Fault detection of excavator’s hydraulic system based on dynamic principal component analysis
来源期刊：中南大学学报(英文版)2008年第5期
论文作者：何清华 贺湘宇 朱建新
文章页码：700 - 700
Key words：hydraulic system; excavator; fault detection; principal component analysis; multivariate statistics
Abstract: 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.
基金信息：the National High-Tech Research and Development Program of China
