In the environment of industrial big data,due to some factors including high-dimensional,low value density and outlier points,it is difficult for the monitoring model to dig the key fluctuation information from mass data,which will lead to high false alarm rate and affect the product quality.To overcome this problem,a novel robust monitoring model in the high-dimensional process was proposed based on minimum covariance determinant estimation and variable selection algorithm.The minimum covariance determinant estimation(MCD) method was applied to estimate the robust mean vector and covariance matrix ;the likelihood ratio test statistic was constructed,the variable selection optimization function was obtained by adding a penalty term;robust monitoring statistics was obtained combining MCD and variable selection,the control limit for monitoring was obtained using Monte Carlo method;finally,the proposed method was empirically studied by simulation data and actual data of the film deposition process.The results show that compared with Hotelling T2 and VS control charts,the proposed method has high abnormal identification accuracy and robustness,the robustness of abnormal fluctuations identification is improved in high-dimensional process quality monitoring in the presence of outlier points,and the desired monitoring efficiency is achieved.