Abstract
The vibration signals generated during the operation of the blast furnace opener contain various noises, which are disturbing and superimposed, and it is difficult to identify the operating status of the open machine, put forward a kind of based on Empirical Mode Decomposition (EMD) filter method. From the physical structure of the opening machine, the complexity of the vibration signal is qualitatively analyzed. The EMD technology is used to adaptively decompose the vibration signal into a single intrinsic mode function (IMF) with different frequency components. the high frequency noise components is filtered in the IMF component, the remaining IMF components are reconstructed to form a new vibration signal and compared with the results of the wavelet threshold denoising way. The consequences show that the EMD filtering method can overcome the disadvantages of glitches and signal superposition after wavelet denoising, and can fully preserve the nonlinear characteristics of the vibration signal. It is an effective method for filtering and denoising detection of mechanical vibration signals of blast furnace opening machines.
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Guo, Z., Li, X., Yu, T., Fang, X. (2019). Vibration Signal EMD Filter Detection Method for Blast Furnace Opening Machine. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2018 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 528. Springer, Singapore. https://doi.org/10.1007/978-981-13-2288-4_18
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DOI: https://doi.org/10.1007/978-981-13-2288-4_18
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