Outlier detection is the core problem in data mining and is widely used in industrial production. Accurate and efficient outlier detection method can reflect the condition of industrial system in time, which provides reference for the relevant personnel. Traditional outlier detection algorithms can′t efficiently detect outliers in those data with complicated change modes, small change range and the characteristics of streaming data. In this paper a new method for detecting outliers is proposed. Firstly, the data are clustered into several categories by clustering. The data in the same categories share the common characteristics. In this way, we believe that the data in the same categories are under the same distribution which are simpler to fit than the whole data. So the original complex data distribution can be factored into several simple distributions. Secondly, kernel density estimation (KDE) hypothesis testing is used for abnormal value detection. Experiments in the UCI dataset and real industrial data show that the proposed method is more efficient than traditional methods.