Fuzzy Nonlinear Regression Analysis using Fuzzified Neural Networks for Fault Diagnosis
In systems such as chemical plants or circulatory systems, 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 based on a fuzzy nonlinear regression is proposed by using a fuzzified neural network which is trained with time-series data with reliability grades. 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 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.
Component Fault Diagnosis Plant Operation Fuzzified Neural Networks Fuzzy Nonlinear Regression
Daisaku Kimua Manabu Nii Takafumi Yamaguchi Yutaka Takahashi Takayuki Yumoto
Electrical Engineering and Computer Sciences, Graduate School of Engineering, University of Hyogo 2167 Shosha, Himeji, Hyogo, Japan
国际会议
哈尔滨
英文
35-40
2010-08-01（万方平台首次上网日期，不代表论文的发表时间）