基于外磁场的离心泵故障预测模型

Fault prediction model of centrifugal pump basedon external magnetic field

针对传统离心泵故障诊断方法在复杂多变的工程环境中缺乏通用性和及时性的问题,提出了基于马氏距离改进KNN算法的离心泵故障预测模型.将故障工况下的外磁场信号进行处理并求得相应工况指标,通过ReliefF算法进行权重分析完成特征提取,进而为KNN算法提供故障预测分类的数据库.使用马氏距离替换KNN算法中原有的距离函数欧氏距离来消除特征指标间量纲影响,以提高预测结果的准确率.通过十折交叉验证法,筛选KNN算法的关键值K,得出在K取值为120时预测模型运行结果最优.根据外磁场信号,基于改进KNN算法建立的故障预测模型能够在离心泵偏工况运行时,对可能出现的故障能够进行准确预测,有效解决了传统监测方法滞后性严重的问题,且模型训练数据包含了0.2 Q_(d)~1.2 Q_(d)全流量下各类工况的外磁场信号.试验结果表明,故障预测模型在0.4 Q_(d),0.8 Q_(d),1.0 Q_(d),1.2 Q_(d)流量工况下,故障预测准确率达到0.8315,0.7999,0.8527,0.8741,基本实现了离心泵故障的准确预测.

Aiming at the lack of universality and timeliness of traditional fault malfunction diagnosis method for centrifugal pumps in the complex and changeable engineering environment,a fault malfunction prediction model of centrifugal pumps based on improved KNN algorithm by Mahalanobis distance was proposed.At first,the external magnetic field signals under fault malfunction condition were processed.Accordingly,the corresponding working condition indexes were obtained.Afterwards,the weight analysis was implemented by ReliefF algorithm to extract features,providing a database of malfunction prediction and classification for KNN algorithm.During the process,original Euclidean distance in KNN algorithm was replaced by Mahalanobis distance to eliminate the dimensional influence between feature indexes,so as to improve the accuracy of the prediction results.The key value K of KNN algorithm was sifted by the 10-fold cross validation method.Consequently,the operation result of the prediction model was proved to be the best when the value of K was 120.When the centrifugal pump was running off-design point,the malfunction prediction model established by the improved KNN algorithm could accurately predict the possible failures or faults according to the external magnetic field signals,effectively solving the serious hysteresis of traditional monitoring methods.The model training data contained the external magnetic field signals under various working conditions of 0.2 Q_(d)-1.2 Q_(d) full flow.The test results show that the malfunction prediction accuracy of the fault prediction model is 0.8315 at 0.4 Q_(d),0.7999 at 0.8 Q_(d),0.8527 at 1.0 Q_(d) and 0.8741 at 1.2 Q_(d) working condition,respectively,which basically realizes the accurate prediction of centrifugal pump malfunction.