Development of a fault diagnosis system based on fuzzified neural networks

In circulatory systems or systems like chemical plants, failure of piping, sensors or valves 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 recorded by the sensor system which has already existed. Reliability grades are beforehand given to the recorded data by domain experts. The state of a target system is determined based on the fuzzy output value from the trained fuzzified neural network. Our proposed technique makes us determine easily the state of the target systems. Our proposed technique is flexibly applicable to various types of systems by considering some parameters for failure determination of target systems.