Strain modal-based damage identification method and its application to crane girder without original model

Abstract

Although there are many methods to identify structural damage of the crane girder, almost all of them are used by means of comparing specific parameters of original and existing structures. And it leads to difficulties in practice. So, a method to identify damage of the crane girder is proposed based on strain modal without original model. First, the strain modal difference curve was obtained by using difference algorithm to deal with strain modal and cubic spline interpolation algorithm to fit the curve, and the damage location was realized by constructing and comparing damage sensitive index (IDSI). Then, the data of undamaged checkpoints adjacent to damaged checkpoints are stationary and modeled by Auto-Regressive Moving-Average Model, with this modeling the original data of the damaged checkpoints was predicted, and variation of the got strain modal difference curve was used to determine damage degree. Finally, the application of this method was implemented by some experiments and numerical calculations, and the results demonstrated that this method can effectively identify the damage of the crane girder.

Keywords

Strain modal difference curve damage sensitive index ARMA modeling damage degree crane girder

Get full access to this article

View all access options for this article.

References

Deng

. Structure mode analysis and damage diagnosis, Nanjing, China: Southeast University Press, 2008.

Pedram

Esfandiari

Khedmati

. Damage detection by a FE model updating method using power spectral density: numerical and experimental investigation. J Sound Vibrat 2017; 397: 51–76.

Thomson

Dahleh

. Theory of vibration with application, 5th ed. New Jersey: Prentice Hall, 1997.

Liu

Shao

Zhu

. Free vibration analysis of a cantilever beam with a slant edge crack. Proc IMechE, Part C: J Mechanical Engineering Science 2016; 203-210: 1989–1996.

Rahmatalla

Hee-Chang

ETL

. Damage detection by the distribution of predicted constraint forces. J Mech Sci Technol 2012; 26: 1079–1087.

Prada

Toivola

et al.

Three-way analysis of structural health monitoring data. Neurocomputing 2012; 80: 119–128.

Dessi

Camerlengo

. Damage identification techniques via modal curvature analysis: overview and comparison. Mech Syst Signal Process 2015; 52-53: 181–205.

Dilena

Limongelli

Morassi

. Damage localization in bridges via the FRF interpolation method. Mech Syst Signal Process 2015; 52–53: 162–180.

Moradipour

Chan

THT

Gallag

. An improved modal strain energy method for structural damage detection, 2D simulation. Struct Eng Mech 2015; 54: 105–119.

10.

Ettefagh

Akbari

Asadi

et al.

New structural damage-identification method using modal updating and model reduction. Proc IMechE, Part C: J Mechanical Engineering Science 2015; 229: 203–210.

11.

Cao

Radzieński

et al.

Identification of multiple damage in beams based on robust curvature mode shapes. Mech Syst Signal Process 2014; 46: 468–480.

12.

Dong

Ding

Gao

. A method for recognition and location of structural damage. Mech Struct Machine 2002; 30: 193–201.

13.

. Experimental modal analysis and its application, Beijing, China: China Science Press, 2001.

14.

Peiying

Houqun

Tongchun

et al.

Direct index method of damage location detection based on difference theory of strain modal shape. J Vibrat Shock 2006; 25: 13–17.

15.

Bao

Zhao

Lin

et al.

Direct index method of beam damage location detection based on difference theory of strain modal shapes and the genetic algorithms application. Math Probl Eng 2012; 2012: 60–66.

16.

Kawahara

Shimizu

et al.

Analyzing relationships among ARMA processes based on non-Gaussianity of external influences. Neurocomputing 2011; 74: 2212–2221.

17.

Oliver

Lautour

Omenzetter

. Damage classification and estimation in experimental structures using time series analysis and pattern recognition. Mech Syst Signal Process 2010; 24: 1556–1569.

18.

Gul

Catbas

. Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering. J Sound Vibrat 2011; 330: 1196–1210.

19.

Chaoneng

. A concise course for numerical analysis, 2nd ed. Beijing, China: Higher Education Press, 2003.

20.

Valenzuela

et al.

Soft-computing techniques and ARMA model for time series prediction. Neurocomputing 2008; 71: 519–537.

21.

Yang

WY X

. Time series analysis in engineering application, 2nd ed. Wuhan, China: Huazhong University of Science and Technology Press, 2007.

22.

Gu PY. Direct index method of damage detection based on strain mode shape of damage structure. PhD Thesis, Hohai University, 2006.