公开(公告)号：US12275597B2

公开(公告)日：2025-04-15

申请号：US17640035

申请日：2021-07-21

Applicant: Northeastern University

Inventor： Xin Sha ,  Lin Feng ,  Yingwei Zhang

IPC: B65G43/00 ,  G06F17/11

Abstract: Provided is a sound-based roller fault detecting method by using double-projection neighborhoods preserving embedding, including: acquiring sound data during operation of a roller, performing a wavelet transform energy feature extraction on normal data in the data to obtain wavelet transform energy feature data, then performing double-projection neighborhoods preserving embedding feature extraction on the wavelet energy feature data to obtain an optimal projection matrix of the feature data, establishing a detection model, constructing T2 statistics of a feature space and a residual space of normal sound data, determining detection control limits according to the T2 statistics by a kernel density estimation method, and further judging whether newly acquired data has faults. According to the present method, main features of the data can be extracted under the conditions of non-dimensional reduction and dimensional reduction, and thus the present method can achieve the purpose of increasing fault detection accuracy.