Performance monitoring and fault diagnosis of vacuum pumps based on airborne sounds

Robin Appadoo, Yuandong Xu, Fengshou Gu, Andrew D. Ball

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a cost-effective scheme of implementing condition monitoring (CM) for a vacuum pump station, which combines the airborne sound (AS) measured remotely with high efficiency of abnormality detection, with surface vibration (SV) measured locally with high diagnostic capability. In particular, AS measurement is employed to implement online and real time monitoring of a number of machines such as several vacuum pumps spread over a large area. Once there is any abnormality found, SV will be used to diagnose the faulty locations and severities. In this way the monitoring can be more cost-effective by avoiding the use of a high number of vibration measurements. Having gained the dynamics of vacuum pumps and had a failure mode and effects analysis (FMEA), this study details the implementation of this scheme based on the vacuum pump station of a paper mill. It demonstrates that airborne sound can show the relative spectral components for each vacuum pump to a certain degree of accuracy, allowing a quick discrimination of potential faults of these pumps. This demonstrates that the AS measurement is an appropriate technique to use for such circumstances where many machines need to be monitored but limited budget can be invested in the complicated monitoring systems.

LanguageEnglish
Title of host publication2018 24th IEEE International Conference on Automation and Computing (ICAC)
Subtitle of host publicationImproving Productivity through Automation and Computing
EditorsXiandong Ma
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781862203426, 9781862203419
ISBN (Print)9781538648919
DOIs
Publication statusPublished - 1 Jul 2019
Event24th IEEE International Conference on Automation and Computing: Improving Productivity through Automation and Computing - Newcastle University, Newcastle upon Tyne, United Kingdom
Duration: 6 Sep 20187 Sep 2018
Conference number: 24
https://ieeexplore.ieee.org/xpl/conhome/8742895/proceeding (Website Containing the Proceedings)
http://www.cacsuk.co.uk/index.php/conferences/icac (Link to Conference Information)

Conference

Conference24th IEEE International Conference on Automation and Computing
Abbreviated titleICAC 2018
CountryUnited Kingdom
CityNewcastle upon Tyne
Period6/09/187/09/18
Internet address

Fingerprint

Vacuum pumps
Performance Monitoring
Fault Diagnosis
Pump
Failure analysis
Vacuum
Acoustic waves
Monitoring
Acoustic variables measurement
Vibration
Vibration measurement
Real-time Monitoring
Failure Modes and Effects Analysis
Vibration Measurement
Condition monitoring
Condition Monitoring
Failure modes
Costs
Monitoring System
Demonstrate

Cite this

Appadoo, R., Xu, Y., Gu, F., & Ball, A. D. (2019). Performance monitoring and fault diagnosis of vacuum pumps based on airborne sounds. In X. Ma (Ed.), 2018 24th IEEE International Conference on Automation and Computing (ICAC): Improving Productivity through Automation and Computing [8748969] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/IConAC.2018.8748969
Appadoo, Robin ; Xu, Yuandong ; Gu, Fengshou ; Ball, Andrew D. / Performance monitoring and fault diagnosis of vacuum pumps based on airborne sounds. 2018 24th IEEE International Conference on Automation and Computing (ICAC): Improving Productivity through Automation and Computing. editor / Xiandong Ma. Institute of Electrical and Electronics Engineers Inc., 2019.
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Appadoo, R, Xu, Y, Gu, F & Ball, AD 2019, Performance monitoring and fault diagnosis of vacuum pumps based on airborne sounds. in X Ma (ed.), 2018 24th IEEE International Conference on Automation and Computing (ICAC): Improving Productivity through Automation and Computing., 8748969, Institute of Electrical and Electronics Engineers Inc., 24th IEEE International Conference on Automation and Computing, Newcastle upon Tyne, United Kingdom, 6/09/18. https://doi.org/10.23919/IConAC.2018.8748969

Performance monitoring and fault diagnosis of vacuum pumps based on airborne sounds. / Appadoo, Robin; Xu, Yuandong; Gu, Fengshou; Ball, Andrew D.

2018 24th IEEE International Conference on Automation and Computing (ICAC): Improving Productivity through Automation and Computing. ed. / Xiandong Ma. Institute of Electrical and Electronics Engineers Inc., 2019. 8748969.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Ball, Andrew D.

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N2 - This paper presents a cost-effective scheme of implementing condition monitoring (CM) for a vacuum pump station, which combines the airborne sound (AS) measured remotely with high efficiency of abnormality detection, with surface vibration (SV) measured locally with high diagnostic capability. In particular, AS measurement is employed to implement online and real time monitoring of a number of machines such as several vacuum pumps spread over a large area. Once there is any abnormality found, SV will be used to diagnose the faulty locations and severities. In this way the monitoring can be more cost-effective by avoiding the use of a high number of vibration measurements. Having gained the dynamics of vacuum pumps and had a failure mode and effects analysis (FMEA), this study details the implementation of this scheme based on the vacuum pump station of a paper mill. It demonstrates that airborne sound can show the relative spectral components for each vacuum pump to a certain degree of accuracy, allowing a quick discrimination of potential faults of these pumps. This demonstrates that the AS measurement is an appropriate technique to use for such circumstances where many machines need to be monitored but limited budget can be invested in the complicated monitoring systems.

AB - This paper presents a cost-effective scheme of implementing condition monitoring (CM) for a vacuum pump station, which combines the airborne sound (AS) measured remotely with high efficiency of abnormality detection, with surface vibration (SV) measured locally with high diagnostic capability. In particular, AS measurement is employed to implement online and real time monitoring of a number of machines such as several vacuum pumps spread over a large area. Once there is any abnormality found, SV will be used to diagnose the faulty locations and severities. In this way the monitoring can be more cost-effective by avoiding the use of a high number of vibration measurements. Having gained the dynamics of vacuum pumps and had a failure mode and effects analysis (FMEA), this study details the implementation of this scheme based on the vacuum pump station of a paper mill. It demonstrates that airborne sound can show the relative spectral components for each vacuum pump to a certain degree of accuracy, allowing a quick discrimination of potential faults of these pumps. This demonstrates that the AS measurement is an appropriate technique to use for such circumstances where many machines need to be monitored but limited budget can be invested in the complicated monitoring systems.

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Appadoo R, Xu Y, Gu F, Ball AD. Performance monitoring and fault diagnosis of vacuum pumps based on airborne sounds. In Ma X, editor, 2018 24th IEEE International Conference on Automation and Computing (ICAC): Improving Productivity through Automation and Computing. Institute of Electrical and Electronics Engineers Inc. 2019. 8748969 https://doi.org/10.23919/IConAC.2018.8748969