Extraction of Information From Vibration Data Using Double Density Discrete Wavelet Analysis for Condition Monitoring

Khalid Rabeyee, Yuandong Xu, Samir Alabied, Fengshou Gu, Andrew Ball

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

Abstract

Many condition monitoring (CM) techniques have been investigated for the purpose of early fault detection and diagnosis in order to avoid unexpected machine breakdowns. However, non-stationary and non-linear characteristics of vibration data can make the signal analysis a challenging task. Multiresolution data analysis approaches have received significant attention in recent years and are widely applied to analyse non-stationary and non-linear data. Double-Density Discrete Wavelet Transform (DD-DWT), which was originally developed for image processing, is proposed and investigated in this paper for effectively extracting diagnostic features from the vibration measurements. DD-DWT has the merits of nearly shift-invariant and less frequency aliasing which and allows the effective extraction of non-stationary periodic peaks, compared with the undecimated DWT. Techniques based on thresholding of wavelet coefficients are gaining popularity for denoising data. The implementation of global, level-dependent, and subband-dependent thresholding based methods are investigated and implemented on the selected wavelet coefficients in order to denoise and enhance the periodic and impulsive fault features. The performance of the proposed method has been evaluated against DWT using both simulated data and experimental datasets from defective tapered roller bearings. Results, using the harmonic to signal ratio (HSR) as a measure, have demonstrated that DD-DWT outperforms conventional DWT in feature extraction and noise suppression. As a result, the proposed method is robust and effective in fault detection and diagnosis.
Original languageEnglish
Title of host publicationSixteenth International Conference on Condition Monitoring and Asset Management (CM 2019)
PublisherBritish Institute of Non-Destructive Testing
ISBN (Print)9781510889774
Publication statusPublished - 1 Aug 2019
Event16th International Conference on Condition Monitoring and Asset Management - The Principle Grand Central Hotel, Glasgow, United Kingdom
Duration: 25 Jun 201927 Jun 2019
Conference number: 16
https://www.bindt.org/events/CM2019/

Conference

Conference16th International Conference on Condition Monitoring and Asset Management
Abbreviated titleCM2019
CountryUnited Kingdom
CityGlasgow
Period25/06/1927/06/19
Internet address

Fingerprint

Wavelet analysis
Discrete wavelet transforms
Condition monitoring
Fault detection
Failure analysis
Tapered roller bearings
Vibration measurement
Signal analysis
Feature extraction
Image processing

Cite this

Rabeyee, K., Xu, Y., Alabied, S., Gu, F., & Ball, A. (2019). Extraction of Information From Vibration Data Using Double Density Discrete Wavelet Analysis for Condition Monitoring. In Sixteenth International Conference on Condition Monitoring and Asset Management (CM 2019) [149467] British Institute of Non-Destructive Testing.
Rabeyee, Khalid ; Xu, Yuandong ; Alabied, Samir ; Gu, Fengshou ; Ball, Andrew. / Extraction of Information From Vibration Data Using Double Density Discrete Wavelet Analysis for Condition Monitoring. Sixteenth International Conference on Condition Monitoring and Asset Management (CM 2019) . British Institute of Non-Destructive Testing, 2019.
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abstract = "Many condition monitoring (CM) techniques have been investigated for the purpose of early fault detection and diagnosis in order to avoid unexpected machine breakdowns. However, non-stationary and non-linear characteristics of vibration data can make the signal analysis a challenging task. Multiresolution data analysis approaches have received significant attention in recent years and are widely applied to analyse non-stationary and non-linear data. Double-Density Discrete Wavelet Transform (DD-DWT), which was originally developed for image processing, is proposed and investigated in this paper for effectively extracting diagnostic features from the vibration measurements. DD-DWT has the merits of nearly shift-invariant and less frequency aliasing which and allows the effective extraction of non-stationary periodic peaks, compared with the undecimated DWT. Techniques based on thresholding of wavelet coefficients are gaining popularity for denoising data. The implementation of global, level-dependent, and subband-dependent thresholding based methods are investigated and implemented on the selected wavelet coefficients in order to denoise and enhance the periodic and impulsive fault features. The performance of the proposed method has been evaluated against DWT using both simulated data and experimental datasets from defective tapered roller bearings. Results, using the harmonic to signal ratio (HSR) as a measure, have demonstrated that DD-DWT outperforms conventional DWT in feature extraction and noise suppression. As a result, the proposed method is robust and effective in fault detection and diagnosis.",
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Rabeyee, K, Xu, Y, Alabied, S, Gu, F & Ball, A 2019, Extraction of Information From Vibration Data Using Double Density Discrete Wavelet Analysis for Condition Monitoring. in Sixteenth International Conference on Condition Monitoring and Asset Management (CM 2019) ., 149467, British Institute of Non-Destructive Testing, 16th International Conference on Condition Monitoring and Asset Management, Glasgow, United Kingdom, 25/06/19.

