Research on feature space migration fault diagnosis for missing data signals

Occasionally occurring events in the actual transmission process can lead to the deletion of signal-generated data, which in turn alters the signal's structure. The aim of this is to modify the structure of the missing signals and to reduce the volume of data. This paper introduces a method for diagnosing and integrating multi-dimensional features within a wide-tree model through migration. Initially, entities are extracted from various dimensions based on the sample structure, and the entity subspace for the target domain is established by eliminating redundant entities. Subsequently, within the Bagged-Tree ensemble classification model, the multi-dimensional feature space is crafted by leveraging the incremental dimensionality reduction from the sampling diagnosis of the sub-classifiers. Decisions at the sub-classification level are integrated using the ensemble classification layer, and ultimately, the target domain diagnosis is accomplished through the fine-tuning of the monitoring sample parameters.