Abstract
In this paper, a new intelligent method for the tool wear condition monitoring based on sparse components analysis (SCA) for blind sources separation and Continuous Wavelet Transform (CWT) have been applied. The CWT used to decompose the raw signals into coefficients; the independent sources obtained from wavelet coefficients estimated by SCA. The nodes energy computing from independent sources used for estimating the health assessment and remaining useful life of cutting tools. The PCA applied for the dimensionality reduction of the nodes energy data where the goodness of fit is measured; the idea is based on the computation of a nonlinear regression function in a high-dimensional feature space where the input data mapped via a nonlinear function. The results of its application in CNC machining show that this indicator can reflect effectively the performance degradation of cutting tools for milling process. The proposed method is applied on real world RUL estimation and health assessment for a given.
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Rabah, B., Benkedjouh, T., Said, R. (2019). Tool Wear Condition Monitoring Based on Blind Source Separation and Wavelet Transform. In: Chadli, M., Bououden, S., Ziani, S., Zelinka, I. (eds) Advanced Control Engineering Methods in Electrical Engineering Systems. ICEECA 2017. Lecture Notes in Electrical Engineering, vol 522. Springer, Cham. https://doi.org/10.1007/978-3-319-97816-1_29
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DOI: https://doi.org/10.1007/978-3-319-97816-1_29
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