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Automated Harmonic Signal Removal-Based Image Feature Extraction Technique: A Comparative Study Using Online Databases

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Intelligent Manufacturing and Mechatronics (iM3F 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 850))

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Abstract

This paper presents an automated harmonic removal technique as an efficient method for identifying and removing the influence of harmonics from the output signal. The method involves disregarding user-defined parameters during system initialization and reconstructing the output signal automatically so that it can be used for system identification. While stochastic subspace-based algorithms (SSI) are generally reliable for modal parameter estimation, applying them to structures with rotating machinery and harmonic excitations presents challenges. Because the SSI method necessitates designating parameters, such as the maximal within-cluster distance, at the outset of each dataset analysis, the issue remains unresolved. In addition to modal identification, the current research concentrates on image-based feature extraction for aggregating and classifying harmonic components and structural poles directly from a stabilization diagram. Utilizing online data sets to validate the algorithm’s efficacy. Using a comparative analysis, the proposed method is compared to existing techniques, namely orthogonal projection-based harmonic signal removal and smoothing techniques based on linear interpolation. The results indicate that the proposed algorithm estimates modal parameters precisely and consistently both before and after the removal of harmonic components from the response signal.

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Acknowledgements

The authors are extremely grateful to Universiti Teknologi Malaysia (UTM) for its financial support via university research grant (Q.J130000.3824.31J47). In addition, the authors would like to express their gratitude to Institute of Noise and Vibration UTM for funding this research under the Higher Institution Centre of Excellence (HICoE) Grant Scheme (R.K130000.7809.4J226).

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Authors and Affiliations

  1. Faculty of Mechanical Engineering, Universiti Teknologi Malaysia (UTM), Skudai, Malaysia

    Muhammad Danial Abu Hasan, Syahril Ramadhan Saufi, M. Firdaus Isham, Shaharil Mad Saad, W. Aliff A. Saad, Zair Asrar Bin Ahmad & Mat Hussin Ab Talib

  2. Institue of Noise and Vibration, Universiti Teknologi Malaysia (UTM), Kuala Lumpur, Malaysia

    Muhammad Danial Abu Hasan, Mohd Salman Leong, Lim Meng Hee & M. Haffizzi Md. Idris

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  1. Muhammad Danial Abu Hasan
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Correspondence to Muhammad Danial Abu Hasan .

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Editors and Affiliations

  1. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Malaysia

    Wan Hasbullah Mohd. Isa

  2. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Malaysia

    Ismail Mohd. Khairuddin

  3. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Malaysia

    Mohd. Azraai Mohd. Razman

  4. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Malaysia

    Sarah 'Atifah Saruchi

  5. School of Intelligent Manufacturing Ecosystem, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Sze-Hong Teh

  6. Department of Computer Science, University of York, York, UK

    Pengcheng Liu

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Abu Hasan, M.D. et al. (2024). Automated Harmonic Signal Removal-Based Image Feature Extraction Technique: A Comparative Study Using Online Databases. In: Mohd. Isa, W.H., Khairuddin, I.M., Mohd. Razman, M.A., Saruchi, S.'., Teh, SH., Liu, P. (eds) Intelligent Manufacturing and Mechatronics. iM3F 2023. Lecture Notes in Networks and Systems, vol 850. Springer, Singapore. https://doi.org/10.1007/978-981-99-8819-8_17

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  • DOI: https://doi.org/10.1007/978-981-99-8819-8_17

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