Review of rotating machinery elements condition monitoring using acoustic emission signal

被引：8

作者：

Kundu, Pradeep ^{[1
]}

机构：

[1] Katholieke Univ Leuven, Dept Mech Engn, Campus Bruges, Brugge, Belgium

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2024年 / 252卷

关键词：

Acoustic emission; Condition Monitoring; Fault diagnosis; Fault prognosis; Machine learning; WIND TURBINE GEARBOX; EMPIRICAL MODE DECOMPOSITION; BEARING FAULT-DIAGNOSIS; SINGLE-STAGE GEARBOX; TOOL-WEAR; ASPERITY CONTACT; HELICAL GEARS; SPUR GEARS; VIBRATION; SPEED;

D O I：

10.1016/j.eswa.2024.124169

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Acoustic emission (AE) is defined as the structure -borne propagating transient elastic stress waves produced due to the rapid release of energy because of internal structure changes or asperities contact under loading. AE sensor is one of the promising sensing technologies for machinery and structures health monitoring. A start -to -end comprehensive condition monitoring solution utilizes the potential of four technologies, i.e., platform, data, analytics, and operations. This study reviews these four technologies developed based on AE sensors for rotating machinery element 's fault diagnosis and prognosis. Operating parameters influence the AE signal; hence, the influence of these parameters on AE characteristics is also discussed. Based on this review, challenges and future directions while using the AE sensor are detailed. No dedicated paper that summarises all these technologies in the context of AE sensors is available. Hence, using AE signals, analysis and measurements are mostly done by researchers based on their personal experiences. This article attempts to summarise the standard practices used by various researchers for AE data measurement and analysis for rotating machinery condition monitoring.

引用

页数：18

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