fault recognition

Study on wear fault recognition of heavy machinery

重型机械设备磨损故障识别研究

Cable Fault Recognition Using Wavelet and Self-Organizing Neural Network

基于小波和自组织网络的电缆故障识别

To facilitate rapid reaction times, fault recognition is usually carried out by newly developed mechanisms.

为了方便快速识别错误, 故障识别通常采用新机制.

For another, this paper builds the feedback artificial neural network module of Elman, writes the program of fault recognition with Matlab, successfully fulfill the recognition of cable open circuit fault and short circuit fault.

紧接着，构建了反馈型Elman人工神经网络模型，利用Matlab语言编写了故障识别程序，成功实现了电缆的断线和短路故障的识别。

On one hand, in order to fur-ther improve the precision of the BP neural network, put the fuzzy theory int-o BP neural network, through the method of turning the input and output in-to the fuzzy feature vector to enhance the fault recognition accuracy.

一方面，为了进一步提高BP神经网络的精度，本文将模糊理论与BP神经网络相结合，通过将输入与输出的特征向量进行模糊化，提高故障识别的精度。

Through optimally selecting a group of virtual standard samples off-line, this algorithm can fulfill rapid and accurate on-line fault recognition, so it can effectively solve the problem of local convergence with FCM.

该方法通过离线优选虚拟标准样本，达到快速、准确在线识别故障的目的，很好地解决了FCM算法经常收敛到局部极值点的问题。