Abstract: Robust estimation is usually used for dealing with the outliers in the industry process data. A new robust estimation method is proposed for improving the Fast-MCD algorithm which has random starting value and artificial value of subsection. Fuzzy clustering is adopted to improve the computing efficiency in this method and the clustering center and clustering number are used to replace the starting value and subsection value. This method is implemented to analyze the temperature and conductivity data of sodium aluminate solution, and the simulation results show the proposed method can realize the identification of outliers. It also can reduce the unreasonable influence of outliers to soft sensing. Compared to Fast-MCD, it has the merits such as rapid convergence and high efficiency.