基于递推主元分析法的汽车衡称重 传感器零点故障检测方法

汽车衡称重传感器零点故障是一种典型的微小故障,不易在线检测,利用递推主元分析(RPCA)与四类故障检测指标相结合的方法,提出一种汽车衡称重传感器零点故障在线检测方法。该方法首先利用基于秩1修正的主元递推算法在线更新主元模型,然后利用Hotelling’s T~2统计量、平方预测误差(SPE)统计量、Hawkins T_H~2统计量、主元相关变量残差(PVR)统计量及其控制限构建故障综合评判方法,最终完成称重传感器零点故障及微小故障在线检测。实验表明,采用这种基于递推主元分析和综合评判方法的称重传感器,零点故障检测准确率比传统方法(即仅采用T~2、T_H~2、SPE、PVR任何一类统计量进行判别),提高了一个数量级,证实了该方法的有效性。

Zero-point fault detection of load cells in truck scale based on recursive principal component analysis

A zero point fault of load cells in truck scale is a typical minor fault and it is difficult to be detected online. A method for detecting zero point fault online is proposed by combining a recursive principal component analysis (RPCA) with four types of fault detection indicators. In this method, firstly, the principal component model is updated online by the principal recursive algorithm based on rank 1 modification, and then the four statistics, i.e., the Hotelling''s T2 statistic, the squared prediction error (SPE) statistic, the Hawkins TH2 statistic, and the principal component related variable residual (PVR) statistic, are used to construct a comprehensive evaluation method for fault detection. This proposed method for fault detection online is applied to load cells in truck scale, and the experimental results show that the accuracy of zero point fault detection is increased with an order of magnitude by the traditional method, which proves the effectiveness of this proposed method.