Multi-condition identification of thermal process data based on mixed constraints semi-supervised clustering

The multi-model method is used in complex system modeling or industrial data monitoring, and its goal is to establish the sub-models corresponding to different conditions. How to divide the modeling data into datasets corresponding to different conditions in the case of insufficient prior knowledge of the process is an important problem to be solved. Machine learning provides many excellent algorithms for condition identification. However, many algorithms are easily affected by abnormal data, but making full use of prior knowledge can effectively solve this problem. Based on the semi-supervised clustering, mixed constraints which include composite distance and the pairwise constraint are introduced to distinguish strongly and weakly dependent data and clarify boundary data to realize a multi-model condition division of thermal process data. Radial Basis Function neural network is used to realize feature learning, and an online condition identification is constructed. The influence of network structure and parameters on the generalization ability of the recognizer is analyzed. On the premise of not significantly increasing the amount of calculation, the generalization ability is improved by adjusting the weight coefficient in the composite distance, and the weight coefficient under low error rate is given by particle swarm optimization. Compared with classic the methods, such as Sliding Windows, Bottom-Up and Top-Down, the proposed method has better performance in segmentation results. Article Highlights The thermal process data in a segment is divided into strongly and weakly dependent data, and different segmentation methods are given. The definition of composite distance parameter, including time component, value component and velocity component, is given to distinguish different types of data. The semi-supervised clustering with mixed constraints, superimposing composite distance constraint and pairwise constraint, is given to segment the thermal process data.