Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation
Intrinsic feature extraction using discriminant diffusion mapping analysis for automated tool wear evaluation
复制标题
使用判别扩散映射分析进行内在特征提取，以进行自动刀具磨损评估
DOI：
10.1631/fitee.1601512
复制
发表时间：
2018-11-01
影响因子：
3
通讯作者：
Li, Yan-ming
中科院分区：
文献类型：
--
作者：
Huang, Yi-xiang;Liu, Xiao;Li, Yan-ming
提出了一种判别扩散图分析（DDMA）方法，用于评价铣削过程中的刀具磨损。作为一种降维技术，DDMA方法被用来融合和减少从时域和频域提取的原始特征，通过保持本征特征空间内的扩散距离和耦合的特征的判别核，以细化从高维特征空间的信息。DDMA方法主要包括三个步骤：（1）信号处理和特征提取;（2）本征维数估计;（3）通过保持扩散距离的特征空间映射实现特征融合。DDMA已被应用到测量的电流信号从主轴在加工中心在铣削实验中，以评估刀具磨损状态。与目前流行的主成分分析方法相比，DDMA能更好地保留与刀具磨损状态相关的有用的内在信息。因此，在这项研究中突出了两个重要的方面：显着较低的尺寸的固有功能，是敏感的刀具磨损的好处，和方便的可用性，在大多数工业加工中心的电流信号。
We present a method of discriminant diffusion maps analysis (DDMA) for evaluating tool wear during milling processes. As a dimensionality reduction technique, the DDMA method is used to fuse and reduce the original features extracted from both the time and frequency domains, by preserving the diffusion distances within the intrinsic feature space and coupling the features to a discriminant kernel to refine the information from the high-dimensional feature space. The proposed DDMA method consists of three main steps: (1) signal processing and feature extraction; (2) intrinsic dimensionality estimation; (3) feature fusion implementation through feature space mapping with diffusion distance preservation. DDMA has been applied to current signals measured from the spindle in a machine center during a milling experiment to evaluate the tool wear status. Compared with the popular principle component analysis method, DDMA can better preserve the useful intrinsic information related to tool wear status. Thus, two important aspects are highlighted in this study: the benefits of the significantly lower dimension of the intrinsic features that are sensitive to tool wear, and the convenient availability of current signals in most industrial machine centers.