Instance-based transfer learning method for locating loose particles inside aerospace equipment

doi:10.1016/j.measurement.2023.113485

2023-11-01

PRE

DOI：10.1016/j.measurement.2023.113485

原文链接

原文求助

摘要

En 中文

The existing supervised-learning-based loose particle localization method requires a lot of resources to create a complete loose particle localization data set, which leads to the problems of long localization testing cycle, large data storage requirement and low resource utilization. In this study, the authors proposed an instance-based transfer learning method for locating loose particles inside aerospace equipment. It used correlations between the localization data sets corresponding to the aerospace equipment with similar structure or size, to complete the knowledge transfer from the source localization data set to the new localization data set. Specifically, based on the existing localization data set containing a large amount of localization data, when facing new aerospace equipment, only a new localization data set containing small amounts of localization data needed to be created. The TrAdaBoost algorithm is used to learn on the new localization data set, the weights of the localization data in the existing localization data set are calculated, the localization data with higher weights are filtered to add to the new localization data set, thus create the transfer training data set and train the loose particle transfer localization (abbr. LPTL) model. This effectively improves the localization efficiency and data resource utilization. In addition, based on engineering application requirements, the authors proposed the definition of equipment-level loose particle localization accuracy. Tests on aerospace power supplies and aerospace electronic single-machines show that, the appropriate weight threshold was set to 0.35, 6004 pieces of localization data were filtered from the existing localization data set. The average classification accuracy of 82.85% and the average localization accuracy of 83.33% were achieved by the LPTL model based on the parameter-optimized XGBoost ensemble classifier. In theory, this method can be extended and applied to the loose particle material identification and weight estimation research, and has high reference value.

Keyword:

Loose particle localization

Instance-based transfer learning

TrAdaBoost algorithm

XGBoost ensemble classifier

Localization accuracy

Aerospace equipment