支持张量机在柴油机故障预测中的应用研究

为了解决柴油机故障预测中大样本、非线性以及高维数据的数据预测问题,避免以向量输入带来的结构信息丢失和数据相关性被破坏等现象,结合支持向量机(SVM)的学习框架和交替投影的思想,研究基于在线支持张量机(OSTM)的柴油机故障预测算法和流程,并以测试精度、学习时间和均方根误差作为评价指标,利用远程监测系统采集的数据,分别应用在线支持向量机(OSVM)和OSTM进行故障预测和分析。结果表明,与OSVM方法相比,OSTM方法测试精度较高,学习时间大幅缩短,预测模型的收敛速度较快,能有效在线预测柴油机故障。

Prediction of diesel engine failure based on OSTM

To resolve the prediction problems with giant sample size, nonlinear and high dimensional data for diesel engines, this paper, aided by the framework of support vector machine (SVM) and the alternating projection method, studied the algorithm and process of diesel engine failure prediction on the basis of online support tensor machine (OSTM) to avoid loss of structural information and damage to data correlation resulting from vector input. Prediction accuracy, learning time and mean square error (MSE) were employed as evaluation indicators, and data collected by distant monitoring system were used in diesel engine failure prediction on the basis of OSVM and OSTM, respectively. The results show that compared with OSVM, OSTM is more accurate in failure prediction, boasting less learning time, higher convergence speed and greater efficiency.