基于神经网络的传感器故障监测与诊断方法研究

Research on Sensor Failure Detection and Diagnosis Based on Neural Network

提出了一种基于神经网络的传感器故障监测与诊断的新方法 .该方法先用BP网络的预测输出和传感器实际输出之差来判断传感器是否发生了故障 ,然后用函数型连接神经网络模拟传感器的输出特性函数 ,通过计算神经元连接权值的变化 ,确定传感器哪个输出特性参数发生了变化 ,最终推断传感器发生了哪一类故障 .该方法的特点是只需要知道一个传感器的信息 .电阻应变式力传感器故障诊断实验结果证明了该方法的实用性 。

A new method for detecting and diagnosing a sensor failure is purposed. A sensor in failure is located through the comparison between the forecast output of back propagation (BP) neural network and the actual one of a sensor. The simulation of sensor output characteristic function with functional link neural network and the calculation of discrepancy of synaptic weight are employed to confirm the changes of an individual output characteristic parameter, and then classifies the failure. This new method needs the signal of one sensor only and has been proved to be unique and practical by the test result of strain sensor failure detection and diagnosis.