Intelligent Built-in Test Design of Controller Module By Improved Biologically Inspired Neural Network

Zhen Xie, G. Hou, Jian-hang Zhang, Congzhi Huang
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Abstract

Built-in test (BIT) technology is widely employed in heavy-duty gas turbine control systems for fault recognition. However, it is difficult to obtain an excellent fault diagnostic ability by using the conventional BIT technology, and the false alarm rate is high. In this paper, a design of intelligent BIT based on improved biologically inspired neural network (BINN) is proposed to reduce false alarm. Firstly, massive historical measurement data of controller module is collected and used as training dataset and test dataset. Secondly, intelligent BIT based on improved BINN is designed to deal with the issue of module state identification and reduce false alarm rate. Finally, the effectiveness of proposed approach is validated by the given extensive numerical simulation results and experimental results.
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基于改进生物启发神经网络的控制器模块智能内置测试设计
嵌入式测试(BIT)技术广泛应用于重型燃气轮机控制系统的故障识别。然而,传统的BIT技术难以获得良好的故障诊断能力,且虚警率高。本文提出了一种基于改进生物启发神经网络(BINN)的智能BIT的设计,以减少误报。首先,收集控制器模块的大量历史测量数据,作为训练数据集和测试数据集;其次,设计了基于改进BINN的智能BIT,解决模块状态识别问题,降低虚警率;最后,通过大量的数值模拟结果和实验结果验证了所提方法的有效性。
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