Existing methods for structural health monitoring are limited due to their sensitivity to changes in environmental and operational conditions, which can obscure the indications of damage by introducing nonlinearities and other types of noise into the structural response. In this article, we introduce a novel approach using state-space probability models to infer the conditions underlying each time step, allowing the definition of a damage metric robust to environmental and operational variation. We define algorithms for training and prediction, describe how the algorithm can be applied in both the presence and absence of measurements for external conditions, and demonstrate the method’s performance on data acquired from a laboratory structure that simulates the effects of damage and environmental and operational variation on bridges.
WordenK.Structural fault detection using a novelty measure. J Sound Vib1997; 201(1): 85–101.
2.
DoeblingSFarrarCPrimeMet al. A review of damage identification methods that examine changes in dynamic properties. Shock Vib Digest1998; 30: 91–105.
3.
CardenEPFanningP.Vibration based condition monitoring: a review. Struct Health Monit2004; 3(4): 355–377.
4.
FanWQiaoP.Vibration-based damage identification methods: a review and comparative study. Struct Health Monit2011; 10(1): 83–111.
5.
PanditSMWuS.Time series and system analysis with applications. New York: Wiley, 1983.
6.
FugateMLSohnHFarrarCR.Vibration-based damage detection using statistical process control. Mech Syst Signal Pr2001; 15: 707–721.
7.
MosaviADickeyDSeracinoRet al. Identifying damage locations under ambient vibrations utilizing vector autoregressive models and Mahalanobis distances. Mech Syst Signal Pr2012; 26: 254–267.
8.
WeijtjensWVerbelenTDe SitterGet al. Data normalization for foundation SHM of an offshore wind turbine: a real-life case study. In: Proceedings of the 7th European workshop on structural health monitoring, Nantes, 8–11 July 2014, pp. 788–795.
9.
FarrarCRCornwellPJDoeblingSWet al. Structural health monitoring studies of the Alamosa Canyon and I-40 bridges. Technical report LA-13635-MS, July2000. Los Alamos, NM: Los Alamos National Laboratory.
10.
AlampalliS. Influence of in-service environment on modal parameters. In: Proceedings of the 16th international modal analysis conference, Santa Barbara, CA, 2–5 February 1998, p. 111.
11.
FigueiredoEParkGFarrarCRet al. Machine learning algorithms for damage detection under operational and environmental variability. Struct Health Monit2011; 10: 559–572.
12.
CrossEJMansonGWordenKet al. Features for damage detection with insensitivity to environmental and operational variations. P Roy Soc A: Math Phy2012; 468: 4098–4122.
13.
FarrarCRSohnHWordenK.Data normalization: a key for structural health monitoring. Technical report LA-UR-01-4212, 1January2001. Los Alamos, NM: Los Alamos National Laboratory.
14.
WordenKCrossEBrownjohnJ.Switching response surface models for structural health monitoring of bridges. In: KozielSLeifssonL (eds) Surrogate-based modeling and optimization. New York: Springer Science + Business Media, 2013, pp. 337–358.
15.
Avendaño-ValenciaLDFassoisSD.Damage/fault diagnosis in an operating wind turbine under uncertainty via a vibration response Gaussian mixture random coefficient model based framework. Mech Syst Signal Pr2017; 91: 326–353.
16.
CryerJDChanK.Time series analysis: with applications in R. 2nd ed.New York: Springer, 2008.
17.
AkaikeH.A new look at the statistical model identification. IEEE T Automat Contr1974; 19(6): 716–723.
18.
SchwarzG.Estimating the dimension of a model. Ann Stat1978; 6(2): 461–464.
19.
FigueiredoEFigueirasJParkGet al. Influence of the autoregressive model order on damage detection. Comput-Aided Civ Inf2011; 26: 225–238.
20.
MontgomeryD.Introduction to statistical quality control. New York: John Wiley & Sons, Inc., 2012.
21.
BodeuxJGolinvalJ.Modal identification and damage detection using the data-driven stochastic subspace and ARMAV methods. Mech Syst Signal Pr2003; 17(1): 83–89.
22.
De StefanoASabiaDSabiaL.Structural identification using VARMA models from noisy dynamic response under unknown random excitation, structural damage assessment using advanced signal processing procedures. In: Proceedings of DAMAS 97, Sheffield, 30 June–2 July 1997, pp. 419–429.
23.
BornnLFarrarCRHigdonDet al. Modeling and diagnosis of structural systems through sparse dynamic graphical models. Mech Syst Signal Pr2016; 74: 133–143.
24.
DzunicZChenJGMobahiHet al. A Bayesian state-space approach for damage detection and classification. Mech Syst Signal Pr2017; 96: 239–259.
25.
BornnLFarrarCRParkGet al. Structural health monitoring with autoregressive support vector machines. J Vib Acoust2009; 131: 021004.
26.
MurphyK. Switching Kalman filters. Technical report, 21 August 1998. The University of British Columbia, Vancouver, BC, Canada, 1998.
27.
QuinnJAWilliamsCKMcIntoshN.Factorial switching linear dynamical systems applied to physiological condition monitoring. IEEE T Pattern Anal2009; 31: 1537–1551.
28.
BornnLFarrarCRParkG.Damage detection in initially nonlinear systems. Int J Eng Sci2010; 48: 909–920.
29.
FigueiredoEFlynnE.Three-story building structure to detect nonlinear effects. Technical report, Los Alamos National Laboratory, Los Alamos, NM, 2009.
30.
FarrarCRBakerWEBellTMet al. Dynamic characterization and damage detection in the I-40 bridge over the Rio Grande. Technical report LA-12767-MS, 1June1994. Los Alamos, NM: Los Alamos National Laboratory.
