KPCA在凿岩台车控制系统传感器故障诊断中的应用

传感器状态对于凿岩台车的作业有着极其重要的影响,对其展开故障诊断十分必要.核主成分分析(KPCA)方法通过集成算子与非线性核函数计算高维特征空间的主元成分,有效捕捉过程变量中的非线性关系,将其用于传感器4种常见故障的诊断,先用Q统计量进行故障监测,再用T2贡献量百分比变化来识别故障.仿真和实际应用结果表明:KPCA方法具有很好的故障监测与诊断能力.

Application of KPCA in the fault detection and diagnosis for the sensor of the rock drilling jumbo control system

The Fault detection and diagnosis for sensors is important for the performance of the rock drilling jumbo seriously. The kernel principal component analysis (KPCA) effectively captures the nonlinear relationship of the process variables, which computes principal component in high dimensional feature space by means of integral operators and nonlinear kernel functions. The KPCA method was used in diagnosing for four common sensor faults. At first its fault was detected by Q statistic, secondly its fault was identified by T2 contribution percent change. The simulation and the practical result shows the KPCA method has good performance for complex control system in sensor fault detection and diagnosis.