传感器故障检测的Powell神经网络方法

Sensor Failure Detection Based on a Powell Neural Network Method

大型热力控制系统必须能够检测传感器故障 ,并采取相应的措施 ,保证控制过程的顺利进行。提出了一种基于Powell神经网络的故障检测新方法 ,为系统中每一个传感器构造一个神经网络观测器 ,首先离线训练神经网络观测器 ,然后进行故障检测并同时在线训练 ,利用神经网络估计输出代替故障传感器输出 ,保证系统的稳定。神经网络的训练采用Powell方法 ,该训练方法的收敛速度快、过程稳定。本方法具有在线学习、诊断多个传感器故障等优点。锅炉的实际试验结果表明 ,此方法行之有效。

It is essential for the control system of a large sized thermodynamic system to detect sensor failures and then take pertinent measures to ensure the successful implementation of the control process. The authors have come up with a new type of failure detection method based on Powell neural network. Under this method a neural network observer is set up for each sensor of the thermodynamic system, which at first received an off line training. On this basis, failure detection and on line training were conducted simultaneously. The neural network training by the use of the Powell method features a rapid and stable training process. It has the merits of the ability to perform on line learning and diagnose the failure of a multiple of sensors. Actual tests on boilers show that the above method is highly effective.