A Novel Wavelet Thresholding Method for Vibration Data Denoising and Diagnostic Feature Enhancement in Condition Monitoring

Khalid Rabeyee, Yuandong Xu, Fengshou Gu, Andrew D. Ball

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

Abstract

Data denoising is essential to extract meaningful information and enhance diagnostic features in condition monitoring (CM). Hence, many techniques have been proposed for vibration data denoising. However, manual techniques require well-skilled labourers who will increase the cost of the monitoring process and may not always be available at the site. Denoising in the transformation domain using wavelets by thresholding has attracted researchers in recent years and been shown to be more effective than conventional filtering methods. However, estimation of the threshold value that can effectively suppress the noise has always been a hot research topic. One of the most promising approaches is to automate the process using adaptive and optimization methodologies. A new denoising method is proposed based on adaptive wavelet coefficients thresholding: a thresholding function is constructed, it adopts an adaptive procedure to estimate the initial threshold value based on the energy distribution of the signal; then the optimization procedure is adopted and the optimal threshold is selected through an iterative procedure by maximizing the harmonic-to-signal ratio (HSR). The new method is applied to both simulated data and real datasets collected from a defective tapered roller bearing and it is found that the proposed method increases the signal-to-noise ratio (SNR) significantly without distorting the signal when compared to benchmark thresholding methods.

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

Wavelet Thresholding
Condition Monitoring
Condition monitoring
Denoising
Thresholding
Diagnostics
Enhancement
Vibration
Tapered roller bearings
Process monitoring
Signal to noise ratio
Threshold Value
Adaptive Processes
Adaptive Procedure
Process Monitoring
Optimization
Energy Distribution
Wavelet Coefficients
Costs
Iterative Procedure

Cite this

Rabeyee, K., Xu, Y., Gu, F., & Ball, A. D. (2019). A Novel Wavelet Thresholding Method for Vibration Data Denoising and Diagnostic Feature Enhancement in Condition Monitoring. In H. Yu (Ed.), 2019 25th IEEE International Conference on Automation and Computing, ICAC 2019: Improving Productivity through Automation and Computing [8894986] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/IConAC.2019.8894986
Rabeyee, Khalid ; Xu, Yuandong ; Gu, Fengshou ; Ball, Andrew D. / A Novel Wavelet Thresholding Method for Vibration Data Denoising and Diagnostic Feature Enhancement in Condition Monitoring. 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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abstract = "Data denoising is essential to extract meaningful information and enhance diagnostic features in condition monitoring (CM). Hence, many techniques have been proposed for vibration data denoising. However, manual techniques require well-skilled labourers who will increase the cost of the monitoring process and may not always be available at the site. Denoising in the transformation domain using wavelets by thresholding has attracted researchers in recent years and been shown to be more effective than conventional filtering methods. However, estimation of the threshold value that can effectively suppress the noise has always been a hot research topic. One of the most promising approaches is to automate the process using adaptive and optimization methodologies. A new denoising method is proposed based on adaptive wavelet coefficients thresholding: a thresholding function is constructed, it adopts an adaptive procedure to estimate the initial threshold value based on the energy distribution of the signal; then the optimization procedure is adopted and the optimal threshold is selected through an iterative procedure by maximizing the harmonic-to-signal ratio (HSR). The new method is applied to both simulated data and real datasets collected from a defective tapered roller bearing and it is found that the proposed method increases the signal-to-noise ratio (SNR) significantly without distorting the signal when compared to benchmark thresholding methods.",
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Rabeyee, K, Xu, Y, Gu, F & Ball, AD 2019, A Novel Wavelet Thresholding Method for Vibration Data Denoising and Diagnostic Feature Enhancement in Condition Monitoring. in H Yu (ed.), 2019 25th IEEE International Conference on Automation and Computing, ICAC 2019: Improving Productivity through Automation and Computing., 8894986, 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.2019.8894986

A Novel Wavelet Thresholding Method for Vibration Data Denoising and Diagnostic Feature Enhancement in Condition Monitoring. / Rabeyee, Khalid; Xu, Yuandong; 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. 8894986.

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

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AB - Data denoising is essential to extract meaningful information and enhance diagnostic features in condition monitoring (CM). Hence, many techniques have been proposed for vibration data denoising. However, manual techniques require well-skilled labourers who will increase the cost of the monitoring process and may not always be available at the site. Denoising in the transformation domain using wavelets by thresholding has attracted researchers in recent years and been shown to be more effective than conventional filtering methods. However, estimation of the threshold value that can effectively suppress the noise has always been a hot research topic. One of the most promising approaches is to automate the process using adaptive and optimization methodologies. A new denoising method is proposed based on adaptive wavelet coefficients thresholding: a thresholding function is constructed, it adopts an adaptive procedure to estimate the initial threshold value based on the energy distribution of the signal; then the optimization procedure is adopted and the optimal threshold is selected through an iterative procedure by maximizing the harmonic-to-signal ratio (HSR). The new method is applied to both simulated data and real datasets collected from a defective tapered roller bearing and it is found that the proposed method increases the signal-to-noise ratio (SNR) significantly without distorting the signal when compared to benchmark thresholding methods.

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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.

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Rabeyee K, Xu Y, Gu F, Ball AD. A Novel Wavelet Thresholding Method for Vibration Data Denoising and Diagnostic Feature Enhancement in Condition Monitoring. 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. 8894986 https://doi.org/10.23919/IConAC.2019.8894986