Nonparametric manifold learning approach for improved process monitoring

A novel nonparametric method based on manifold learning is proposed for industrial process monitoring. In conventional algorithms, to preserve the global and local structure information of data, heat kernels containing two auxiliary parameters are introduced to define the global and local weight matrices, respectively. However, it is difficult to identify and choose these two parameters empirically. The inadequate selection of parameters can lead to one-sided and inappropriate global and local feature extractions, resulting in an inadequate fault detection performance. To resolve the above problems, a nonparametric strategy is used in this study to generate two nonparametric weight matrices to replace the heat kernel-based weight matrices. Consequently, the proposed method requires no auxiliary parameters in defining the weight matrices, making it more practical. Moreover, it automatically determines a good trade-off between global and local feature extractions. A process monitoring model based on the proposed method was developed. The feasibility and effectiveness of the new nonparametric method are evaluated using a synthetic example and the Tennessee Eastman chemical process.