公开(公告)号：US20240201048A1

公开(公告)日：2024-06-20

申请号：US17789523

申请日：2022-02-10

Applicant: JIANGSU UNIVERSITY

Inventor： Wei FAN ,  Yingqi XU ,  Zhenqiang CHEN ,  Yujie SHEN ,  Long CHEN

IPC: G01M13/045

CPC classification number: G01M13/045

Abstract: A generalized autocorrelation method for bearing fault feature extraction under a variable rotational speed condition includes: resampling an original vibration signal in an order domain based on instantaneous phase information by using an order tracking processing method, to greatly weaken a frequency modulation phenomenon; further weakening background noise in consideration of a correlation between a plurality of adjacent fragments by using a generalized autocorrelation method; and controlling an accumulation of periodic disturbances by considering only a correlation between several adjacent signal fragments based on that conventional noise resistant correlation (NRC) methods consider a correlation between all signal fragments and cannot eliminate influence of accumulated periodic disturbances. Compared with the conventional methods, this method overcomes the difficulties caused by mutually restricting signal features, and achieves a better effect.

公开(公告)号：US20240219267A1

公开(公告)日：2024-07-04

申请号：US17920053

申请日：2022-02-10

Applicant: JIANGSU UNIVERSITY

Inventor： Wei FAN ,  Zhenqiang CHEN ,  Yingqi XU ,  Yujie SHEN ,  Long CHEN

IPC: G01M99/00 ,  G01H1/00

CPC classification number: G01M99/005 ,  G01H1/003

Abstract: A strong-robustness method for extracting early degradation features of signals and monitoring an operational status of a device is provided. Acquired vibration signal data of a rotating mechanical device is grouped at equal time intervals in a chronological order. Compression conversion is performed on the data, a newly defined function is solved, thereby a performance degradation index of the device is obtained. Data of the device in a normal status is obtained by determining an overall trend of an Exponentially Weighted Moving Average (EWMA) statistic, and a control limit for the EWMA statistic is constructed by using the data in the normal status. The calculated performance degradation index of the device is converted into an EWMA statistic, and the EWMA statistic is compared with the control limit. If the EWMA statistic does not fluctuate about a center line or exceeds the control limit, a monitored state is out of control.