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Operational Damage Identification Scheme Utilizing De-Noised Frequency Response Functions and Artificial Neural Network

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Abstract

A damage identification scheme combining impact-synchronous modal analysis (ISMA) and artificial neural network is developed in this study. The ISMA de-noising method makes it feasible to detect and classify the damage states with high accuracy when the machine is under operation. The feed-forward backprop network was utilized in this study. The input feature vector of the network consisted of the FRF changes in a selected vibrational mode frequency interval at several measurement points. The scheme was tested on a rectangular Perspex plate. It is proved that the trained network can successfully identify damage locations with the testing data collected by ISMA, which allows the damage detection to be carried out without shutting down the tested machine. For the plate structure in this study, an overall accuracy reached 100% when all five measurement points were used. With the input features optimized by mode shape assessment, 100% accuracy was also achieved with only two measurement points.

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Acknowledgements

The authors wish to acknowledge the financial support and advice given by University of Malaya RU Geran (GPF001A-2018), Impact-Oriented Interdisciplinary Research Grant (IIRG007B-2019), Advanced Shock and Vibration Research (ASVR) Group of University of Malaya, and other project collaborators.

Funding

University of Malaya RU Geran (GPF001A-2018), Impact-Oriented Interdisciplinary Research Grant (IIRG007B-2019).

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Authors and Affiliations

  1. Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Shilei Chen & Zhi Chao Ong

  2. State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, 300350, People’s Republic of China

    Wei Haur Lam

  3. Photonics Research Centre, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Kok-Sing Lim

  4. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Khin Wee Lai

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  1. Shilei Chen
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  2. Zhi Chao Ong
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  3. Wei Haur Lam
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  4. Kok-Sing Lim
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  5. Khin Wee Lai
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Correspondence to Zhi Chao Ong.

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Chen, S., Ong, Z.C., Lam, W.H. et al. Operational Damage Identification Scheme Utilizing De-Noised Frequency Response Functions and Artificial Neural Network. J Nondestruct Eval 39, 66 (2020). https://doi.org/10.1007/s10921-020-00709-x

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