Improvement of Experimental SEA model accuracy using Independent Component Analysis

Statistical Energy Analysis (SEA) is a suitable tool to predict vibration stationary responses. It is roughly categorized into analytical, FEM based, and experimental SEA due to the process of model construction. SEA is expected to be used for designing machines with optimal vibration transfer paths. However experimental SEA model accuracy especially in low frequency is highly dependent on experimental condition and target structure. It is required to develop model construction process for more convenient use of SEA. In this paper, preprocessing algorithm using independent component analysis (ICA) is proposed for improvement of model construction of SEA. Here, ICA is a signal processing method which is originally developed for bio-signal analysis and it is used for separation of complex mixture of several vibration sources. Feasibility of the proposed method is examined through an experiment with a test structure, composed of three flat steel plates.

[1]  R. Lyon,et al.  Theory and Application of Statistical Energy Analysis , 2014 .

[2]  Gaëtan Kerschen,et al.  Output-only modal analysis using blind source separation techniques , 2007 .

[3]  Qian Du,et al.  Automated Target Detection and Discrimination Using Constrained Kurtosis Maximization , 2008, IEEE Geoscience and Remote Sensing Letters.

[4]  Guillaume Gelle,et al.  BLIND SOURCE SEPARATION: A TOOL FOR ROTATING MACHINE MONITORING BY VIBRATIONS ANALYSIS? , 2001 .

[5]  Darryll J. Pines,et al.  A review of vibration-based techniques for helicopter transmission diagnostics , 2005 .

[6]  M. P. Norton,et al.  Fundamentals of Noise and Vibration Analysis for Engineers , 1990 .

[7]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[8]  Christopher J James,et al.  Independent component analysis for biomedical signals , 2005, Physiological measurement.

[9]  俊一 甘利,et al.  A. Hyvärinen, J. Karhunen and E. Oja, Independent Component Analysis, Jhon Wiley & Sons, 2001年，504ページ． （根本幾・川勝真喜訳：独立成分分析——信号解析の新しい世界，東京電機大学出版局，2005年，532ページ．） , 2010 .

[10]  E. Oja,et al.  Independent Component Analysis , 2001 .

[11]  Toru Yamazaki,et al.  A Structural Design Process for Reducing Structure-Borne Sound on Machinery Using SEA , 2007 .

[12]  Kenji Nagase,et al.  An Estimation Method of Mode Shapes by Independent Component Analysis and Its Application to Fault Diagnosis , 2004 .

[13]  M. Zuo,et al.  Feature separation using ICA for a one-dimensional time series and its application in fault detection , 2005 .

[14]  Jean-Claude Golinval,et al.  Physical interpretation of independent component analysis in structural dynamics , 2007 .

[15]  Michael J. Roan,et al.  A NEW, NON-LINEAR, ADAPTIVE, BLIND SOURCE SEPARATION APPROACH TO GEAR TOOTH FAILURE DETECTION AND ANALYSIS , 2002 .