Conventional principal component analysis (PCA) can obtain low-dimensional representations of original data space

but the selection of principal components (PCs) based on variance is subjective

which may lead to information loss and poormonitoring performance. To address dimension reduction and information preservation simultaneously

this paper proposes a novel PC selection scheme named full variable expression.On the basis of the proposed relevance of variables with each principal component

key principal components can be determined. All the key principal components serve as a low-dimensional representation of the entire original variables

preserving the information of original data spacewithout information loss.A squaredMahalanobis distance

which is introduced as themonitoring statistic

is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method

a numerical example and the Tennessee Eastman benchmark are used.