Fault Diagnosis for Planetary Gearbox Using On-Rotor MEMS Sensor and EMD Analysis

Zainab Mones, Ibrahim Alqatawneh, Dong Zhen, Fengshou Gu, Andrew D. Ball

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

Abstract

Rotating machinery fault diagnosis based on Micro-Electro-Mechanical Systems (MEMS) technologies has recently received considerable attention. The significant advancements in MEMS make it easier and more conceivable to mount a low-cost MEMS sensor directly on the rotating shaft, allowing more accurate dynamic characteristics of the rotor to be obtained for mechanical condition monitoring and fault diagnosis. However, the measured signals contain strong background noise and modulation effect, which results in low signal-to-noise ratio (SNR), and consequently attenuate the accuracy of the diagnosis results. To improve the SNR of the measured signals, a denoising method based on empirical mode decomposition (EMD) is developed in this paper to suppress the background noise and enhance the modulation components for accurate fault feature extraction. Firstly, EMD is applied to decompose the original signal into a list of intrinsic mode functions (IMFs), and then the IMF with the highest correlation coefficient value is selected for further analysis. Finally, the envelop analysis (EA) is employed to demodulate the denoised signal by EMD for fault feature extraction and diagnosis. The experimental results show that the proposed EMD based denoising approach gives a promising result in diagnosing common PG faults.

Original languageEnglish
Title of host publication2019 25th IEEE International Conference on Automation and Computing, ICAC 2019
Subtitle of host publicationImproving Productivity through Automation and Computing
EditorsHui Yu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781861376657
ISBN (Print)9781728125183
DOIs
Publication statusPublished - 11 Nov 2019
Event25th IEEE International Conference on Automation and Computing: Improving Productivity through Automation and Computing - Lancaster University, Lancaster, United Kingdom
Duration: 5 Sep 20197 Sep 2019
Conference number: 25
http://www.research.lancs.ac.uk/portal/en/activities/25th-ieee-international-conference-on-automation-and-computing-icac19-57-september-2019-lancaster-university-uk(679d94ff-4efb-46b5-9c80-c6d34a13bae4).html

Conference

Conference25th IEEE International Conference on Automation and Computing
Abbreviated titleICAC 2019
CountryUnited Kingdom
CityLancaster
Period5/09/197/09/19
Internet address

Fingerprint

Gearbox
Fault Diagnosis
Micro-electro-mechanical Systems
Rotor
Failure analysis
Rotors
Decomposition
Decompose
Sensor
Sensors
Intrinsic Mode Function
Feature extraction
Signal to noise ratio
Fault
Modulation
Denoising
Rotating machinery
Feature Extraction
Condition monitoring
Rotating Machinery

Cite this

Mones, Z., Alqatawneh, I., Zhen, D., Gu, F., & Ball, A. D. (2019). Fault Diagnosis for Planetary Gearbox Using On-Rotor MEMS Sensor and EMD Analysis. In H. Yu (Ed.), 2019 25th IEEE International Conference on Automation and Computing, ICAC 2019 : Improving Productivity through Automation and Computing [8895155] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/IConAC.2019.8895155
Mones, Zainab ; Alqatawneh, Ibrahim ; Zhen, Dong ; Gu, Fengshou ; Ball, Andrew D. / Fault Diagnosis for Planetary Gearbox Using On-Rotor MEMS Sensor and EMD Analysis. 2019 25th IEEE International Conference on Automation and Computing, ICAC 2019 : Improving Productivity through Automation and Computing. editor / Hui Yu. Institute of Electrical and Electronics Engineers Inc., 2019.
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title = "Fault Diagnosis for Planetary Gearbox Using On-Rotor MEMS Sensor and EMD Analysis",
abstract = "Rotating machinery fault diagnosis based on Micro-Electro-Mechanical Systems (MEMS) technologies has recently received considerable attention. The significant advancements in MEMS make it easier and more conceivable to mount a low-cost MEMS sensor directly on the rotating shaft, allowing more accurate dynamic characteristics of the rotor to be obtained for mechanical condition monitoring and fault diagnosis. However, the measured signals contain strong background noise and modulation effect, which results in low signal-to-noise ratio (SNR), and consequently attenuate the accuracy of the diagnosis results. To improve the SNR of the measured signals, a denoising method based on empirical mode decomposition (EMD) is developed in this paper to suppress the background noise and enhance the modulation components for accurate fault feature extraction. Firstly, EMD is applied to decompose the original signal into a list of intrinsic mode functions (IMFs), and then the IMF with the highest correlation coefficient value is selected for further analysis. Finally, the envelop analysis (EA) is employed to demodulate the denoised signal by EMD for fault feature extraction and diagnosis. The experimental results show that the proposed EMD based denoising approach gives a promising result in diagnosing common PG faults.",
keywords = "Condition monitoring, EMD, MEMS Accelerometer, On-rotor measurement, Planetary gearbox",
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Mones, Z, Alqatawneh, I, Zhen, D, Gu, F & Ball, AD 2019, Fault Diagnosis for Planetary Gearbox Using On-Rotor MEMS Sensor and EMD Analysis. in H Yu (ed.), 2019 25th IEEE International Conference on Automation and Computing, ICAC 2019 : Improving Productivity through Automation and Computing., 8895155, Institute of Electrical and Electronics Engineers Inc., 25th IEEE International Conference on Automation and Computing, Lancaster, United Kingdom, 5/09/19. https://doi.org/10.23919/IConAC.2019.8895155

