Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
In systems like chemical plants or circulatory systems, failures of piping, monitoring sensors or valves for control causes serious problems. These failures can be prevented by the increase in sensors and operators for condition monitoring. However, since the increase in cost is required by adding sensors and operators, it is not easy to realize. In this paper, a technique of diagnosing target systems is proposed by using a fuzzified neural network which is trained with time-series data with reliability grades which are given beforehand by domain experts. Our proposed technique makes us determine easily the state of the target systems because the state of a target system is determined based on the fuzzy output from the trained fuzzified neural network. From results of computer simulations, our proposed technique is flexibly applicable to various types of systems by considering some parameters for failure determination of target systems.
