An Effective Combination of Pre-Processing Technique and Deep Learning Algorithm for Hammering Sound Inspection

Balage Don Hiroshan Lakmal; Daisuke Oka; Yoichi Shiraishi; Kazuhiro Motegi

doi:doi:10.11648/j.ajnna.20180401.13

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An Effective Combination of Pre-Processing Technique and Deep Learning Algorithm for Hammering Sound Inspection

Balage Don Hiroshan Lakmal, Daisuke Oka, Yoichi Shiraishi, Kazuhiro Motegi

Published in American Journal of Neural Networks and Applications (Volume 4, Issue 1)

Received: 3 July 2018 Accepted: 6 August 2018 Published: 1 September 2018

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Abstract

This paper deals with the identification problem of defective products of door strikers installed in automobiles based on their hammering sounds. The difference of the hammering sounds between defective and acceptable products is very small and each sound signal has a unique pattern. The capabilities of conventional human sensory tests are not enough to identify such differences between these two classes. Hence it is suggested to apply deep learning algorithms (DLA) as per the versatility and feature extraction power. Usually, some kinds of pre-processing are adopted before the application of DLA in order to increase the accuracy of inspection as well as to reduce the training and the application time of DLA. In this paper, the combinations of five kinds of pre-processing techniques and three types of DLAs are applied to the actual hammering sounds inspection of door strikers. Especially in two types of DLAs, the sound data have been evaluated as images. The evaluation results show that the combination of the wavelet analysis and the Convolutional Neural Network (CNN) only attained the 100% accuracy of inspection with great response time. The wavelet analysis and the CNN are independently attain the high performances comparing with others and it is likely that they are useful in this kind of hammering sound inspections.

Published in	American Journal of Neural Networks and Applications (Volume 4, Issue 1)
DOI	10.11648/j.ajnna.20180401.13
Page(s)	15-23
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Pre-Processing, Deep Learning Algorithms, Non-Destructive Testing, Door Striker, Convolutional Neural Network, Wavelet Analysis

References

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APA Style

Balage Don Hiroshan Lakmal, Daisuke Oka, Yoichi Shiraishi, Kazuhiro Motegi. (2018). An Effective Combination of Pre-Processing Technique and Deep Learning Algorithm for Hammering Sound Inspection. American Journal of Neural Networks and Applications, 4(1), 15-23. https://doi.org/10.11648/j.ajnna.20180401.13

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ACS Style

Balage Don Hiroshan Lakmal; Daisuke Oka; Yoichi Shiraishi; Kazuhiro Motegi. An Effective Combination of Pre-Processing Technique and Deep Learning Algorithm for Hammering Sound Inspection. Am. J. Neural Netw. Appl. 2018, 4(1), 15-23. doi: 10.11648/j.ajnna.20180401.13

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AMA Style

Balage Don Hiroshan Lakmal, Daisuke Oka, Yoichi Shiraishi, Kazuhiro Motegi. An Effective Combination of Pre-Processing Technique and Deep Learning Algorithm for Hammering Sound Inspection. Am J Neural Netw Appl. 2018;4(1):15-23. doi: 10.11648/j.ajnna.20180401.13

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@article{10.11648/j.ajnna.20180401.13,
  author = {Balage Don Hiroshan Lakmal and Daisuke Oka and Yoichi Shiraishi and Kazuhiro Motegi},
  title = {An Effective Combination of Pre-Processing Technique and Deep Learning Algorithm for Hammering Sound Inspection},
  journal = {American Journal of Neural Networks and Applications},
  volume = {4},
  number = {1},
  pages = {15-23},
  doi = {10.11648/j.ajnna.20180401.13},
  url = {https://doi.org/10.11648/j.ajnna.20180401.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20180401.13},
  abstract = {This paper deals with the identification problem of defective products of door strikers installed in automobiles based on their hammering sounds. The difference of the hammering sounds between defective and acceptable products is very small and each sound signal has a unique pattern. The capabilities of conventional human sensory tests are not enough to identify such differences between these two classes. Hence it is suggested to apply deep learning algorithms (DLA) as per the versatility and feature extraction power. Usually, some kinds of pre-processing are adopted before the application of DLA in order to increase the accuracy of inspection as well as to reduce the training and the application time of DLA. In this paper, the combinations of five kinds of pre-processing techniques and three types of DLAs are applied to the actual hammering sounds inspection of door strikers. Especially in two types of DLAs, the sound data have been evaluated as images. The evaluation results show that the combination of the wavelet analysis and the Convolutional Neural Network (CNN) only attained the 100% accuracy of inspection with great response time. The wavelet analysis and the CNN are independently attain the high performances comparing with others and it is likely that they are useful in this kind of hammering sound inspections.},
 year = {2018}
}

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TY  - JOUR
T1  - An Effective Combination of Pre-Processing Technique and Deep Learning Algorithm for Hammering Sound Inspection
AU  - Balage Don Hiroshan Lakmal
AU  - Daisuke Oka
AU  - Yoichi Shiraishi
AU  - Kazuhiro Motegi
Y1  - 2018/09/01
PY  - 2018
N1  - https://doi.org/10.11648/j.ajnna.20180401.13
DO  - 10.11648/j.ajnna.20180401.13
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 15
EP  - 23
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20180401.13
AB  - This paper deals with the identification problem of defective products of door strikers installed in automobiles based on their hammering sounds. The difference of the hammering sounds between defective and acceptable products is very small and each sound signal has a unique pattern. The capabilities of conventional human sensory tests are not enough to identify such differences between these two classes. Hence it is suggested to apply deep learning algorithms (DLA) as per the versatility and feature extraction power. Usually, some kinds of pre-processing are adopted before the application of DLA in order to increase the accuracy of inspection as well as to reduce the training and the application time of DLA. In this paper, the combinations of five kinds of pre-processing techniques and three types of DLAs are applied to the actual hammering sounds inspection of door strikers. Especially in two types of DLAs, the sound data have been evaluated as images. The evaluation results show that the combination of the wavelet analysis and the Convolutional Neural Network (CNN) only attained the 100% accuracy of inspection with great response time. The wavelet analysis and the CNN are independently attain the high performances comparing with others and it is likely that they are useful in this kind of hammering sound inspections.
VL  - 4
IS  - 1
ER  -

