Abstract
This paper presents a novel method for fault classification and degradation assessment in rotary machinery through wavelet packet decomposition and data-driven regression methods. Wavelet Packet Decomposition is applied to extract the coefficient and energy based features from vibration signals. During the experiment, we used several machine-learning methods, including Artificial Neural Networks, Support Vector Machine, and K-Nearest Neighbor Classification for degradation assessment and compared the numerical results.
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Li, Z., Pedersen, V.G.B., Wang, K., He, Y. (2018). Fault Classification and Degradation Assessment Based on Wavelet Packet Decomposition for Rotary Machinery. In: Wang, K., Wang, Y., Strandhagen, J., Yu, T. (eds) Advanced Manufacturing and Automation VII. IWAMA 2017. Lecture Notes in Electrical Engineering, vol 451. Springer, Singapore. https://doi.org/10.1007/978-981-10-5768-7_54
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DOI: https://doi.org/10.1007/978-981-10-5768-7_54
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