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Structural Monitoring and Maintenance Volume 7, Number 1, March 2020, pages 13-25 DOI: https://doi.org/10.12989/smm.2020.7.1.013 |
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 open accessAnalysis of acoustic emission signals during fatigue testing of a M36 bolt using the Hilbert-Huang spectrum |
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Felix Leaman, Aljoscha Herz, Victoria Brinnel,
Ralph Baltes and Elisabeth Clausen
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| Abstract | ||
| One of the most important aspects in structural health monitoring is the detection of fatigue damage. Structural components such as heavy-duty bolts work under high dynamic loads, and thus are prone to accumulate fatigue damage and cracks may originate. Those heavy-duty bolts are used, for example, in wind power generation and mining equipment. Therefore, the investigation of new and more effective monitoring technologies attracts a great interest. In this study the acoustic emission (AE) technology was employed to detect incipient damage during fatigue testing of a M36 bolt. Initial results showed that the AE signals have a high level of background noise due to how the load is applied by the fatigue testing machine. Thus, an advanced signal processing method in the time-frequency domain, the Hilbert-Huang Spectrum (HHS), was applied to reveal AE components buried in background noise in form of high-frequency peaks that can be associated with damage progression. Accordingly, the main contribution of the present study is providing insights regarding the detection of incipient damage during fatigue testing using AE signals and providing recommendations for further research. | ||
| Key Words | ||
| acoustic emission; empirical mode decomposition; Hilbert-Huang spectrum; crack detection; structural health monitoring | ||
| Address | ||
| Felix Leaman, Aljoscha Herz, Ralph Baltes and Elisabeth Clausen: Institute for Advanced Mining Technologies, RWTH Aachen University, Germany Victoria Brinnel: Institute for Ferrous Metallurgy, RWTH Aachen University, Germany | ||
