Conventional principal component analysis (PCA) selects several Principal Components (PCs) with most variance information of normal samples in process monitoring. Because fault information may not be necessarily mapped into high variance components

PC selection by variance contribution often leads to serious information loss and poor monitoring performance. ReliefF-PCA algorithm was proposed to select PCs of higher weighted and more effective components in distinguishing normal and fault conditions. The PCs selected in this way avoided subjectivity

blindness

and critical information loss as happened in conventional PCA. ReliefF-PCA algorithm in process monitoring had two advantages of better monitoring performance and more effective dimension reduction of original data. Subsequently

a weighted contribution plot of fault variables was proposed. The result of Tennessee Eastman (TE) simulation study showed that the ReliefF-PCA algorithm achieves desired outcomes.