The goal of this study is the introduction and experimental assessment of a sequential probability ratio test framework for vibration-based structural health monitoring. This framework is based on a combination of binary and multihypothesis versions of the statistically optimal sequential probability ratio test and employs the residual sequences obtained through a single stochastic time series model of the healthy structure. The full list of properties and capabilities of the sequential probability ratio test is for the first time presented and explored in the context of vibration-based damage diagnosis. The approach postulated in this framework is shown to achieve early and robust damage detection, identification (classification), and quantification based on predetermined sampling plans, which are both analytically and experimentally compared and assessed. The framework’s performance is determined a priori via the use of the analytical expressions of the operating characteristic and average sample number functions in combination with baseline data records. It is shown to require, on average, a minimal number of signal samples in order to reach a decision compared to fixed sample size most powerful tests. The effectiveness of the proposed approach is validated and experimentally assessed via its application to a lightweight aluminum truss structure.
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