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Vibration Signal Evaluation Based on K-Means Clustering as a Pre-Stage of Operational Modal Analysis for Structural Health Monitoring of Rotating Machines

Author

Listed:
  • Nathali Rolon Dreher

    (School of Mechanical Engineering, University of Campinas, 200 Mendeleyev Street, Campinas 13083-860, Brazil)

  • Gustavo Chaves Storti

    (School of Mechanical Engineering, University of Campinas, 200 Mendeleyev Street, Campinas 13083-860, Brazil)

  • Tiago Henrique Machado

    (School of Mechanical Engineering, University of Campinas, 200 Mendeleyev Street, Campinas 13083-860, Brazil)

Abstract

Rotating machines are key components in energy generation processes, and faults can lead to shutdowns or catastrophes encompassing economic and social losses. Structural Health Monitoring (SHM) of structures in operation is successfully performed via Operational Modal Analysis (OMA), which has advantages over traditional methods. In OMA, white noise inputs lead to the accurate extraction of modal parameters without taking the system out of operation. However, this excitation condition is not easy to attain for rotating machines used in power generation, and OMA can provide inaccurate information. This research investigates the applicability of machine learning as a pre-stage of OMA to differentiate adequate from inadequate excitations and prevent inaccurate extraction of modal parameters. Data from a rotor system was collected under different conditions and OMA was applied. In a training stage, measurements were characterized by statistical features and K-means was used to determine which features provided information about the excitation condition, that is, which excitation was adequate to extract the rotor’s modal parameters via OMA. In a testing stage, data were successfully classified as adequate or not adequate for OMA, achieving 100% accuracy and revealing the technique’s potential to support SHM of rotating machines. The technique is extendable to other monitoring systems based on OMA.

Suggested Citation

  • Nathali Rolon Dreher & Gustavo Chaves Storti & Tiago Henrique Machado, 2023. "Vibration Signal Evaluation Based on K-Means Clustering as a Pre-Stage of Operational Modal Analysis for Structural Health Monitoring of Rotating Machines," Energies, MDPI, vol. 16(23), pages 1-14, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7848-:d:1291056
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    References listed on IDEAS

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    1. Gangrou Wu & Min He & Peng Liang & Chunsheng Ye & Yue Xu, 2020. "Automated Modal Identification Based on Improved Clustering Method," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-16, April.
    2. João Pacheco & Gustavo Oliveira & Filipe Magalhães & Carlos Moutinho & Álvaro Cunha, 2021. "Vibration-Based Monitoring of Wind Turbines: Influence of Layout and Noise of Sensors," Energies, MDPI, vol. 14(2), pages 1-19, January.
    3. Francisco Pimenta & Carlo Ruzzo & Giuseppe Failla & Felice Arena & Marco Alves & Filipe Magalhães, 2020. "Dynamic Response Characterization of Floating Structures Based on Numerical Simulations," Energies, MDPI, vol. 13(21), pages 1-18, October.
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