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Surface roughness monitoring by singular spectrum analysis of vibration signals

机译：通过振动信号奇异谱分析监测表面粗糙度

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This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.
机译：这项研究评估了两种方法，这些方法基于将奇异频谱分析（SSA）应用到CNC精加工中工件与刀具相互作用中产生的振动信号的基础上，即奇异频谱分析（SSA）的应用，即主要成分的单独分析（I- SSA），以及相关主成分的分组分析（G-SSA）。奇异频谱分析是时间序列分析的一种非参数技术，它将信号分解为一组独立的附加时间序列，称为主成分。使用这两种方法，在不同切削条件下进行了许多实验，以评估表面粗糙度的监测。结果表明，振动信号处理的奇异频谱分析可区分有效预测表面粗糙度的频率范围。相关主成分的分组分析（G-SSA）被证明是监测表面粗糙度的最有效方法，以较低的分析计算成本获得了最佳的预测和可靠性结果。最后，结果表明，奇异光谱分析是一种理想的方法分析应用于表面粗糙度在线监测的振动信号。

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