谷氨酸发酵过程的扩展卡尔曼滤波和神经网络级联故障诊断策略

Wei Liu
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引用次数: 33

摘要

本文的目的是介绍在谷氨酸发酵过程故障诊断中所取得的结果。该诊断算法基于扩展卡尔曼滤波(EKF)和神经网络分类器。网络的输入是过程I/O数据,如压力、温度、EKF估计的参数、动态方程计算的状态值等,网络的输出是过程故障情况。以谷氨酸间歇发酵过程为例,进行了13项测量、5个估计参数、3种计算状态和11种故障情况的研究。运行测试结果表明,该策略更适合于此类工业过程的故障诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An extended Kalman filter and neural network cascade fault diagnosis strategy for the glutamic acid fermentation process

The purpose of this paper is to present results that were obtained in fault diagnosis of glutamic acid fermentation process. The diagnosis algorithm is based on the extended Kalman filter (EKF) and neural network classifier. Inputs of the network are the process I/O data, such as pressure and temperature, parameters estimated by EKF, and state values calculated by dynamic equations, while outputs of the network are process fault situations. A batch glutamic acid fermentation process is studied as a test case, which is with 13 measurements, five estimated parameters, three calculated states, and 11 fault situations. The running test results show that the strategy appears to be better suited to diagnose faults of such an industrial process.

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