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Abstract

To address the problems of singularities, sensitivity to measurement noise, and low efficiency in traditional principal component analysis (PCA)-based operational modal analysis (OMA), we present a Sanger neural network principal component analysis (SNNPCA) algorithm to identify the operational modal parameters. SNNPCA is a two-layer neural network that is trained using a generalized Hebbian algorithm to ensure that its output converges to the principal components. After SNNPCA has converged, the link weights of SNNPCA correspond to the separation matrix of PCA. In SNNPCA-based OMA, the measurement response points are set as the input neurons, modal coordinate response signals are set as the output neurons, and the link weights of the neural network represent the modal shapes. Therefore, the operational modal identification process in SNNPCA is physically meaningful and convergent. Furthermore, SNNPCA inherits the parallel nature of neural network algorithms, so it is also insensitive to measurement noise. Simulation results show that SNNPCA can identify the principal modal parameters accurately using only measurement response signals. This method can be applied in embedded devices to realize online monitoring and real-time fault diagnosis.

Keywords

Operational modal analysis principal component analysis Sanger neural network generalized Hebbian rule parallel measurement noise

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References

Goursat

Döhler

Mevel

and Andersen

, Crystal clear SSI for operational modal analysis of aerospace vehicles, in: Structural Dynamics, Springer, 2011, pp. 1421–1430.

Wang

and Xiao

J.F.

, Modal analysis for the framework of beam pumping unit based on solidworks simulation, in: Applied Mechanics and Materials, Vol. 318, Trans Tech Publications, 2013, pp. 39–43.

Bayraktar

Sevim

Altunişik

and Turker

, Analytical and operational modal analyses of Turkish style reinforced concrete minarets for structural identification, Experimental Techniques 33 (2009), 65–75.

Rebelo

Julio

Varum

and Costa

, Cable tensioning control and modal identification of a circular cable-stayed footbridge, Experimental Techniques 34 (2009), 62–68.

Uhl

Lisowski

and Kurowski

, In-Operation Modal Analysis and its Application, Publisher Department of Robotics and Machine Dynamics UMM, 2001.

Wang

Gou

Bai

and Yan

, Modal parameter identification with principal component analysis, Journal of Xi’an JiaoTong University 47(11) (2013), 97–103.

Wang

Guan

Gou

Hou

Bai

and Yan

, Principal component analysis based three-dimensional operational modal analysis, International Journal of Applied Electromagnetics and Mechanics 45(1–4) (2014), 137–144.

Han

and Feeny

, Application of proper orthogonal decomposition to structural vibration analysis, Mechanical Structures and Signal Processing 17(5) (2003), 989–1001.

Wang

B.T.

and Cheng

D.K.

, Modal analysis of mdof structure by using free vibration response data only, Journal of Sound and Vibration 311(3) (2008), 737–755.

10.

Poncelet

Kerschen

Golinval

J.C.

and Verhelst

, Output-only modal analysis using blind source separation techniques, Mechanical Systems and Signal Processing 21(6) (2007), 2335–2358.

11.

Zhou

and Chelidze

, Blind source separation based on vibration mode identification, Mechanical Systems and Signal Processing 21(8) (2007), 3072–3087.

12.

Kerschen

Poncelet

and Golinval

J.-C.

, Physical interpretation of independent component analysis in structural dynamics, Mechanical Systems and Signal Processing 21 (2007), 1561–1575.

13.

Bai

Yan

and Wang

, Modal identification method following locally linear embedding, Journal of Xi’an Jiaotong University 47(1) (2013), 85–89.

14.

Reynders

and De Roeck

, Reference-based combined deterministic–stochastic subspace identification for experimental and operational modal analysis, Mechanical Systems and Signal Processing 22(3) (2008), 617–637.

15.

Pearson LIII

, On lines and planes of closest fit to systems of points in space, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 2(11) (1901), 559–572.

16.

Wold

, Nonlinear Estimation by Iterative Least Squares Procedures, in: Festschrift for J. Neyman: Research Papers in Statistics David

F.N. (Hrsg)

(ed.), London, 1966.

17.

Chung

I.H.

Sainath

T.N.

Ramabhadran

Picheny

Gunnels

Austel

Chauhari

and Kingsbury

, Parallel deep neural network training for big data on blue gene/q, IEEE Transactions on Parallel and Distributed Systems 28(6) (2017), 1703–1714.

18.

Wang

and Song

, Neural network online training platform based on embedded system, Transducer and Microsystem Technologies 29(8) (2010), 100–103.

19.

Hebb

D.O.

, The organization of behavior: A Neuropsychological Approach, American Journal of Physical Medicine and Rehabilitation 30(1) (1949), 74–76.

20.

Oja

, Simplified neuron model as a principal component analyzer, Journal of Mathematical Biology 15(3) (1982), 267–273.

21.

Sanger

T.D.

, Optimal unsupervised learning, Neural Networks 1(Supplement-1) (1988), 127.

22.

Sanger

T.D.

, Optimal unsupervised learning in a single-layer linear feedforward neural network, Neural Networks 2(6) (1989), 459–473.

23.

Zhe

Yanda

and Falong

, A neural network learning algorithm for PCA and MCA, Acta ELectronica Sinica 24(4) (1996), 12–16.

24.

Diamantaras

K.I.

and Kung

S.Y.

, Principal Component Neural Networks: Theory and Application, Wiley, New York, 1996.

25.

Kung

S.Y.

and Diamantaras

K.I.

, A neural network learning algorithm for adaptive principal component extraction (APEX), in: International Conference on Acoustics, Speech, and Signal Processing (ICASSP-90), 1990, pp. 861–864.

26.

Sese

Palmer

A.L.

and Montano

J.J.

, Psychometric measurement models and artificial neural networks, International Journal of Testing 4(3) (2004), 253–266.

27.

Mishra

A.K.

and Motaung

, Application of linear and nonlinear PCA to SAR ATR, in: 2015 25th International Conference Radioelektronika (RADIOELEKTRONIKA 2015), pp. 349–354.

28.

Karhunen

and Oja

, New Methods for Stochastic Approximation of Truncated Karhunen-Loeve Expansions, Teknillinen korkeakoulu, 1981.

29.

Zhou

and Chelidze

, Blind source separation based vibration mode identification, Mechanical Systems and Signal Processing 21(8) (2007), 3072–3087.

Parallel principal components algorithm for OMA following Sanger neural network

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