基于有源自回归模型与模糊C-均值聚类的挖掘机液压系统故障诊断

为了提高挖掘机的可靠性水平和智能化程度,提出了基于有源自回归(ARX)模型与模糊C-均值(FCM)聚类的挖掘机液压系统故障诊断方法。该方法将故障诊断分成故障特征提取和故障分类两个部分。在故障特征提取中,针对已知故障和测试故障分别建立ARX模型,提取ARX模型的自回归系数作为故障特征。在故障分类中,以FCM聚类作为故障分类器,将测试故障归入已知故障的某个分类中,判断系统的故障类型。仿真和实验结果表明,ARX模型与FCM聚类相结合的故障诊断方法能有效地应用于挖掘机液压系统。

Fault diagnosis for excavator hydraulic system based on auto-regressive with extra inputs model and fuzzy C-means clustering

In order to improve the reliability and intelligence of the excavator, a fault diagnosis approach of its hydraulic system based on an auto-regressive with extra inputs (ARX) model and the fuzzy C-means (FCM) clustering was proposed. This approach does fault diagnosis by two steps: fault feature extraction and fault classification. In the first step, the ARX models are established for known faults and unknown faults. The auto-regressive parameters extracted from the model are regarded as the fault feature. In the second step, the FCM clustering is used as the fault feature classifier to identify unknown faults. Both the simulation and experiment results showed that the proposed fault diagnosis approach can be effectively applied to the excavator hydraulic system.