Vibration Based Health Monitoring for a Thin Aluminum Plate: Experimental Assessment of Several Statistical Time Series Methods Fotis P. Kopsaftopoulos and Spilios D. Fassois Stochastic Mechanical Systems & Automation (SMSA) Laboratory Department of Mechanical & Aeronautical Engineering University of Patras, GR 265 00 Patras, Greece E-mail: {fkopsaf,fassois}@mech.upatras.gr Internet: http://www.smsa.upatras.gr ABSTRACT This work aims at the experimental assessment of a number of statistical time series methods for Structural Health Monitoring (SHM). The main features and oper- ation of the employed non–parametric and parametric methods are briefly reviewed. Their performance is subsequently assessed via laboratory experiments pertaining to damage detection and identification on a thin aluminum plate structure. The results of the study demonstrate the potential and effectiveness of the statistical time series SHM methods. INTRODUCTION Vibration based statistical time series methods for Structural Health Monitoring utilize random excitation and/or vibration response signals, along with statistical model building and decision making tools, for inferring the health state of a struc- ture [1–5]. They offer a number of important advantages, including no requirement for physics–based or finite element type models, no requirement for complete modal models, the treatment of uncertainties, and statistical decision making with specified performance characteristics. In spite of these, the literature on vibration–based time series methods for condition monitoring remains relatively sparse, and, in particular, no application studies that assess and experimentally compare the various methods are available. 5th European Workshop on Structural Health Monitoring (SHM 2010) Sorrento, Italy, 29 June – 2 July 2010 The goal of the present study is to present the application of a number of statis- tical time series methods to damage detection and identification in a thin aluminum Fotis P. Kopsaftopoulos and Spilios D. Fassois, Stochastic Mechanical Systems & Automation (SMSA) Laboratory, Department of Mechanical & Aeronautical Engineering, University of Patras, GR 265 00 Patras, Greece.