Condition monitoring and fault diagnosis based on multipoint optimal minimum entropy deconvolution adjusted technique

Ibrahim Alqatawneh, Jiang Kuosheng, Zainab Mones, Qiang Zeng, Fengshou Gu, Andrew D. Ball

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

Abstract

Planetary gearbox (PG) exhibits unique dynamic behaviour that imposes great challenges in gear fault diagnosis. In particular, multiple and time-varying vibration transmission paths from the gear meshing point to the sensor, usually mounted on the PG housing, cause not only additional spectral components in the signal but also strong noise. Thus, the influence of the transmission paths and multiple vibration sources make fault indications hard to distinguish. This paper presents a new approach for fault diagnosis of PG based on Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA). MOMEDA has been demonstrated effective to suppress the path dissertation for linear time-invariant (LTI) system. However, its performance has not been examined with the case of a time-variant system such as PG vibration system. Therefore, an experimental evaluation is carried out to evaluate and optimise MOMEDA analysis for minimising the path influnces and enhancing periodic fault impulses generated by the faulty gear. A set of experimental data acquired from the PG with seeded with common faults on the planet gear and sun gear. The results obtained by the optimised filter length show that the MOMEDA has the expected capability and allows the seeded faults to be diagnostic successfully under different loads, confirming the generality of the approach.

Original 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.
Number of pages6
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

Gearbox
Condition Monitoring
Deconvolution
Condition monitoring
Fault Diagnosis
Failure analysis
Gears
Entropy
Fault
Path
Vibration
Meshing
Planets
Sun
Experimental Evaluation
Impulse
Dynamic Behavior
Linear Time
Time-varying
Diagnostics

Cite this

Alqatawneh, I., Kuosheng, J., Mones, Z., Zeng, Q., Gu, F., & Ball, A. D. (2019). Condition monitoring and fault diagnosis based on multipoint optimal minimum entropy deconvolution adjusted technique. In X. Ma (Ed.), 2018 24th IEEE International Conference on Automation and Computing (ICAC): Improving Productivity through Automation and Computing [8748963] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/IConAC.2018.8748963
Alqatawneh, Ibrahim ; Kuosheng, Jiang ; Mones, Zainab ; Zeng, Qiang ; Gu, Fengshou ; Ball, Andrew D. / Condition monitoring and fault diagnosis based on multipoint optimal minimum entropy deconvolution adjusted technique. 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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abstract = "Planetary gearbox (PG) exhibits unique dynamic behaviour that imposes great challenges in gear fault diagnosis. In particular, multiple and time-varying vibration transmission paths from the gear meshing point to the sensor, usually mounted on the PG housing, cause not only additional spectral components in the signal but also strong noise. Thus, the influence of the transmission paths and multiple vibration sources make fault indications hard to distinguish. This paper presents a new approach for fault diagnosis of PG based on Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA). MOMEDA has been demonstrated effective to suppress the path dissertation for linear time-invariant (LTI) system. However, its performance has not been examined with the case of a time-variant system such as PG vibration system. Therefore, an experimental evaluation is carried out to evaluate and optimise MOMEDA analysis for minimising the path influnces and enhancing periodic fault impulses generated by the faulty gear. A set of experimental data acquired from the PG with seeded with common faults on the planet gear and sun gear. The results obtained by the optimised filter length show that the MOMEDA has the expected capability and allows the seeded faults to be diagnostic successfully under different loads, confirming the generality of the approach.",
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Alqatawneh, I, Kuosheng, J, Mones, Z, Zeng, Q, Gu, F & Ball, AD 2019, Condition monitoring and fault diagnosis based on multipoint optimal minimum entropy deconvolution adjusted technique. in X Ma (ed.), 2018 24th IEEE International Conference on Automation and Computing (ICAC): Improving Productivity through Automation and Computing., 8748963, 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.2018.8748963

Condition monitoring and fault diagnosis based on multipoint optimal minimum entropy deconvolution adjusted technique. / Alqatawneh, Ibrahim; Kuosheng, Jiang; Mones, Zainab; Zeng, Qiang; 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. 8748963.

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

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T1 - Condition monitoring and fault diagnosis based on multipoint optimal minimum entropy deconvolution adjusted technique

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AU - Mones, Zainab

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AU - Gu, Fengshou

AU - Ball, Andrew D.

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N2 - Planetary gearbox (PG) exhibits unique dynamic behaviour that imposes great challenges in gear fault diagnosis. In particular, multiple and time-varying vibration transmission paths from the gear meshing point to the sensor, usually mounted on the PG housing, cause not only additional spectral components in the signal but also strong noise. Thus, the influence of the transmission paths and multiple vibration sources make fault indications hard to distinguish. This paper presents a new approach for fault diagnosis of PG based on Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA). MOMEDA has been demonstrated effective to suppress the path dissertation for linear time-invariant (LTI) system. However, its performance has not been examined with the case of a time-variant system such as PG vibration system. Therefore, an experimental evaluation is carried out to evaluate and optimise MOMEDA analysis for minimising the path influnces and enhancing periodic fault impulses generated by the faulty gear. A set of experimental data acquired from the PG with seeded with common faults on the planet gear and sun gear. The results obtained by the optimised filter length show that the MOMEDA has the expected capability and allows the seeded faults to be diagnostic successfully under different loads, confirming the generality of the approach.

AB - Planetary gearbox (PG) exhibits unique dynamic behaviour that imposes great challenges in gear fault diagnosis. In particular, multiple and time-varying vibration transmission paths from the gear meshing point to the sensor, usually mounted on the PG housing, cause not only additional spectral components in the signal but also strong noise. Thus, the influence of the transmission paths and multiple vibration sources make fault indications hard to distinguish. This paper presents a new approach for fault diagnosis of PG based on Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA). MOMEDA has been demonstrated effective to suppress the path dissertation for linear time-invariant (LTI) system. However, its performance has not been examined with the case of a time-variant system such as PG vibration system. Therefore, an experimental evaluation is carried out to evaluate and optimise MOMEDA analysis for minimising the path influnces and enhancing periodic fault impulses generated by the faulty gear. A set of experimental data acquired from the PG with seeded with common faults on the planet gear and sun gear. The results obtained by the optimised filter length show that the MOMEDA has the expected capability and allows the seeded faults to be diagnostic successfully under different loads, confirming the generality of the approach.

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DO - 10.23919/IConAC.2018.8748963

M3 - Conference contribution

SN - 9781538648919

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

A2 - Ma, Xiandong

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Alqatawneh I, Kuosheng J, Mones Z, Zeng Q, Gu F, Ball AD. Condition monitoring and fault diagnosis based on multipoint optimal minimum entropy deconvolution adjusted technique. 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. 8748963 https://doi.org/10.23919/IConAC.2018.8748963