On the influence of sample length and measurement noise on the stochastic subspace damage detection technique

Saeid Allahdadian (1) , Michael Döhler (2) , Carlos Ventura (1) , Laurent Mevel (2)

1 UBC - University of British Columbia
2 I4S - Statistical Inference for Structural Health Monitoring

In this paper the effects of measuring noise and number of samples is studied on the stochastic subspace damage detection (SSDD) technique. In this technique, i.e., SSDD, the need of evaluating the eigenstructure of the system is circumvented, making this approach capable of dealing with real-time measurements of structures. In previous studies, the effect of these practical parameters was examined on simulated measurements from a model of a real structure. In this study, these effects are formulated for the expected damage index evaluated from a Chi-square distributed value. Several theorems are proposed and proved. These theorems are used to develop a guideline to serve the user of the SSDD method to face these effects.

Mots clés: damage detection; subspace method; health monitoring; signal noise; sampling; stochastic subspace method

Domaines: Applications [stat.AP]; Dynamique, vibrations; Traitement du signal et de l'image [eess.SP]

Saeid Allahdadian, Michael Döhler, Carlos Ventura, Laurent Mevel. On the influence of sample length and measurement noise on the stochastic subspace damage detection technique. IMAC – 34th International Modal Analysis Conference, Jan 2016, Orlando, FL, United States. ⟨10.1007/978-3-319-29956-3_4⟩. ⟨hal-01262256⟩