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

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.

[1]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis , 1997, Neural Computation.

[2]  Erkki Oja,et al.  Performance analysis of the FastICA algorithm and Crame/spl acute/r-rao bounds for linear independent component analysis , 2006, IEEE Transactions on Signal Processing.

[3]  J. P. Royston,et al.  Algorithm AS 181: The W Test for Normality , 1982 .

[4]  Sarwat Zahwi,et al.  FACTORS INFLUENCING UNCERTAINTY EVALUATION FOR SURFACE ROUGHNESS MEASUREMENTS , 2003 .

[5]  Masashi Sugiyama,et al.  Estimating Squared-Loss Mutual Information for Independent Component Analysis , 2009, ICA.

[6]  Kaare Brandt Petersen,et al.  Flexible and efficient implementations of Bayesian independent component analysis , 2007, Neurocomputing.

[7]  H Han Haitjema,et al.  Noise bias removal in profile measurements , 2005 .

[8]  Jie Yang,et al.  A source adaptive independent component analysis algorithm through solving the estimating equation , 2009, Expert Syst. Appl..

[9]  Visa Koivunen,et al.  Blind separation methods based on Pearson system and its extensions , 2002, Signal Process..

[10]  S. Standard.  GUIDE TO THE EXPRESSION OF UNCERTAINTY IN MEASUREMENT , 2006 .

[11]  Sung-Nien Yu,et al.  Selection of significant independent components for ECG beat classification , 2009, Expert Syst. Appl..

[12]  E. Oja,et al.  Independent Component Analysis , 2013 .

[13]  Mohd. Alauddin Mohd. Ali,et al.  Fetal heart rate monitoring based on independent component analysis , 2006, Comput. Biol. Medicine.

[14]  B. Muralikrishnan,et al.  Recent advances in separation of roughness, waviness and form , 2002 .

[15]  Hiroshi Sawada,et al.  Spatio–Temporal FastICA Algorithms for the Blind Separation of Convolutive Mixtures , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[16]  J. Royston.  The W Test for Normality , 1982 .