Fault Diagnosis for Planetary Gearbox Using On-Rotor MEMS Sensor and EMD Analysis. / Mones, Zainab; Alqatawneh, Ibrahim; Zhen, Dong; Gu, Fengshou; Ball, Andrew D.

2019 25th IEEE International Conference on Automation and Computing, ICAC 2019 : Improving Productivity through Automation and Computing. ed. / Hui Yu. Institute of Electrical and Electronics Engineers Inc., 2019. 8895155.

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

TY - GEN

T1 - Fault Diagnosis for Planetary Gearbox Using On-Rotor MEMS Sensor and EMD Analysis

AU - Mones, Zainab

AU - Alqatawneh, Ibrahim

AU - Zhen, Dong

AU - Gu, Fengshou

AU - Ball, Andrew D.

PY - 2019/11/11

Y1 - 2019/11/11

N2 - Rotating machinery fault diagnosis based on Micro-Electro-Mechanical Systems (MEMS) technologies has recently received considerable attention. The significant advancements in MEMS make it easier and more conceivable to mount a low-cost MEMS sensor directly on the rotating shaft, allowing more accurate dynamic characteristics of the rotor to be obtained for mechanical condition monitoring and fault diagnosis. However, the measured signals contain strong background noise and modulation effect, which results in low signal-to-noise ratio (SNR), and consequently attenuate the accuracy of the diagnosis results. To improve the SNR of the measured signals, a denoising method based on empirical mode decomposition (EMD) is developed in this paper to suppress the background noise and enhance the modulation components for accurate fault feature extraction. Firstly, EMD is applied to decompose the original signal into a list of intrinsic mode functions (IMFs), and then the IMF with the highest correlation coefficient value is selected for further analysis. Finally, the envelop analysis (EA) is employed to demodulate the denoised signal by EMD for fault feature extraction and diagnosis. The experimental results show that the proposed EMD based denoising approach gives a promising result in diagnosing common PG faults.

AB - Rotating machinery fault diagnosis based on Micro-Electro-Mechanical Systems (MEMS) technologies has recently received considerable attention. The significant advancements in MEMS make it easier and more conceivable to mount a low-cost MEMS sensor directly on the rotating shaft, allowing more accurate dynamic characteristics of the rotor to be obtained for mechanical condition monitoring and fault diagnosis. However, the measured signals contain strong background noise and modulation effect, which results in low signal-to-noise ratio (SNR), and consequently attenuate the accuracy of the diagnosis results. To improve the SNR of the measured signals, a denoising method based on empirical mode decomposition (EMD) is developed in this paper to suppress the background noise and enhance the modulation components for accurate fault feature extraction. Firstly, EMD is applied to decompose the original signal into a list of intrinsic mode functions (IMFs), and then the IMF with the highest correlation coefficient value is selected for further analysis. Finally, the envelop analysis (EA) is employed to demodulate the denoised signal by EMD for fault feature extraction and diagnosis. The experimental results show that the proposed EMD based denoising approach gives a promising result in diagnosing common PG faults.

KW - Condition monitoring

KW - EMD

KW - MEMS Accelerometer

KW - On-rotor measurement

KW - Planetary gearbox

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DO - 10.23919/IConAC.2019.8895155

M3 - Conference contribution

AN - SCOPUS:85075776135

SN - 9781728125183

BT - 2019 25th IEEE International Conference on Automation and Computing, ICAC 2019

A2 - Yu, Hui

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Mones Z, Alqatawneh I, Zhen D, Gu F, Ball AD. Fault Diagnosis for Planetary Gearbox Using On-Rotor MEMS Sensor and EMD Analysis. In Yu H, editor, 2019 25th IEEE International Conference on Automation and Computing, ICAC 2019 : Improving Productivity through Automation and Computing. Institute of Electrical and Electronics Engineers Inc. 2019. 8895155 https://doi.org/10.23919/IConAC.2019.8895155