The traditional robust outlier removing algorithm can not obtain the accurate mean and standard deviation of the sample data. Thus it can decrease the ability of processing the  fault diagnosis in the statistical monitoring model based on PCA.An outlier detection algorithm which combines CDCm(Closest Distance to Center, Maximum Variable Distance) and MVT (Ellipsoidal Multivariate Trimming) is proposed. It can overcome the above limitations，utilizing a modified scale to obtain the mean and standard deviation of the processing data,and can carry out the centering and standardization of it.Then the normal data of observations and the closest distance to the center are extracted from the modeling database by the CDCm algorithm of maximum variable distance.Using it,the first mahalanobis distance of MVT is obtained. The other normal data is gotten by the iterative calculation of the mahalanobis distance. A method is applied to detecting outliers from a fermentation process and comparing with the traditional robust outlier detection algorithms. The analysis and experimental results show that it can improve the outlier detecting efficiency and accuracy.