采用神经网络技术降低机电设备BIT虚警

机电设备 Ｂ Ｉ Ｔ 的突出问题是虚警率高，重要原因之一是 Ｂ Ｉ Ｔ 系统传感器通路故障。本文选取神经网络技术进行传感器通路故障诊断，剖析某大型船舶动力装置机电设备 Ｂ Ｉ Ｔ 系统中传感器通路的故障机理和类型，得到其故障样本数据，经过神经网络学习训练后对实际系统进行故障诊断和识别，实验结果表明该方法简洁、有效，能够有效地诊断故障并识别出故障类型，具有实用价值。

Decreasing False Alarm of Mechantronics Equipment Built-in Test Based on Neural Network

The significant problem of mechantronics equipment built in test (MEBIT) is its high false alarm rate. One of the important causes is its sensor channel's fault of BIT system. The method of neural network is adopted for the sensor channel fault diagnosis. The fault mechanism and types of sensor channels of MEBIT in a large ship power engine and the fault samples are obtained. After the training of neural network, the actual system's faults are diagnosed and identified. The experimental results show that the neural network can diagnose the faults and identify their types. The method is compact, effective and of practical value.