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Fault detection of excavator’s hydraulic system based on dynamic principal component analysis 总被引:1,自引:0,他引:1
In order to improve reliability of the excavator's hydraulic system, a fault detection approach based on dynamic principal component analysis(PCA) was proposed. Dynamic PCA is an extension of PCA, which can effectively extract the dynamic relations among process variables. With this approach, normal samples were used as training data to develop a dynamic PCA model in the first step. Secondly, the dynamic PCA model decomposed the testing data into projections to the principal component subspace(PCS) and residual subspace(RS). Thirdly, T2 statistic and Q statistic performed as indexes of fault detection in PCS and RS, respectively. Several simulated faults were introduced to validate the approach. The results show that the dynamic PCA model developed is able to detect overall faults by using T2 statistic and Q statistic. By simulation analysis, the proposed approach achieves an accuracy of 95% for 20 test sample sets, which shows that the fault detection approach can be effectively applied to the excavator's hydraulic system. 相似文献
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基于动态GRNN模型的挖掘机液压系统故障检测 总被引:1,自引:0,他引:1
提出了一种针对工程机械液压系统的动态广义回归神经网络(GRNN)模型的故障诊断方法.动态GRNN模型是一种全局递归的动态模型,具有很强非线性收敛能力.首先建立系统正常状态故障建立动态GRNN模型;计算动态GRNN模型的检测阈值;然后将测试故障样本带入动态GRNN模型当中,其残差平方和在对应阈值范围内即可判定故障.通过实验分析,基于动态GRNN模型的故障检测方法准确地诊断出了90%以上的系统故障,实验结果表明,这一方法能够有效地应用于挖掘机液压系统的故障诊断. 相似文献
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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。 相似文献
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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。 相似文献
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