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Research Papers

An Automated Approach to Enhance Multiscale Signal Monitoring of Manufacturing Processes

[+] Author and Article Information
Marco Grasso

Dipartimento di Meccanica,
Politecnico di Milano,
Via La Masa 1,
Milan 20156, Italy
e-mail: marcoluigi.grasso@polimi.it

Bianca Maria Colosimo

Dipartimento di Meccanica,
Politecnico di Milano,
Via La Masa 1,
Milan 20156, Italy
e-mail: biancamaria.colosimo@polimi.it

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received May 20, 2015; final manuscript received October 5, 2015; published online November 16, 2015. Assoc. Editor: Robert Gao.

J. Manuf. Sci. Eng 138(5), 051003 (Nov 16, 2015) (16 pages) Paper No: MANU-15-1245; doi: 10.1115/1.4031797 History: Received May 20, 2015; Revised October 05, 2015

Multiscale signal decomposition represents an important step to enhance process monitoring results in many manufacturing applications. Empirical mode decomposition (EMD) is a data driven technique that gained an increasing interest in this framework. However, it usually yields an-over decomposition of the signal, leading to the generation of spurious and meaningless modes and the possible mixing of embedded modes. This study proposes an enhanced signal decomposition approach that synthetizes the original information content into a minimal number of relevant modes via a data-driven and automated procedure. A criterion based on the kernel estimation of density functions is proposed to estimate the dissimilarities between the intrinsic modes generated by the EMD, together with a methodology to automatically determine the optimal number of final modes. The performances of the method are demonstrated by means of simulated signals and real industrial data from a waterjet cutting application.

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Figures

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Fig. 1

Conceptual scheme of the proposed approach

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Fig. 2

Sine wave of amplitude X=1 (left panel) and corresponding probability density function (right panel)

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Fig. 3

Transient sine wave of amplitude X=1 (left panel) and corresponding probability density function (right panel)

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Fig. 4

One realization of multiscale random profiles in example A

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Fig. 5

Example of EMD of one profile in example A (left panel) and corresponding probability density functions (right panel)

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Fig. 6

Plot of the distance statistics Di,i+1* (left panel) and plot of statistics SSWf(K) and K*SSBf(K) for different choices of K (right panel)—one profile realization in example A

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Fig. 7

Final CMF decomposition provided by the proposed approach for one profile realization in example A

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Fig. 8

One realization of multiscale random profiles in example B

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Fig. 9

Example of EMD of one profile in example B (left panel) and corresponding probability density functions (right panel)

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Fig. 10

Plot of the distance statistics Di,i+1* (left panel) and plot of statistics SSWf(K) and K*SSBf(K) for different choices of K (right panel)—one profile realization in example B

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Fig. 11

Final CMF decomposition provided by the proposed approach for one profile realization in example B

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Fig. 12

Final CMF decomposition provided by the proposed approach for a variant of example A where a Gaussian baseline was added to the source signal

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Fig. 13

Examples of CMF decompositions achieved by using the kurtosis-based method (left panel) and the multivariate index-based method (right panel) for IMF dissimilarity estimation—example A

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Fig. 14

Installation of the pressure transducer in the UHP pump (left panel) and scheme of the waterjet plant (right panel)

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Fig. 15

Example of one pressure signal profile corresponding to a complete pumping cycle (top panel) and details of transients corresponding to the end of active strokes of plunger 1 and plunger 2 (bottom panels)

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Fig. 16

EMD of one water pressure profile (left panel) and corresponding density functions (right panel)

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Fig. 17

Plot of the distance statistics Di,i+1* (left panel) and plot of statistics SSWf(K) and K*SSBf(K) for different choices of K (right panel)—one pressure profile in the waterjet case study

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Fig. 18

CMF decomposition provided by the proposed approach for one pressure profile in the waterjet case study

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Fig. 19

Example of pressure signal wavelet decomposition—Daubechies mother wavelet of fourth-order, nine decomposition levels

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Fig. 20

Examples of CMF decompositions achieved by using the kurtosis-based method (left panel) and multivariate index-based method (right panel) for one pressure profile in the waterjet case study

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