Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

H.O.A. Ahmed; A.K. Nandi

doi:10.1186/s10033-021-00553-8

Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

H.O.A. Ahmed, A.K. Nandi^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

Roller bearing failure is one of the most common faults in rotating machines. Various techniques for bearing fault
diagnosis based on faults feature extraction have been proposed. But feature extraction from fault signals requires
expert prior information and human labour. Recently, deep learning algorithms have been applied extensively in the
condition monitoring of rotating machines to learn features automatically from the input data. Given its robust performance
in image recognition, the convolutional neural network (CNN) architecture has been widely used to learn
automatically discriminative features from vibration images and classify health conditions. This paper proposes and
evaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis. The first stage in the proposed method
is to generate the RGB vibration images (RGBVIs) from the input vibration signals. To begin this process, first, the 1-D
vibration signals were converted to 2-D grayscale vibration Images. Once the conversion was completed, the regions
of interest (ROI) were found in the converted 2-D grayscale vibration images. Finally, to produce vibration images
with more discriminative characteristics, an algorithm was applied to the 2-D grayscale vibration images to produce
connected components-based RGB vibration images (RGBVIs) with sets of colours and texture features. In the second
stage, with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and
to classify bearing health conditions. Two cases of fault classification of rolling element bearings are used to validate
the proposed method. Experimental results of this investigation demonstrate that RGBVI-CNN can generate advantageous
health condition features from bearing vibration signals and classify the health conditions under different
working loads with high accuracy. Moreover, several classification models trained using RGBVI-CNN offered high
performance in the testing results of the overall classification accuracy, precision, recall, and F-score.

Original language	English
Number of pages	21
Journal	Chinese Journal of Mechanical Engineering (English Edition)
Volume	34
Issue number	37
DOIs	https://doi.org/10.1186/s10033-021-00553-8
Publication status	Published - 12 Apr 2021

Bibliographical note

Copyright © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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10.1186/s10033-021-00553-8Licence: CC BY 4.0

Connected components-based colour image representations of vibrations for a two-stage fault diagnosis
Copyright © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Final published version, 3.05 MBLicence: CC BY 4.0

Cite this

@article{2c0f9e331c2647f58b56e29ad0bf5ba3,

title = "Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks",

abstract = "Roller bearing failure is one of the most common faults in rotating machines. Various techniques for bearing faultdiagnosis based on faults feature extraction have been proposed. But feature extraction from fault signals requiresexpert prior information and human labour. Recently, deep learning algorithms have been applied extensively in thecondition monitoring of rotating machines to learn features automatically from the input data. Given its robust performancein image recognition, the convolutional neural network (CNN) architecture has been widely used to learnautomatically discriminative features from vibration images and classify health conditions. This paper proposes andevaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis. The first stage in the proposed methodis to generate the RGB vibration images (RGBVIs) from the input vibration signals. To begin this process, first, the 1-Dvibration signals were converted to 2-D grayscale vibration Images. Once the conversion was completed, the regionsof interest (ROI) were found in the converted 2-D grayscale vibration images. Finally, to produce vibration imageswith more discriminative characteristics, an algorithm was applied to the 2-D grayscale vibration images to produceconnected components-based RGB vibration images (RGBVIs) with sets of colours and texture features. In the secondstage, with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs andto classify bearing health conditions. Two cases of fault classification of rolling element bearings are used to validatethe proposed method. Experimental results of this investigation demonstrate that RGBVI-CNN can generate advantageoushealth condition features from bearing vibration signals and classify the health conditions under differentworking loads with high accuracy. Moreover, several classification models trained using RGBVI-CNN offered highperformance in the testing results of the overall classification accuracy, precision, recall, and F-score.",

author = "H.O.A. Ahmed and A.K. Nandi",

note = "Copyright {\textcopyright} The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.",

year = "2021",

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doi = "10.1186/s10033-021-00553-8",

language = "English",

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journal = "Chinese Journal of Mechanical Engineering (English Edition)",

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AU - Ahmed, H.O.A.

AU - Nandi, A.K.

N1 - Copyright © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

PY - 2021/4/12

Y1 - 2021/4/12

N2 - Roller bearing failure is one of the most common faults in rotating machines. Various techniques for bearing faultdiagnosis based on faults feature extraction have been proposed. But feature extraction from fault signals requiresexpert prior information and human labour. Recently, deep learning algorithms have been applied extensively in thecondition monitoring of rotating machines to learn features automatically from the input data. Given its robust performancein image recognition, the convolutional neural network (CNN) architecture has been widely used to learnautomatically discriminative features from vibration images and classify health conditions. This paper proposes andevaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis. The first stage in the proposed methodis to generate the RGB vibration images (RGBVIs) from the input vibration signals. To begin this process, first, the 1-Dvibration signals were converted to 2-D grayscale vibration Images. Once the conversion was completed, the regionsof interest (ROI) were found in the converted 2-D grayscale vibration images. Finally, to produce vibration imageswith more discriminative characteristics, an algorithm was applied to the 2-D grayscale vibration images to produceconnected components-based RGB vibration images (RGBVIs) with sets of colours and texture features. In the secondstage, with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs andto classify bearing health conditions. Two cases of fault classification of rolling element bearings are used to validatethe proposed method. Experimental results of this investigation demonstrate that RGBVI-CNN can generate advantageoushealth condition features from bearing vibration signals and classify the health conditions under differentworking loads with high accuracy. Moreover, several classification models trained using RGBVI-CNN offered highperformance in the testing results of the overall classification accuracy, precision, recall, and F-score.

AB - Roller bearing failure is one of the most common faults in rotating machines. Various techniques for bearing faultdiagnosis based on faults feature extraction have been proposed. But feature extraction from fault signals requiresexpert prior information and human labour. Recently, deep learning algorithms have been applied extensively in thecondition monitoring of rotating machines to learn features automatically from the input data. Given its robust performancein image recognition, the convolutional neural network (CNN) architecture has been widely used to learnautomatically discriminative features from vibration images and classify health conditions. This paper proposes andevaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis. The first stage in the proposed methodis to generate the RGB vibration images (RGBVIs) from the input vibration signals. To begin this process, first, the 1-Dvibration signals were converted to 2-D grayscale vibration Images. Once the conversion was completed, the regionsof interest (ROI) were found in the converted 2-D grayscale vibration images. Finally, to produce vibration imageswith more discriminative characteristics, an algorithm was applied to the 2-D grayscale vibration images to produceconnected components-based RGB vibration images (RGBVIs) with sets of colours and texture features. In the secondstage, with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs andto classify bearing health conditions. Two cases of fault classification of rolling element bearings are used to validatethe proposed method. Experimental results of this investigation demonstrate that RGBVI-CNN can generate advantageoushealth condition features from bearing vibration signals and classify the health conditions under differentworking loads with high accuracy. Moreover, several classification models trained using RGBVI-CNN offered highperformance in the testing results of the overall classification accuracy, precision, recall, and F-score.

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Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

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