Acoustic emission signal processing for rolling bearing running state assessment using compressive sensing

Liu, Chang; Mao, Jianlin; Liu, Xiaoqin

doi:10.1016/j.ymssp.2016.12.010

Cited by 43 publications

(14 citation statements)

References 16 publications

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“…Liu et al 243 proposed a framework based on compressive sensing (CS) signal processing for the running state of the rolling bearing. Moreover, compressive sensing is applied to extract compressive features from the AE signal directly.…”

Section: And Fd For Im Based On Aementioning

confidence: 99%

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

AlShorman

Alkahatni

Masadeh

et al. 2021

Advances in Mechanical Engineering

126

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Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating machinery (RM) has a vital role in the modern industrial world. However, the remaining useful life (RUL) of machinery is crucial for continuous monitoring and timely maintenance. Moreover, reduced maintenance costs, enhanced safety, efficiency, reliability, and availability are the main important industrial issues to maintain valuable and high-cost machinery. Undoubtedly, induction motor (IM) is considered to be a pivotal component in industrial machines. Recently, acoustic emission (AE) becomes a very accurate and efficient method for fault, leaks and fatigue detection and monitoring techniques. Moreover, CM and FD based on the AE of IM have been growing over recent years. The proposed research study aims to review condition monitoring (CM) and fault diagnosis (FD) studies based on sound and AE for four types of faults: bearings, rotor, stator, and compound. The study also points out the advantages and limitations of using sound and AE analysis in CM and FD. Existing public datasets for AE based analysis for CM and FD of IM are also mentioned. Finally, challenges facing AE based CM and FD for RM, especially for IM, and possible future works are addressed in this study.

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Section: And Fd For Im Based On Aementioning

confidence: 99%

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

AlShorman

Alkahatni

Masadeh

et al. 2021

Advances in Mechanical Engineering

126

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“…races or rollers), generating high energy AE signals. Measurement of these signals can lead to early fault detection [54,55].…”

Section: Acoustic Emissionmentioning

confidence: 99%

Perspectives on railway axle bearing condition monitoring

Entezami

Roberts

Weston

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part F:

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Defects in railway axle bearings can affect operational efficiency, or cause in-service failures, damaging the track and train. Healthy bearings produce a certain level of vibration and noise, but a bearing with a defect causes substantial changes in the vibration and noise levels. It is possible to detect the bearing defects at an early stage of their development, allowing an operator to repair the damage before it becomes serious. When a vehicle is scheduled for maintenance, or due for overhaul, knowledge of bearing damage and severity is beneficial, resulting in fewer operational problems and optimised fleet availability. This paper is a review of the state of the art in condition monitoring systems for rolling element bearings, especially the axlebox bearings. This includes exploring the sensing technologies, summarising the main signal processing methods and condition monitoring techniques, i.e. wayside and on-board. Examples of commercially available systems and outputs of current research work are presented. The effectiveness of the current monitoring technologies is assessed and the p– f curve is presented. It is concluded that the research and practical tests on axlebox bearing monitoring are limited compared to the generic bearing applications.

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“…As a nondestructive testing method, acoustic emission technology (AET) is characterized by its integrity, passive detection, and high sensitivity. It has applications in damage localization [2][3][4], signal processing [5][6][7], and damage recognition [8][9][10]. In recent years, researchers have also carried out research on the application of acoustic emission technology in wood fractures.…”

Section: Introductionmentioning

confidence: 99%

Thermodynamic Approach for the Identification of Instability in the Wood Using Acoustic Emission Technology

Zhao

2020

Forests

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In order to monitor the crack growth of the wood material better and reduce failure risks, this paper studied the attenuation characteristics of acoustic emission signals in wood through pencil lead breaking (PLB) tests, in the aim of estimating the true amplitude value of the acoustic emission source signal. The tensile test of the double cantilever beam (DCB) specimens was used to simulate the crack tip growth within wood material, monitoring acoustic activity and location of crack tips within wood material using acoustic emission technology and digital image correlation (DIC). Results showed that the attenuation degree of acoustic emission signals increased exponentially as the propagation distance increased, and the relationship between relative amplitude attenuation rate and the propagation distance of the acoustic emission signal was established by the regression method, which provides the input parameters for the establishment of the crack instability prediction model in the next step. Based on a thermodynamic approach, a theoretical model for predicting crack instability was established, and the model was verified by DCB tests. The model uses acoustic emission parameters as the basis for judging whether the crack is instable. It provides theoretical support for the application of acoustic emission technology in wood health monitoring.

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Acoustic emission signal processing for rolling bearing running state assessment using compressive sensing

Cited by 43 publications

References 16 publications

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

Perspectives on railway axle bearing condition monitoring

Thermodynamic Approach for the Identification of Instability in the Wood Using Acoustic Emission Technology

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