支持向量空间方法在刀具运行可靠性评估中的应用

Cutting Tools Operation Reliability Assessment Based on Support Vector Space

针对单台或小样本数控机床刀具可靠性评估时,传统的基于大样本统计的可靠性评估方法因缺乏时间、动态、个性化的精确性描述而难以发挥作用。为了提高单台或小样本条件下的机床刀具运行可靠性评估精度和可信性,作者提出了一种基于奇异值分解(SVD)和支持向量空间的运行可靠性评估方法。首先通过实验对机床切削加工过程中的刀架振动信号进行获取,采用小波包分解、能量分布和时频域统计量分析,提取出与刀具磨损量密切相关的显著特征指标。为了降低计算复杂程度和减小冗余成分,进一步利用SVD对所提取的刀具正常磨损条件的振动信号时频域高维特征数据集进行降维处理。然后将降维数据作为测试样本代入支持向量空间模型构造支持向量空间超球体,以该超球体所定义的圆心和半径为计算依据,将待检样本相对于超球体的相对距离作为描述刀具的性能退化指标,并引入降半型隶属度函数,将相对距离指标映射到[0,1]区间,将其定义为刀具的运行可靠性。分别以两把磨损状态为失效与正常的刀具作为评估案例,通过对其降维前后数据进行支持向量超球体空间构造以及可靠性评估,结果表明维数据约简可有效减小数据分散性过大所导致的超球体形状变形问题。最后以多把刀具统一失效阈值下的通用数据对刀具1进行可靠性评估,结果表明在训练数据条件足够大的情况下,刀具的相对距离和运行可靠度趋势性更加明显,波动性减少,特别是在磨损后期,这种变化有利于刀具服役性能的精准评估。所提出的运行可靠性评估方法摆脱了传统可靠性评估对大样本统计数据的依赖,为丰富和发展小样本数据的可靠性评估提供了新的理论支持和技术手段。

For a single or a small batch CNC turning cutting tool reliability estimation,the traditional reliability estimation methods large sample statistics-based is inefficient.Which are limited for some reasons,such as the difficulty for time-dynamic process description,inaccurate model and non-individual characteristics.In order to improve the precision and credibility of reliability assessment to the cutting tools under the condition of a single or a small batch sample,a new operation reliability estimation method based on singular value decomposition(SVD)transform and support vector space is proposed.Firstly,the vibration signals of the tool holder are acquired during the cutting process.The salient features closely related to the tool wear are extracted by wavelet packet decomposition,energy distribution and time-frequency statistic analysis.The SVD method is employed for the dimensionality reduction of high dimension feature data so as to reduce the computational complexity and the redundant component.Secondly,dimension reduced data are substituted into support vector space model to establish a hyper sphere.Then the relative distance between the sample points and the hyper sphere is calculated and used to describe the degradation of the tool.The semi normal function is further introduced to reflect the mapping relationship of the relative distance and the operation reliability of the tool.Two tools whose wear states are failure and normal are used as evaluation cases respectively.The data before and after dimension reduction are used to established the support vector hyper sphere space for reliability evaluation.The results show that dimensional data reduction can effectively reduce the deformation of the hyper sphere coming from the data dispersion.Finally,the unified data of multiple tools under the uniform failure threshold are used for reliability evaluation of the 1st tool.The results show that the changing trend of the tool's relative distance and running reliability are more obvious and whose volatility is reduced when the training data conditions are sufficient.Especially,at the end of tool wear,it is conducive to the accurate evaluation of the tool service performance.The proposed operational reliability assessment method is free from the dependence of the traditional reliability assessment method on large-sample statistical data,which enriches and develops the reliability evaluation theory.