Multimode process monitoring strategy based on improved just-in-time-learning associated with locality preserving projections

In this paper, a multimode process monitoring strategy based on improved just-in-time-learning associated with locality preserving projections (IJITL-LPP) is proposed. First, raw data are projected into the feature space using locality preserving projections (LPP). Second, IJITL searches for similar samples of the query sample in the feature space by introducing a variational inference Gaussian mixture model (VIGMM). Finally, the new statistic named average distance is created to complete process monitoring. In the IJITL, the introduced VI can automatically determine the number of modes, thereby accelerating the efficiency of selecting similar samples. In the process monitoring phase, the average distance can reduce the impact of different mode dispersion on fault detection. In addition, LPP can render the model less sensitive to outliers. Compared with principal component analysis (PCA), LPP, K nearest neighbour rules, Gaussian mixture model (GMM), K-means based-PCA, and just-in-time-learning (JITL)-based LPP, the proposed method has better performance in a numerical case, the Tennessee Eastman process, and the semiconductor etching process.