection molding machine. By utilizing the proposed system machine downtime is reduced, speeded production with high productivity is achieved.