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Author Information

Balage Don Hiroshan Lakmal

Department of Science and Engineering, Graduate School of Science and Technology, Gunma University, Gunma, Japan
Daisuke Oka

Department of Science and Engineering, Graduate School of Science and Technology, Gunma University, Gunma, Japan
Yoichi Shiraishi

Department of Science and Engineering, Graduate School of Science and Technology, Gunma University, Gunma, Japan
Kazuhiro Motegi

Department of Science and Engineering, Graduate School of Science and Technology, Gunma University, Gunma, Japan

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APA Style

Balage Don Hiroshan Lakmal, Daisuke Oka, Yoichi Shiraishi, Kazuhiro Motegi. (2018). An Effective Combination of Pre-Processing Technique and Deep Learning Algorithm for Hammering Sound Inspection. American Journal of Neural Networks and Applications, 4(1), 15-23. https://doi.org/10.11648/j.ajnna.20180401.13

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ACS Style

Balage Don Hiroshan Lakmal; Daisuke Oka; Yoichi Shiraishi; Kazuhiro Motegi. An Effective Combination of Pre-Processing Technique and Deep Learning Algorithm for Hammering Sound Inspection. Am. J. Neural Netw. Appl. 2018, 4(1), 15-23. doi: 10.11648/j.ajnna.20180401.13

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AMA Style

Balage Don Hiroshan Lakmal, Daisuke Oka, Yoichi Shiraishi, Kazuhiro Motegi. An Effective Combination of Pre-Processing Technique and Deep Learning Algorithm for Hammering Sound Inspection. Am J Neural Netw Appl. 2018;4(1):15-23. doi: 10.11648/j.ajnna.20180401.13

Copy | Download

@article{10.11648/j.ajnna.20180401.13,
  author = {Balage Don Hiroshan Lakmal and Daisuke Oka and Yoichi Shiraishi and Kazuhiro Motegi},
  title = {An Effective Combination of Pre-Processing Technique and Deep Learning Algorithm for Hammering Sound Inspection},
  journal = {American Journal of Neural Networks and Applications},
  volume = {4},
  number = {1},
  pages = {15-23},
  doi = {10.11648/j.ajnna.20180401.13},
  url = {https://doi.org/10.11648/j.ajnna.20180401.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20180401.13},
  abstract = {This paper deals with the identification problem of defective products of door strikers installed in automobiles based on their hammering sounds. The difference of the hammering sounds between defective and acceptable products is very small and each sound signal has a unique pattern. The capabilities of conventional human sensory tests are not enough to identify such differences between these two classes. Hence it is suggested to apply deep learning algorithms (DLA) as per the versatility and feature extraction power. Usually, some kinds of pre-processing are adopted before the application of DLA in order to increase the accuracy of inspection as well as to reduce the training and the application time of DLA. In this paper, the combinations of five kinds of pre-processing techniques and three types of DLAs are applied to the actual hammering sounds inspection of door strikers. Especially in two types of DLAs, the sound data have been evaluated as images. The evaluation results show that the combination of the wavelet analysis and the Convolutional Neural Network (CNN) only attained the 100% accuracy of inspection with great response time. The wavelet analysis and the CNN are independently attain the high performances comparing with others and it is likely that they are useful in this kind of hammering sound inspections.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - An Effective Combination of Pre-Processing Technique and Deep Learning Algorithm for Hammering Sound Inspection
AU  - Balage Don Hiroshan Lakmal
AU  - Daisuke Oka
AU  - Yoichi Shiraishi
AU  - Kazuhiro Motegi
Y1  - 2018/09/01
PY  - 2018
N1  - https://doi.org/10.11648/j.ajnna.20180401.13
DO  - 10.11648/j.ajnna.20180401.13
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 15
EP  - 23
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20180401.13
AB  - This paper deals with the identification problem of defective products of door strikers installed in automobiles based on their hammering sounds. The difference of the hammering sounds between defective and acceptable products is very small and each sound signal has a unique pattern. The capabilities of conventional human sensory tests are not enough to identify such differences between these two classes. Hence it is suggested to apply deep learning algorithms (DLA) as per the versatility and feature extraction power. Usually, some kinds of pre-processing are adopted before the application of DLA in order to increase the accuracy of inspection as well as to reduce the training and the application time of DLA. In this paper, the combinations of five kinds of pre-processing techniques and three types of DLAs are applied to the actual hammering sounds inspection of door strikers. Especially in two types of DLAs, the sound data have been evaluated as images. The evaluation results show that the combination of the wavelet analysis and the Convolutional Neural Network (CNN) only attained the 100% accuracy of inspection with great response time. The wavelet analysis and the CNN are independently attain the high performances comparing with others and it is likely that they are useful in this kind of hammering sound inspections.
VL  - 4
IS  - 1
ER  -

Copy | Download