Detection and Diagnosis of Centrifugal Pump Bearing Faults Based on the Envelope Analysis of Airborne Sound Signals

Alsadak Daraz, Samir Alabied, Ann Smith, Fengshou Gu, Andrew Ball

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

Abstract

As key components in centrifugal pumps rolling bearings work to reduce friction and maintain the impeller rotor in correct alignment with stationary parts under the action of radial and transverse loads. Effective fault detection of bearings allows appropriate preventive action to be taken timely, where required, and enhances performance operation. To develop an easy implementation and yet effective method for detecting and diagnosing pump bearing faults, the focus of this study is on utilising airborne sound signals which can be acquired more remotely and at lower cost, compared with vibration based methods which needs high numbers of sensors for monitoring a pump system. However, acoustic signals are much noisy, and it is difficult to detect machine faults using conventional signal processing methods such as time domain features, where the results have a limited and weak fault signatures. Thus, a more advanced signal processing technique: the envelope spectrum is adopted to establish accurate diagnostic fault patterns. The evaluating results show that the proposed method is effective and accurate to enhance the amplitudes at bearing characteristic frequencies, allowing diagnostic information to be extracted reliably, which also makes the Root Mean Square (RMS) of the envelope signals give a full separation between faulty and healthy cases over a wide range of pump operation, outperforming the vibration signals.
Original languageEnglish
Title of host publication 2018 24th International Conference on Automation and Computing (ICAC)
EditorsXiandong Ma
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)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

Bearings (structural)
Centrifugal pumps
Acoustic waves
Pumps
Signal processing
Fault detection
Rotors
Acoustics
Friction
Monitoring
Sensors
Costs

Cite this

Daraz, A., Alabied, S., Smith, A., Gu, F., & Ball, A. (2019). Detection and Diagnosis of Centrifugal Pump Bearing Faults Based on the Envelope Analysis of Airborne Sound Signals. In X. Ma (Ed.), 2018 24th International Conference on Automation and Computing (ICAC) [8749053] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/IConAC.2018.8749053
Daraz, Alsadak ; Alabied, Samir ; Smith, Ann ; Gu, Fengshou ; Ball, Andrew. / Detection and Diagnosis of Centrifugal Pump Bearing Faults Based on the Envelope Analysis of Airborne Sound Signals. 2018 24th International Conference on Automation and Computing (ICAC). editor / Xiandong Ma. Institute of Electrical and Electronics Engineers Inc., 2019.
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abstract = "As key components in centrifugal pumps rolling bearings work to reduce friction and maintain the impeller rotor in correct alignment with stationary parts under the action of radial and transverse loads. Effective fault detection of bearings allows appropriate preventive action to be taken timely, where required, and enhances performance operation. To develop an easy implementation and yet effective method for detecting and diagnosing pump bearing faults, the focus of this study is on utilising airborne sound signals which can be acquired more remotely and at lower cost, compared with vibration based methods which needs high numbers of sensors for monitoring a pump system. However, acoustic signals are much noisy, and it is difficult to detect machine faults using conventional signal processing methods such as time domain features, where the results have a limited and weak fault signatures. Thus, a more advanced signal processing technique: the envelope spectrum is adopted to establish accurate diagnostic fault patterns. The evaluating results show that the proposed method is effective and accurate to enhance the amplitudes at bearing characteristic frequencies, allowing diagnostic information to be extracted reliably, which also makes the Root Mean Square (RMS) of the envelope signals give a full separation between faulty and healthy cases over a wide range of pump operation, outperforming the vibration signals.",
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Daraz, A, Alabied, S, Smith, A, Gu, F & Ball, A 2019, Detection and Diagnosis of Centrifugal Pump Bearing Faults Based on the Envelope Analysis of Airborne Sound Signals. in X Ma (ed.), 2018 24th International Conference on Automation and Computing (ICAC)., 8749053, 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.2018.8749053

Detection and Diagnosis of Centrifugal Pump Bearing Faults Based on the Envelope Analysis of Airborne Sound Signals. / Daraz, Alsadak; Alabied, Samir; Smith, Ann; Gu, Fengshou; Ball, Andrew.

2018 24th International Conference on Automation and Computing (ICAC). ed. / Xiandong Ma. Institute of Electrical and Electronics Engineers Inc., 2019. 8749053.

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

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AU - Alabied, Samir

AU - Smith, Ann

AU - Gu, Fengshou

AU - Ball, Andrew

PY - 2019/7/1

Y1 - 2019/7/1

N2 - As key components in centrifugal pumps rolling bearings work to reduce friction and maintain the impeller rotor in correct alignment with stationary parts under the action of radial and transverse loads. Effective fault detection of bearings allows appropriate preventive action to be taken timely, where required, and enhances performance operation. To develop an easy implementation and yet effective method for detecting and diagnosing pump bearing faults, the focus of this study is on utilising airborne sound signals which can be acquired more remotely and at lower cost, compared with vibration based methods which needs high numbers of sensors for monitoring a pump system. However, acoustic signals are much noisy, and it is difficult to detect machine faults using conventional signal processing methods such as time domain features, where the results have a limited and weak fault signatures. Thus, a more advanced signal processing technique: the envelope spectrum is adopted to establish accurate diagnostic fault patterns. The evaluating results show that the proposed method is effective and accurate to enhance the amplitudes at bearing characteristic frequencies, allowing diagnostic information to be extracted reliably, which also makes the Root Mean Square (RMS) of the envelope signals give a full separation between faulty and healthy cases over a wide range of pump operation, outperforming the vibration signals.

AB - As key components in centrifugal pumps rolling bearings work to reduce friction and maintain the impeller rotor in correct alignment with stationary parts under the action of radial and transverse loads. Effective fault detection of bearings allows appropriate preventive action to be taken timely, where required, and enhances performance operation. To develop an easy implementation and yet effective method for detecting and diagnosing pump bearing faults, the focus of this study is on utilising airborne sound signals which can be acquired more remotely and at lower cost, compared with vibration based methods which needs high numbers of sensors for monitoring a pump system. However, acoustic signals are much noisy, and it is difficult to detect machine faults using conventional signal processing methods such as time domain features, where the results have a limited and weak fault signatures. Thus, a more advanced signal processing technique: the envelope spectrum is adopted to establish accurate diagnostic fault patterns. The evaluating results show that the proposed method is effective and accurate to enhance the amplitudes at bearing characteristic frequencies, allowing diagnostic information to be extracted reliably, which also makes the Root Mean Square (RMS) of the envelope signals give a full separation between faulty and healthy cases over a wide range of pump operation, outperforming the vibration signals.

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SN - 9781538648919

BT - 2018 24th International Conference on Automation and Computing (ICAC)

A2 - Ma, Xiandong

PB - Institute of Electrical and Electronics Engineers Inc.

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Daraz A, Alabied S, Smith A, Gu F, Ball A. Detection and Diagnosis of Centrifugal Pump Bearing Faults Based on the Envelope Analysis of Airborne Sound Signals. In Ma X, editor, 2018 24th International Conference on Automation and Computing (ICAC). Institute of Electrical and Electronics Engineers Inc. 2019. 8749053 https://doi.org/10.23919/IConAC.2018.8749053