A hybrid reasoning architecture for fleet vehicle maintenance

This article has described a novel approach for integrated diagnosis/prognosis of systems. The suggested architecture enables encoding of analytical techniques from a system´s point of view and its expansion for prognosis tasks under the same structure. The performance of such a knowledge-based system depends on the degree of completeness of its enables encoding of analytical techniques from a system´s point of view and its expansion for prognosis tasks under the same structure. The performance of such a knowledge-based system depends on the degree of completeness of its knowledge base. Since the system can interact with multiple vehicles, it learns about several operating environments, resulting in a rich accumulation of experiences in relatively very short time. At the same time, it also serves multiple systems. A natural language processing technique has been developed to extract information from the textual descriptions that is less computationally expensive than the usual NLP techniques and still preserves the meaning of the text. The experimental test data are currently being gathered for the experiments from the domain of automobiles to demonstrate the capability of the system