CAJ | 학술논문

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。
전감기상태대우착암태차적작업유착겁기중요적영향，대기전개고장진단십분필요。핵주성분분석（KPCA）방법통과집성산자여비선성핵함수계산고유특정공간적주원성분，유효포착과정변량중적비선성관계，장기용우전감기4충상견고장적진단，선용Q통계량진행고장감측，재용T2공헌량백분비변화래식별고장。방진화실제응용결과표명：KPCA방법구유흔호적고장감측여진단능력。
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.