Fault diagnosis algorithms based on neighborhood preserving embedding (NPE) have been widely used because they can effectively extract the local information of the process. However, the typical NPE method is sensitive to parameter selection and outliers, while ignoring the global information of process data. Therefore, a fault diagnosis algorithm based on robust low-rank adaptive neighborhood preserving embedding (RLANPE) is proposed. This method integrates adaptive neighborhood embedding, projection learning and low-rank representation into a framework, which can effectively extract the local information of data while obtaining global optimal solution. In order to explore the global information of the data and eliminate the influence of outliers, low-rank constraint is imposed on the RLANPE to further enhance the information extraction capability. Meanwhile, the RLANPE introduces projection constraints based on ${l_{2,1