Non-contact micro-vibration measurement and fault diagnosis method based on dynamic visual signal alignment

By extracting the time domain microvibration signals of rotating machinery and performing fault classification based on dynamic visual signal alignment, the problem of difficult to effectively utilize non-contact sensor data in the prior art is solved, and a highly accurate rotating machinery fault diagnosis is achieved.

CN119719979AActive Publication Date: 2025-03-28XI AN JIAOTONG UNIV
4 Cites 2 Cited by

Patent Information

Application Number
CN202411770214.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-04
Publication Date
2025-03-28
Estimated Expiration
2044-12-04

AI Technical Summary

Technical Problem

The prior art has the problem that contactless sensor data is difficult to effectively utilize in micro vibration measurement and fault diagnosis of rotating machinery, especially when event data lacks detailed feature information and is very random.

Method used

The non-contact microvibration measurement and fault diagnosis method based on dynamic visual signal alignment is adopted. The event data of rotating mechanical vibration is collected through the event camera, the time domain microvibration signals of each pixel point are extracted, and fault classification is carried out through signal alignment and feature clustering methods.

Benefits of technology

It realizes non-contact, global vibration monitoring and fault diagnosis, and improves the accuracy and reliability of rotating machinery in complex working environments.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

A non-contact micro-vibration measurement and fault diagnosis method based on dynamic visual signal alignment comprises the steps that firstly, a dynamic visual sensor-event camera is used for collecting event data of vibration of a rotating machine in a non-contact mode, and an event data characterization method is used for converting event streams into event frame sequences which are equal in interval, not overlapped and continuous; then micro-vibration information of each pixel point is extracted from the event frame sequence through a deep neural network, the difference of vibration signals of adjacent pixel points is reduced through a signal alignment method, and contact sensing data is used as a reference for optimizing model performance through a feature clustering method; and finally, performing intelligent fault diagnosis by using the obtained micro-vibration data. According to the invention, the applicability of the non-contact sensor in the fields of vibration monitoring and fault diagnosis of rotating machinery is improved.
Need to check novelty before this filing date? Find Prior Art