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首页> 外文期刊>Journal of Advanced Computatioanl Intelligence and Intelligent Informatics >Fuzzy Nonlinear Regression Analysis Using Fuzzified Neural Networks for Fault Diagnosis of Chemical Plants
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Fuzzy Nonlinear Regression Analysis Using Fuzzified Neural Networks for Fault Diagnosis of Chemical Plants

机译：基于模糊神经网络的模糊非线性回归分析在化工厂故障诊断中的应用

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摘要

In systems such as chemical plants or circulatory systems, failure of piping, sensors or valves causes serious problems. These failures can be avoided by the increase in sensors and operators for condition monitoring. However, since adding sensors and operators leads to an increase in cost, it is difficult 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 that is trained with time-series data with reliability grades. Our proposed technique uses numerical data recorded by the existing monitoring system. 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.
机译：在化工厂或循环系统等系统中，管道，传感器或阀门的故障会引起严重的问题。通过增加用于状态监测的传感器和操作员可以避免这些故障。但是，由于增加传感器和操作员导致成本增加，因此难以实现。本文提出了一种基于模糊非线性回归的目标系统诊断技术，该方法采用了模糊神经网络，该神经网络由具有可靠性等级的时间序列数据训练而成。我们提出的技术使用现有监控系统记录的数值数据。领域专家会预先给记录的数据提供可靠性等级。基于来自训练后的模糊神经网络的模糊输出，确定目标系统的状态。我们提出的技术使我们能够轻松确定目标系统的状态。通过考虑一些用于确定目标系统故障的参数，我们提出的技术可灵活应用于各种类型的系统。

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