Intelligent Hybrid Fault Diagnostic System

Abstract

Recently, intelligent hybrid fault diagnostic systems are widely used to deal with the modern complex systems. It is found that, case based reasoning approach has proved its powerful as modern diagnostic technique. It can simplify the computation analysis required for diagnosis the complex systems. But, they still suffer from some limitations due to: (1) its inability to achieve a diagnosis for new faults that having no similar cases in its case library. (2) problems of storing the cases in the library and its effect on the complexity of the retrieving and adaptation processes. The proposed research introduces a new intelligent hybrid system that integrates the case based reasoning, neural network, and the genetic algorithm approaches to improve the performance of the CBR diagnostic systems. It incorporates the neural network into the case based system to diagnose the new faults. While, it uses the genetic algorithm to optimize the topology of the neural network and to select the optimum cases to be stored in the case library to manage its size. Therefore, the proposed hybrid diagnostic system can increase the accuracy, decrease the time and simplify the complexity of the retrieving and adaptation processes of CBR systems. The suggested system has applied for diagnosis the faults of a Wireless Network. This network communicates between a robot arm used for cleaning the solar cell panels of the complex PV systems, and its control centre as a case of study. The obtained results have proved good performance of the proposed hybrid diagnostic system in the practical sites. KeywordsCase-based reasoning, genetic algorithm, neural network, fault diagnosis, and PV System.