利用多传感器信号指标的风扇状态监测方法研究

Study on Fan Condition Monitoring Method Using Multi-sensor Signal Index

本文提出一种基于多传感器信号指标的风扇状态监测方法。首先使用多种常用的传感器对风扇进行拾取信号,采集不同状态下的多种传感器信号,并且计算出信号的指标;然后采用主成分分析法(PCA)对多传感器信号指标进行降维处理,提取可以表征风扇运行状态的主要成分指标;最后使用对非线性特征指标进行学习具有优势的循环神经网络(RNN)进行预测分析,实现对风扇状态的有效监测和故障识别,并通过实验验证了本文方法的有效性。

This paper presents a fan condition monitoring method based on multi-sensor signal indexes.Firstly,a variety of commonly used sensors are used to pick up the signals of the fan,collect the signals of various sensors in different states,and calculate the indicators of the signals.Secondly,the principal component analysis is used to reduce the dimension of multi-sensor signal indexes,simplify the state detection model and extract the main component indexes which can represent the running state of the fan.Finally,the RNN,which has the advantage of learning the nonlinear characteristic indexes,is used to predict and analyze the fan state,so as to realize the effective monitoring of the fan state.Experimental results show the effectiveness of the present method.