Motor Bearing Fault Source Localization Based on Sound and Robot Movement Characteristics

Dong Lv, Guojin Feng, Dong Zhen, Xiaoxia Liang, Guohua Sun, Fengshou Gu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Manual inspection is often inefficient and easily misses detections for the extensive amount of mechanical equipment in industrial production. Incorporating acoustic measurement and diagnostics into mobile robots has multiple advantages. Utilizing the movement characteristics of the robot to search for the location with the best signal quality at different points can help it better adapt to the complex acoustic environment of industrial sites. The focus is on using nonsynchronous measurement technology, the robot can provide precise sequential movement, which can extend the lower frequency detection limit of the array. It is proved by simulation and experiments that the fault sound source of motor bearing can be accurately located based on robot moving characteristics. This method provides a valuable reference for non-contact monitoring of rotating machinery such as motors.

Original languageEnglish
Title of host publicationICSMD 2024 - 5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798331529192
ISBN (Print)9798331529208
DOIs
Publication statusPublished - 20 Mar 2025
Event5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence - Huangshan, China
Duration: 31 Oct 20243 Nov 2024
Conference number: 5

Conference

Conference5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence
Abbreviated titleICSMD 2024
Country/TerritoryChina
CityHuangshan
Period31/10/243/11/24

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