基于智能感知技术的电厂设备状态监测方法

Monitoring Method for the State of Power Plant Equipment Based on Smart Perceptive Technology

针对滚动轴承复合故障振动信号的非平稳、非线性特性且不同类型故障间相互耦合的特性,提出一种以智能传感器技术、物联网技术为基础的电厂转动设备状态监测方法。用智能振动传感器采集设备轴承的振动信号,对采集到的振动信号进行数据分析,利用深度学习中的一维卷积神经网络(1DCNN)对滚动轴承进行故障诊断。实验结果表明,该故障诊断方法识别准确率高且鲁棒性强、通用性好。

Considering non-stationary and nonlinear features of rolling bearing’s compound fault vibration signals and that of the inter-coupling among various types of faults,a kind of monitoring method which has the smart vibration sensor technology and technology of Internet of things based to monitor rotating equipment was proposed,in which,the smart vibration sensor collects vibration signals of the bearing and then has them analyzed and finally has the 1D convolutional neural network(1D-CNN)in depth learning adopted to diagnose the faults in rolling bearings.The experimental results show that,this diagnosis method has high accuracy,which also has strong robustness and good versatility.