Blind separation of manufacturing variability with independent component analysis: A convolutive approach

被引：8
|
作者：
Gandini, M. [1 ]
Lombardi, F. [1 ]
Vaccarino, F. [2 ,3 ]
机构：
[1] Politecn Torino, Dept Prod Syst & Business Econ, I-10129 Turin, Italy
[2] Politecn Torino, Dept Math, I-10129 Turin, Italy
[3] ISI Fdn, I-10133 Turin, Italy
来源：
EXPERT SYSTEMS WITH APPLICATIONS | 2011年 / 38卷 / 08期
关键词：
Independent component analysis; Blind source separation; Technological fingerprint; Manufacturing engineering; Geometrical product specification; ALGORITHM;
D O I：
10.1016/j.eswa.2011.02.036
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we apply the blind source separation model to the scope of extracting information from a workpiece about the process that made it. Given any manufactured workpiece, we may think about it as the carrier of the information built in the process that made it. Using recent inspection technologies such as stylus profiler, we are able to generate signals from a workpiece. We analyze these signals using independent component analysis (ICA) in its various formulations. In doing this, we develop a convolutive version of ICA to overcome technical and metrological problems arisen. By using this convolutive modification of ICA we are able to demix the recorded signal and to recover the technological fingerprint over it. Simulations on NIST benchmarks are included, as well as a case study on a turned workpiece. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码：9939 / 9946
页数：8
相关论文
共 50 条
  • [1] A Unifying View on Blind Source Separation of Convolutive Mixtures Based on Independent Component Analysis
    Brendel, Andreas
    Haubner, Thomas
    Kellermann, Walter
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2023, 71 : 816 - 830
  • [2] A BOUNDED COMPONENT ANALYSIS APPROACH FOR THE SEPARATION OF CONVOLUTIVE MIXTURES OF DEPENDENT AND INDEPENDENT SOURCES
    Inan, Huseyin A.
    Erdogan, Alper T.
    2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2013, : 3223 - 3227
  • [3] Convolutive transfer function-based independent component analysis for overdetermined blind source separation
    Wang, Taihui
    Yang, Feiran
    Li, Nan
    Zhang, Chen
    Yang, Jun
    2022 16TH IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP2022), VOL 1, 2022, : 22 - 26
  • [4] Independent vector analysis for convolutive blind noncircular source separation
    Zhang, Hefa
    Li, Liping
    Li, Wanchun
    SIGNAL PROCESSING, 2012, 92 (09) : 2275 - 2283
  • [5] Time-Domain Blind Signal Separation of Convolutive Mixtures via Multidimensional Independent Component Analysis
    Murakami, Takahiro
    Tanaka, Toshihisa
    Ishida, Yoshihisa
    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2009, E92A (03) : 733 - 744
  • [6] Blind Separation of Convolutive sEMG Mixtures based on Independent Vector Analysis
    Wang, Xiaomei
    Guo, Yina
    Tian, Wenyan
    EIGHTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2015), 2015, 9875
  • [7] Convolutive Bounded Component Analysis Algorithms for Independent and Dependent Source Separation
    Inan, Huseyin A.
    Erdogan, Alper T.
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 26 (04) : 697 - 708
  • [8] On blind separation of convolutive mixtures of independent linear signals
    Tugnait, JK
    8TH IEEE SIGNAL PROCESSING WORKSHOP ON STATISTICAL SIGNAL AND ARRAY PROCESSING, PROCEEDINGS, 1996, : 312 - 315
  • [9] Blind source separation and independent component analysis
    Puntonet, Carlos G.
    Lang, Elmar W.
    NEUROCOMPUTING, 2006, 69 (13-15) : 1413 - 1413
  • [10] Independent Component Analysis and Blind Source Separation
    Barros, Allan Kardec
    Principe, Jose Carlos
    Erdogmus, Deniz
    SIGNAL PROCESSING, 2007, 87 (08) : 1817 - 1818
12345