Extraction of Information From Vibration Data Using Double Density Discrete Wavelet Analysis for Condition Monitoring. / Rabeyee, Khalid; Xu, Yuandong; Alabied, Samir; Gu, Fengshou; Ball, Andrew.

Sixteenth International Conference on Condition Monitoring and Asset Management (CM 2019) . British Institute of Non-Destructive Testing, 2019. 149467.

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

TY - GEN

T1 - Extraction of Information From Vibration Data Using Double Density Discrete Wavelet Analysis for Condition Monitoring

AU - Rabeyee, Khalid

AU - Xu, Yuandong

AU - Alabied, Samir

AU - Gu, Fengshou

AU - Ball, Andrew

PY - 2019/8/1

Y1 - 2019/8/1

N2 - Many condition monitoring (CM) techniques have been investigated for the purpose of early fault detection and diagnosis in order to avoid unexpected machine breakdowns. However, non-stationary and non-linear characteristics of vibration data can make the signal analysis a challenging task. Multiresolution data analysis approaches have received significant attention in recent years and are widely applied to analyse non-stationary and non-linear data. Double-Density Discrete Wavelet Transform (DD-DWT), which was originally developed for image processing, is proposed and investigated in this paper for effectively extracting diagnostic features from the vibration measurements. DD-DWT has the merits of nearly shift-invariant and less frequency aliasing which and allows the effective extraction of non-stationary periodic peaks, compared with the undecimated DWT. Techniques based on thresholding of wavelet coefficients are gaining popularity for denoising data. The implementation of global, level-dependent, and subband-dependent thresholding based methods are investigated and implemented on the selected wavelet coefficients in order to denoise and enhance the periodic and impulsive fault features. The performance of the proposed method has been evaluated against DWT using both simulated data and experimental datasets from defective tapered roller bearings. Results, using the harmonic to signal ratio (HSR) as a measure, have demonstrated that DD-DWT outperforms conventional DWT in feature extraction and noise suppression. As a result, the proposed method is robust and effective in fault detection and diagnosis.

AB - Many condition monitoring (CM) techniques have been investigated for the purpose of early fault detection and diagnosis in order to avoid unexpected machine breakdowns. However, non-stationary and non-linear characteristics of vibration data can make the signal analysis a challenging task. Multiresolution data analysis approaches have received significant attention in recent years and are widely applied to analyse non-stationary and non-linear data. Double-Density Discrete Wavelet Transform (DD-DWT), which was originally developed for image processing, is proposed and investigated in this paper for effectively extracting diagnostic features from the vibration measurements. DD-DWT has the merits of nearly shift-invariant and less frequency aliasing which and allows the effective extraction of non-stationary periodic peaks, compared with the undecimated DWT. Techniques based on thresholding of wavelet coefficients are gaining popularity for denoising data. The implementation of global, level-dependent, and subband-dependent thresholding based methods are investigated and implemented on the selected wavelet coefficients in order to denoise and enhance the periodic and impulsive fault features. The performance of the proposed method has been evaluated against DWT using both simulated data and experimental datasets from defective tapered roller bearings. Results, using the harmonic to signal ratio (HSR) as a measure, have demonstrated that DD-DWT outperforms conventional DWT in feature extraction and noise suppression. As a result, the proposed method is robust and effective in fault detection and diagnosis.

KW - Failure analysis

KW - Fault Detection

KW - Planetary gearbox

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UR - https://www.bindt.org/events/CM2019/

M3 - Conference contribution

SN - 9781510889774

BT - Sixteenth International Conference on Condition Monitoring and Asset Management (CM 2019)

PB - British Institute of Non-Destructive Testing

ER -

Rabeyee K, Xu Y, Alabied S, Gu F, Ball A. Extraction of Information From Vibration Data Using Double Density Discrete Wavelet Analysis for Condition Monitoring. In Sixteenth International Conference on Condition Monitoring and Asset Management (CM 2019) . British Institute of Non-Destructive Testing. 2019. 149467