Abstract
The vibration condition monitoring is the process of monitoring the vibration signals in machinery to identify a significant change in the development of fault. Gears are important rotary devices for power and torque transmission. Study on gear teeth relationship is considered as one of the most complicated applications because the speed, load conditions, and application cause different failures, leading to non-stationary operating conditions. Hence, an appropriate signal processing technique to identify the gear fault diagnosis plays a vital role in condition monitoring system. This work attempts the Hilbert–Huang transform (HHT) to identify the effect of the new time–frequency distribution, which increases the performance of fault diagnosis in gear. Also, the method using HHT is compared with fast Fourier transform (FFT). As a novel approach, the statistical feature called energy was calculated for all intrinsic mode functions (IMF) obtained from the empirical mode decomposition (EMD) of the signal which is suitable for the selection of IMF for applying HHT. The fault diagnosis of gear is done clearly using the present approach.
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Krishnakumari, A., Saravanan, M., Ramakrishnan, M., Ponnuri, S.M., Srinadh, R. (2020). Vibration Condition Monitoring of Spur Gear Using Feature Extraction of EMD and Hilbert–Huang Transform. In: Reddy, A., Marla, D., Simic, M., Favorskaya, M., Satapathy, S. (eds) Intelligent Manufacturing and Energy Sustainability. Smart Innovation, Systems and Technologies, vol 169. Springer, Singapore. https://doi.org/10.1007/978-981-15-1616-0_13
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DOI: https://doi.org/10.1007/978-981-15-1616-0_13
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