基于模糊数学和RBF神经网络的事故预测

对于能源和化工装置，在其运行过程中根据有关变量的发展趋势，预测事故，有着极其重要的意义。人们对此开展了广泛的研究，特别在应用智能化软件解决此类问题方面做了较多的工作，并取得了一系列的成果。

Failure Prediction Based on Both Fuzzy Mathematics and a Radial Basis Function (RBF) Neural Network

It is of crucial importance to have the ability to predict incipient and potential failures of a power plant or chemical engineering process unit during its operation by tracing the development trend of relevant variables. A comprehensive research has been performed in this regard, and a series of promising results have been attained, especially regarding the application of intelligent software for coping with the relevant issues. However, all the traditional methods are mostly based on the analysis of failure modes. In this paper proposed is a fuzzy mathematics aided method with the use of RBF neural network method to identify failure symptoms. Satisfactory results have been obtained when the proposed method was used to predict the failure of a coal fired boiler.