基于偏最小二乘回归的挖掘机液压系统故障诊断

Fault diagnosis of excavator hydraulic system based on partial least squares regression

为了提高挖掘机液压系统的可靠性水平,提出了一种针对挖掘机液压系统的偏最小二乘回归(PLSR)故障诊断方法,其原理是:首先使用非线性迭代偏最小二乘(NIPALS)算法分析系统正常状态下的样本,选择累积方差最大的PLS成分数目,建立输出变量关于输入变量间的PLSR辨识模型;然后,将系统故障状态下的样本代入PLSR辨识模型,运用广义似然比(GLR)检验对模型残差进行假设检验,判断系统的故障状态。实验结果表明,采用基于PLSR的故障诊断方法能准确地诊断出所有系统故障,能有效地应用于挖掘机液压系统的故障诊断。

In order to improve the reliability of the excavator＇s hydraulic system, a fault diagnosis approach based on partial least squares regression （PLSR） was proposed. The principal of the method was as follows： firstly, nonlinear iterative partial least squares （NIPALS） algorithm was applied to compute components and the optimal number of components was determined by the total variance explained. As a result, an input-output PLSR model was established. Secondly, generalized likelihood ratio （GLR） test performed a hypothesis test for model residual so as to identify the system faults. The experimental results show that all the test faults are correctly identified, and it can be used in the fault diagnosis.