公开(公告)号：US20250103038A1

公开(公告)日：2025-03-27

申请号：US18724548

申请日：2022-01-11

Applicant: Northeastern University

Inventor： Chuan MA ,  Yingwei ZHANG ,  Lin FENG

IPC: G05B23/02 ,  G05B19/418

Abstract: Provided is a method for detecting abnormal working conditions of multi-view data based on feature regression. By the method, data capable of being acquired in a production process is collected together, a big data pool is established, and historical data information is fully utilized; by analyzing the data in the data pool, the method for detecting abnormal working conditions based on the multi-view data is established by a feature regression method, and a general mathematical model is established for preprocessed data acquired by different sensors; left and right projection vectors solved through the model can make similar sample points have better clustering effects in a low dimensional space; and by comparing a correlation between vectors after dimensionality reduction and various category vectors, production working conditions at a current time can be recognized.