红外特征提取与关联分析的设备故障预警方法

Infrared feature extraction and correlation analysis for early warning of equipment faults

水电站电气设备的安全稳定运行至关重要。为了实现设备故障的自动预警,通过红外特征提取并进行灰色关联分析,提出建立主成分分析(PCA)和基于密度的聚类算法(DBSCAN)的设备故障预警模型。首先,通过数据预处理补全缺失数据,剔除异常数据后进行主成分分析降维并提取新的主成分特征。其次,将新的主成分采用DBSCAN算法构建特征样本集,建立灰色关联模型,计算灰色关联系数,然后,通过灰色关联系数的变化程度突变点进行故障预警。实验结果表明,所提方法能有效提取红外特征,并在设备异常状态下实现设备故障预警,故障预警准确率达到97.88%。

The safe and stable operation of electrical equipment in hydropower plants is critical.In order to achieve the automatic early warning of equipment faults,through infrared features extraction and gray-associated analysis,it is proposed to establish an early warning model of equipment failure with principal component analysis(PCA)and density-based clustering algorithm(DBSCAN).Firstly,the missing data are made up through the data pre-processing,the abnormal data are eliminated,and the principal component analysis is performed to reduce the dimensionality and extract the new principal component features.Secondly,the new principal components are used to construct the feature sample set by DBSCAN algorithm,establish a gray association model,calculate the gray associated coefficient,and then fail to warn the degree of change point of the gray association coefficient carry out the fault early warning through the change degree of the grey correlation coefficient of the mutation points.The experimental results show that the proposed method can effectively extract infrared characteristics and achieve equipment fault warning under the abnormal state of the equipment,and the fault warning accuracy rate reaches 97.88%.